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reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/filter_children_classes/vande_waterbeemd_filter.py | """VandeWaterbeemd Filter
This runs a VandeWaterbeemd filter for drugs which are likely to be blood
brain barrier permeable. VandeWaterbeemd filter filters molecules for
Molecular weight (MW), and Polar Sureface Area (PSA).
To pass the VandeWaterbeemd filter a ligand must have:
Molecular Weight: less than 450 dalton
Polar Sureface Area: less than 90 A^2
If you use the Van de Waterbeemd Filter please cite: Van de Waterbeemd, Han:
et al Estimation of Dlood-Brain Barrier Crossing of Drugs Using Molecular Size
and Shape, and H-Bonding Descriptors. Journal of Drug Targeting (1998), 6(2),
151-165.
"""
import __future__
import rdkit
import rdkit.Chem as Chem
import rdkit.Chem.MolSurf as MolSurf
import rdkit.Chem.Descriptors as Descriptors
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
from autogrow.operators.filter.filter_classes.parent_filter_class import ParentFilter
class VandeWaterbeemdFilter(ParentFilter):
"""
This runs a VandeWaterbeemd filter for drugs which are likely to be blood
brain barrier permeable. VandeWaterbeemd filter filters molecules for
Molecular weight (MW), and Polar Sureface Area (PSA).
To pass the VandeWaterbeemd filter a ligand must have:
Molecular Weight: less than 450 dalton
Polar Sureface Area: less than 90 A^2
If you use the Van de Waterbeemd Filter please cite: Van de Waterbeemd,
Han: et al Estimation of Dlood-Brain Barrier Crossing of Drugs Using
Molecular Size and Shape, and H-Bonding Descriptors. Journal of Drug
Targeting (1998), 6(2), 151-165.
Inputs:
:param class ParentFilter: a parent class to initialize off
Returns:
:returns: bool bool: True if the mol passes the filter; False if it fails the filter
"""
def run_filter(self, mol):
"""
This runs a VandeWaterbeemd filter for drugs which are likely to be
blood brain barrier permeable. VandeWaterbeemd filter filters
molecules for Molecular weight (MW), and Polar Sureface Area (PSA).
To pass the VandeWaterbeemd filter a ligand must have:
Molecular Weight: less than 450 dalton
Polar Sureface Area: less than 90 A^2
Inputs:
:param rdkit.Chem.rdchem.Mol object mol: An rdkit mol object to be
tested if it passes the filters
Returns:
:returns: bool bool: True if the mol passes the filter; False if it
fails the filter
"""
exact_mwt = Descriptors.ExactMolWt(mol)
if exact_mwt >= 450:
return False
psa = MolSurf.TPSA(mol)
if psa >= 90:
return False
# passes everything
return True
| 2,715 | 34.272727 | 88 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/filter_children_classes/ghose_modified_filter.py | """Ghose Filter
This runs a Ghose filter for drug-likeliness. Ghose filter filters molecules
by Molecular weight (MW), the number of atoms, and the logP value.
The upper bound of MW is relaxed from 480Da to 500Da. This is
less restrictive and works in conjunction with Lipinski. This is also
to retro-match AutoGrow 3.1.3 which set Lipinski's upper limit to 500Da.
We protonate the mol in this filter because hydrogens affect
atom count. Our Ghose implementation counts hydrogens in against
the total number of atoms.
To pass the filter a molecule must be:
MW between 160 and 500 dalton
Number of Atoms: between 20 and 70
logP between -0,4 and +5,6
If you use the Ghose Filter please cite: A.K. Ghose et al. A knowledge-based
approach in designing combinatorial or medicinal chemistry libraries for drug
discovery. 1. A qualitative and quantitative characterization of known drug
databases Journal of Combinatorial Chemistry, 1 (1999), pp. 55-68
"""
import __future__
import copy
import rdkit
import rdkit.Chem as Chem
import rdkit.Chem.Lipinski as Lipinski
import rdkit.Chem.Crippen as Crippen
import rdkit.Chem.Descriptors as Descriptors
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
from autogrow.operators.filter.filter_classes.parent_filter_class import ParentFilter
class GhoseModifiedFilter(ParentFilter):
"""
This runs a Ghose filter for drug-likeliness. Ghose filter filters
molecules by Molecular weight (MW), the number of atoms, and the logP
value. The upper bound of MW is relaxed from 480Da to 500Da. This is
less restrictive and works in conjunction with Lipinski. This is also
to retro-match AutoGrow 3.1.3 which set Lipinski's upper limit to 500Da.
We protonate the mol in this filter because hydrogens affect
atom count. Our Ghose implementation counts hydrogens in against
the total number of atoms.
To pass the filter a molecule must be:
MW between 160 and 500 dalton
Number of Atoms: between 20 and 70
logP between -0,4 and +5,6
If you use the Ghose Filter please cite: A.K. Ghose et al. A
knowledge-based approach in designing combinatorial or medicinal chemistry
libraries for drug discovery. 1. A qualitative and quantitative
characterization of known drug databases Journal of Combinatorial
Chemistry, 1 (1999), pp. 55-68
Inputs:
:param class ParentFilter: a parent class to initialize off
"""
def run_filter(self, mol):
"""
This runs a Ghose filter for drug-likeliness. Ghose filter filters
molecules by Molecular weight (MW), the number of atoms, and the logP
value.
We protonate the mol in this filter because hydrogens affect
atom count. Our Ghose implementation counts hydrogens in against
the total number of atoms.
To pass the filter a molecule must be:
MW between 160 and 500 dalton
Number of Atoms: between 20 and 70
logP between -0,4 and +5,6
Inputs:
:param rdkit.Chem.rdchem.Mol object mol: An rdkit mol object to be
tested if it passes the filters
Returns:
:returns: bool bool: True if the mol passes the filter; False if it
fails the filter
"""
copy_mol = copy.deepcopy(mol)
copy_mol = Chem.AddHs(copy_mol)
exact_mwt = Descriptors.ExactMolWt(copy_mol)
if ((exact_mwt < 160) or (exact_mwt > 500)):
return False
num_atoms = copy_mol.GetNumAtoms()
if ((num_atoms < 20) or (num_atoms > 70)):
return False
# molar Refractivity
MolMR = Crippen.MolMR(copy_mol)
if ((MolMR < 40) or (MolMR > 130)):
return False
# molar LogP
mol_log_p = Crippen.MolLogP(copy_mol)
if ((mol_log_p < -0.4) or (mol_log_p > 5.6)):
return False
# passed all filters
return True
| 3,978 | 34.846847 | 85 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/filter_children_classes/ghose_filter.py | """Ghose Filter
This runs a Ghose filter for drug-likeliness. Ghose filter filters molecules
by Molecular weight (MW), the number of atoms, and the logP value.
We protonate the mol in this filter because hydrogens affect
atom count. Our Ghose implementation counts hydrogens in against
the total number of atoms.
To pass the filter a molecule must be:
MW between 160 and 480 dalton
Number of Atoms: between 20 and 70
logP between -0,4 and +5,6
If you use the Ghose Filter please cite: A.K. Ghose et al. A knowledge-based
approach in designing combinatorial or medicinal chemistry libraries for drug
discovery. 1. A qualitative and quantitative characterization of known drug
databases Journal of Combinatorial Chemistry, 1 (1999), pp. 55-68
"""
import __future__
import copy
import rdkit
import rdkit.Chem as Chem
import rdkit.Chem.Lipinski as Lipinski
import rdkit.Chem.Crippen as Crippen
import rdkit.Chem.Descriptors as Descriptors
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
from autogrow.operators.filter.filter_classes.parent_filter_class import ParentFilter
class GhoseFilter(ParentFilter):
"""
This runs a Ghose filter for drug-likeliness. Ghose filter filters
molecules by Molecular weight (MW), the number of atoms, and the logP
value.
We protonate the mol in this filter because hydrogens affect
atom count. Our Ghose implementation counts hydrogens in against
the total number of atoms.
To pass the filter a molecule must be:
MW between 160 and 480 dalton
Number of Atoms: between 20 and 70
logP between -0,4 and +5,6
If you use the Ghose Filter please cite: A.K. Ghose et al. A
knowledge-based approach in designing combinatorial or medicinal chemistry
libraries for drug discovery. 1. A qualitative and quantitative
characterization of known drug databases Journal of Combinatorial
Chemistry, 1 (1999), pp. 55-68
Inputs:
:param class ParentFilter: a parent class to initialize off
"""
def run_filter(self, mol):
"""
This runs a Ghose filter for drug-likeliness. Ghose filter filters
molecules by Molecular weight (MW), the number of atoms, and the logP
value.
We protonate the mol in this filter because hydrogens affect
atom count. Our Ghose implementation counts hydrogens in against
the total number of atoms.
To pass the filter a molecule must be:
MW between 160 and 480 dalton
Number of Atoms: between 20 and 70
logP between -0,4 and +5,6
Inputs:
:param rdkit.Chem.rdchem.Mol object mol: An rdkit mol object to be
tested if it passes the filters
Returns:
:returns: bool bool: True if the mol passes the filter; False if it
fails the filter
"""
# Make a copy of the mol so we can AddHs without affecting other filters
# number of atoms is altered by the presence/absence of hydrogens.
# Our Ghose filter counts hydrogenss towards atom count
copy_mol = copy.deepcopy(mol)
copy_mol = Chem.AddHs(copy_mol)
exact_mwt = Descriptors.ExactMolWt(copy_mol)
if ((exact_mwt < 160) or (exact_mwt > 480)):
return False
num_atoms = copy_mol.GetNumAtoms()
if ((num_atoms < 20) or (num_atoms > 70)):
return False
# molar Refractivity
MolMR = Crippen.MolMR(copy_mol)
if ((MolMR < 40) or (MolMR > 130)):
return False
# molar LogP
mol_log_p = Crippen.MolLogP(copy_mol)
if ((mol_log_p < -0.4) or (mol_log_p > 5.6)):
return False
# passed all filters
return True
| 3,772 | 33.614679 | 85 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/filter_children_classes/__init__.py | """
This imports all of the filters within the FilterClasses file. This is very
important as the filters will not work if this doesn't exist.
This is a dynamic and modular way of doing these imports.
Code is taken from:
https://stackoverflow.com/questions/1057431/how-to-load-all-modules-in-a-folder
"""
from os.path import dirname, basename, isfile
import glob
modules = glob.glob(dirname(__file__) + "/*.py")
__all__ = [basename(f)[:-3] for f in modules if isfile(f) and not f.endswith("__init__.py")]
from . import *
| 526 | 26.736842 | 92 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/filter_children_classes/pains_filter.py | """#PAINS Filter
This will filter a ligand using a PAINS filter. PAINS eliminates of Pan Assay
Interference Compounds using substructure a search.
This will include PAINS_A, PAINS_B, and PAINS_C filtering.
This script relies on the RDKit predefined FilterCatalog. FilterCatalog is
maintained by RDKit.
If using the PAINS filter please cite: Baell JB, Holloway GA. New Substructure
Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening
Libraries and for Their Exclusion in Bioassays. J Med Chem 53 (2010) 2719D40.
doi:10.1021/jm901137j.
"""
import __future__
from rdkit.Chem import FilterCatalog
from rdkit.Chem.FilterCatalog import FilterCatalogParams
from autogrow.operators.filter.filter_classes.parent_filter_class import ParentFilter
class PAINSFilter(ParentFilter):
"""
This will filter a ligand using a PAINS filter. PAINS eliminates of Pan
Assay Interference Compounds using substructure a search.
This will include PAINS_A, PAINS_B, and PAINS_C filtering.
This script relies on the RDKit predefined FilterCatalog. FilterCatalog is
maintained by RDKit.
If using the PAINS filter please cite: Baell JB, Holloway GA. New
Substructure Filters for Removal of Pan Assay Interference Compounds
(PAINS) from Screening Libraries and for Their Exclusion in Bioassays. J
Med Chem 53 (2010) 2719D40. doi:10.1021/jm901137j.
Inputs:
:param class ParentFilter: a parent class to initialize off
"""
def __init__(self):
"""
This loads in the filters which will be used.
"""
self.filters_list = self.get_filters_list()
def get_filters_list(self):
"""
This loads in the filters which will be used.
Returns:
:returns: rdkit.Chem.rdfiltercatalog.FilterCatalog filters: A set of
RDKit Filters
"""
# Make a list of all the different PAINS Filters. PAINS should include
# PAINS_A,PAINS_B, and PAINS_C, but because RDKit documentation
# doesn't specify this explicitly we have included all 4 of the PAINS
# FilterCatalogs for precaution.
params_PAINS_A = FilterCatalogParams()
params_PAINS_A.AddCatalog(FilterCatalogParams.FilterCatalogs.PAINS_A)
params_PAINS_B = FilterCatalogParams()
params_PAINS_B.AddCatalog(FilterCatalogParams.FilterCatalogs.PAINS_B)
params_PAINS_C = FilterCatalogParams()
params_PAINS_C.AddCatalog(FilterCatalogParams.FilterCatalogs.PAINS_C)
params_PAINS = FilterCatalogParams()
params_PAINS.AddCatalog(FilterCatalogParams.FilterCatalogs.PAINS)
params_list = [params_PAINS_A, params_PAINS_B, params_PAINS_C, params_PAINS]
filters_list = []
for param in params_list:
filter = FilterCatalog.FilterCatalog(param)
filters_list.append(filter)
return filters_list
def run_filter(self, mol):
"""
Runs a PAINS filter by matching common false positive molecules to the
current mol.
This will filter a ligand using a PAINS filter. PAINS eliminates of
Pan Assay Interference Compounds using substructure a search.
Based on the PAINS filter implementation in RDKit described in
http://rdkit.blogspot.com/2016/04/changes-in-201603-release-filtercatalog.html
Inputs:
:param rdkit.Chem.rdchem.Mol object mol: An rdkit mol object to be
tested if it passes the filters Returns:
Returns:
:returns: bool bool: True if the mol passes the filter;
False if it fails the filter
"""
# This is our set of all the PAINS filters
for filters in self.filters_list:
# If the mol matches a mol in the filter list we return a False
# (as it failed the filter)
if filters.HasMatch(mol) is True:
return False
# if No matches are found to filter list this will return a True as it
# Passed the filter.
return True
| 4,056 | 35.881818 | 86 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/filter_children_classes/nih_filter.py | """#NIH Filter
This will filter a ligand using the NIH filter to remove ligands with
undersirable functional groups.
This script relies on the RDKit predefined FilterCatalog. FilterCatalog is
maintained by RDKit.
If using the NIH filter please cite: Jadhav A, et al. Quantitative Analyses of
Aggregation, Autofluorescence, and Reactivity Artifacts in a Screen for
Inhibitors of a Thiol Protease. J Med Chem 53 (2009) 37D51.
doi:10.1021/jm901070c.
"""
import __future__
import rdkit
from rdkit.Chem import FilterCatalog
from rdkit.Chem.FilterCatalog import FilterCatalogParams
from autogrow.operators.filter.filter_classes.parent_filter_class import ParentFilter
class NIHFilter(ParentFilter):
"""
This will filter a ligand using a NIH screening filter. This script relies
on the RDKit predefined FilterCatalog. FilterCatalog is maintained by
RDKit.
The NIH filter is used to eliminate ligands with undersirable functional
groups
If using the NIH filter please cite: Jadhav A, et al. Quantitative
Analyses of Aggregation, Autofluorescence, and Reactivity Artifacts in a
Screen for Inhibitors of a Thiol Protease. J Med Chem 53 (2009) 37D51.
doi:10.1021/jm901070c.
Inputs:
:param class ParentFilter: a parent class to initialize off of.
"""
def __init__(self):
"""
This loads in the filters which will be used.
"""
self.filters = self.get_filters()
def get_filters(self):
"""
This loads in the filters which will be used.
Returns:
:returns: rdkit.Chem.rdfiltercatalog.FilterCatalog filters: A set of
RDKit Filters
"""
# Make a list of the NIH filter.
params = FilterCatalogParams()
params.AddCatalog(FilterCatalogParams.FilterCatalogs.NIH)
# This is our set of all the NIH filters
filters = FilterCatalog.FilterCatalog(params)
return filters
def run_filter(self, mol):
"""
Runs a NIH filter by matching common false positive molecules to the
current mol.
The NIH filter is used to eliminate ligands with undersirable
functional groups
Based on the PAINS filter implementation in RDKit described in
http://rdkit.blogspot.com/2016/04/changes-in-201603-release-filtercatalog.html
Inputs:
:param rdkit.Chem.rdchem.Mol object mol: An rdkit mol object to be
tested if it passes the filters
Returns:
:returns: bool bool: True if the mol passes the filter; False if it
fails the filter
"""
# If the mol matches a mol in the filter list. we return a False (as
# it failed the filter)
if self.filters.HasMatch(mol) is True:
return False
# if No matches are found to filter list this will return a True
# as it Passed the filter.
return True
| 2,926 | 31.164835 | 86 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/conversion_to_3d.py | """
Run file type conversions from .smi to .sdf to .pdb
"""
import __future__
import glob
import sys
import os
from os.path import basename
import rdkit
import rdkit.Chem as Chem
from func_timeout import func_timeout
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
# Some pathing to allow for importing Gypsum Functions
CURRENT_DIR = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
GYPSUM_DIR = str(CURRENT_DIR) + os.sep + "convert_files" + os.sep + "gypsum_dl"
GYPSUM_GYPSUM_DIR = (
str(CURRENT_DIR)
+ os.sep
+ "convert_files"
+ os.sep
+ "gypsum_dl"
+ os.sep
+ "gypsum_dl"
)
sys.path.extend([GYPSUM_DIR, CURRENT_DIR, GYPSUM_GYPSUM_DIR])
import autogrow.operators.convert_files.gypsum_dl.gypsum_dl.MolObjectHandling as MOH
from autogrow.operators.convert_files.gypsum_dl.gypsum_dl.Start import prepare_molecules
class StdoutRedirection:
"""Standard output redirection context manager
Class taken from
https://stackoverflow.com/questions/4110891/how-to-redirect-the-output-of-print-to-a-txt-file
"""
def __init__(self, path):
"""
Inputs:
:param str path: the path
"""
self._path = path
def __enter__(self):
"""
This will return the class self object
and flush the print statements.
Returns:
:returns: self self: class self object
"""
sys.stdout.flush()
sys.stdout = open(self._path, mode="w")
sys.stdout.flush()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
"""
Exit the output piping
Inputs:
:param obj exc_type: exc_type
:param obj exc_val: exc_val
:param obj exc_tb: exc_tb
"""
sys.stdout.flush()
sys.stdout.close()
sys.stdout = sys.__stdout__
def convert_to_3d(vars, smi_file, smile_file_directory):
"""
This function converts SMILES from 1D to 3D using gypsum Gypsum converts
SMILES in an .smi file to 3D .sdf files Then rdkit converts the sdfs to
PDB files.
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param str smi_file: the file name of the .smi file
:param srt smile_file_directory: the directory path which contains the
.smi file
"""
print("CONVERTING SMILES TO SDF")
# convert smiles in an .SMI file to sdfs using gypsum
gypsum_output_folder_path = convert_smi_to_sdfs_with_gypsum(vars, smi_file, smile_file_directory)
print("CONVERTING SMILES TO SDF COMPLETED")
print("CONVERTING SDF TO PDB") ### using rdkit
convert_sdf_to_pdbs(vars, smile_file_directory, gypsum_output_folder_path)
print("CONVERTING SDF TO PDB COMPLETED")
def convert_smi_to_sdfs_with_gypsum(vars, gen_smiles_file, smile_file_directory):
"""
Convert a file of SMILES to a set of 3d .sdf files using Gypsum. This does
so by making a set of .json files for running Gypsum for every ligand in
the .smi file.
This will print out the list of ligands which failed to convert to 3D.
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param str gen_smiles_file: the file name of the .smi file to be converted
to 3D sdf's
:param srt smile_file_directory: the directory path which contains the
.smi file
Returns:
:returns: str gypsum_output_folder_path: a path to the folder with all of
the 3D sdf's created by gypsum.
"""
max_variants_per_compound = vars["max_variants_per_compound"]
gypsum_thoroughness = vars["gypsum_thoroughness"]
min_ph = vars["min_ph"]
max_ph = vars["max_ph"]
pka_precision = vars["pka_precision"]
gypsum_timeout_limit = vars["gypsum_timeout_limit"]
# Make a new folder to put gypsum .smi's and json. Name folder
# gypsum_submission_files.
folder_path = "{}gypsum_submission_files{}".format(smile_file_directory, os.sep)
if os.path.exists(folder_path) is False:
os.makedirs(folder_path)
# Make Output for Gypsum folder (where .sdf's go)
gypsum_output_folder_path = "{}3D_SDFs{}".format(smile_file_directory, os.sep)
if os.path.exists(gypsum_output_folder_path) is False:
os.makedirs(gypsum_output_folder_path)
# Make a folder to put the log files into within the 3D_SDFs folder
gypsum_log_path = "{}log{}".format(gypsum_output_folder_path, os.sep)
if os.path.exists(gypsum_log_path) is False:
os.makedirs(gypsum_log_path)
# Make All of the json files to submit to gypsum
list_of_gypsum_params = make_smi_and_gyspum_params(
gen_smiles_file,
folder_path,
gypsum_output_folder_path,
max_variants_per_compound,
gypsum_thoroughness,
min_ph,
max_ph,
pka_precision,
)
# create a the job_inputs to run gypsum in multithread
job_input = tuple(
[tuple([gypsum_log_path, gypsum_params, gypsum_timeout_limit,])
for gypsum_params in list_of_gypsum_params]
)
sys.stdout.flush()
failed_to_convert = vars["parallelizer"].run(
job_input, run_gypsum_multiprocessing
)
sys.stdout.flush()
'''
fail: return smiles
success: return None
'''
lig_failed_to_convert = [x for x in failed_to_convert if x is not None]
lig_failed_to_convert = list(set(lig_failed_to_convert))
if len(lig_failed_to_convert) > 0:
print("The Following ligands Failed to convert in Gypsum")
print("Likely due to a Timeout")
print(lig_failed_to_convert)
sys.stdout.flush()
return gypsum_output_folder_path
def make_smi_and_gyspum_params(gen_smiles_file, folder_path,
gypsum_output_folder_path, max_variance,
gypsum_thoroughness, min_ph, max_ph,
pka_precision):
"""
Make an individual .smi file and parameter dictionary to submit to Gypsum
for every ligand in the generation_*_to_convert.smi file.
The .smi file for each ligand will be noted within the dictionary as
"source".
Inputs:
:param str gen_smiles_file: the file name of the .smi file to be converted
to 3D sdf's
:param srt folder_path: the directory path which will contain the inputs
and outputs from Gypsum
:param str gypsum_output_folder_path: a path to the folder with all of the
3D sdf's created by gypsum.
:param int max_variance: User variable for how many conformers per ligand
should be made by Gypsum
:param int gypsum_thoroughness: User variable for How widely Gypsum-DL
will search for low-energy conformers. Larger values increase run times
but can produce better results
:param float min_ph: User variable for Minimum pH to consider by
Dimorphite-DL
:param float max_ph: User variable for Maximum pH to consider by
Dimorphite-DL
:param float pka_precision: User variable for Size of pH substructure
ranges by Dimorphite-DL
Returns:
:returns: list list_of_gypsum_params: a list of dictionaries. Each
dictionary contains the Gypsum-DL parameters to convert a single
ligand from SMILES to 3D .sdf
"""
list_of_gypsum_params = []
with open(gen_smiles_file) as smiles_file:
for line in smiles_file:
if line == "\n":
continue
line = line.replace("\n", "")
line = line.replace(" ", "\t")
parts = line.split("\t") # split line into parts separated by 4-spaces
if len(parts) == 0 or len(parts) == 1:
print(parts)
smile = parts[0]
# ligand_name example
# (Gen_30_Cross_639427+Gen_31_Cross_717928)Gen_34_Cross_709666 But
# bash doesn't like + or () for file names so we will abridge
# lig_name_short name for above example becomes
# Gen_34_Cross_709666 if ligand is from the source files we wont
# split the name
ligand_name = parts[1]
if len(ligand_name.split(")")) == 2:
lig_name_short = ligand_name.split(")")[1]
elif len(ligand_name.split(")")) == 1:
lig_name_short = ligand_name
else:
printout = "Ligand name failed to abridge. Smiles may be \
named in improper format please separate with _ \
or camelcase. Our formatting is: \
(Gen_2_Cross_631+Gen_3_Cross_744)Gen_4_Cross_702 \
which reads as Gen_34_Cross_702 (aka ligand 702) \
was produced by crossover using ligands: \
Gen_2_Cross_631 and Gen_3_Cross_744. \
This will abridge to Gen_4_Cross_702 for saving \
files.\nThe failed ligand name was \
{}".format(ligand_name)
print(printout)
raise Exception(printout)
smi_line = "{}\t{}".format(smile, lig_name_short)
smi_path = "{}{}.smi".format(folder_path, lig_name_short)
# make .smi file
with open(smi_path, "w") as smi_file:
smi_file.write(smi_line)
# Make .json file
gypsum_params = {
"source": smi_path,
"output_folder": gypsum_output_folder_path,
"num_processors": 1,
"job_manager": "serial",
"use_durrant_lab_filters": True,
"max_variants_per_compound": max_variance,
"thoroughness": gypsum_thoroughness,
"separate_output_files": True,
"add_pdb_output": False,
"add_html_output": False,
"min_ph": min_ph,
"max_ph": max_ph,
"pka_precision": pka_precision,
"skip_optimize_geometry": False,
"skip_alternate_ring_conformations": False,
"skip_adding_hydrogen": False,
"skip_making_tautomers": False,
"skip_enumerate_chiral_mol": False,
"skip_enumerate_double_bonds": False,
"let_tautomers_change_chirality": False,
"2d_output_only": False,
"cache_prerun": False,
"test": False,
}
list_of_gypsum_params.append(gypsum_params)
return list_of_gypsum_params
def run_gypsum_multiprocessing(gypsum_log_path, gypsum_params,
gypsum_timeout_limit):
"""
This converts the a single ligand from a SMILE to a 3D SDF using Gypsum.
This is used within a multithread.
This uses a Try statement and a bash script to be able to create a timeout
to prevent stalling or very long conversions.
Inputs:
:param str gypsum_log_path: a path to the folder to place the log files
produced when running gypsum.
:param dict gypsum_params: dictionary of params to be feed to Gypsum-DL to
convert to 3D sdf for a single ligand
:param int gypsum_timeout_limit: this is taken from
vars["gypsum_timeout_limit"]. It determines the maximum amount of time to
run Gypsum per ligand
Returns:
:returns: str lig_id: the name of the ligand if it failed or None if it
successfully converted to 3D sdf.
"""
current_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
gypsum_dir = str(current_dir) + os.sep + "convert_files" + os.sep + "gypsum_dl" + os.sep
gypsum_gypsum_dir = str(gypsum_dir) + os.sep + "gypsum_dl" + os.sep
sys.path.extend([current_dir, gypsum_dir, gypsum_gypsum_dir])
lig_id = gypsum_params["source"].split(os.sep)[-1].replace(".smi", "")
log_file = "{}{}_log.txt".format(gypsum_log_path, lig_id)
try:
with StdoutRedirection(log_file):
func_timeout(gypsum_timeout_limit, prepare_molecules, args=(gypsum_params,))
sys.stdout.flush()
except:
# This Ligand Timed out
return lig_id
# Check if it worked if it failed return lig_id if it works return None
did_gypsum_complete = check_gypsum_log_did_complete(log_file)
if did_gypsum_complete in [None, False]:
# Failed to convert
return lig_id
return None
def check_gypsum_log_did_complete(log_file_path):
"""
This function checks a log_file_path to see if the last line reads
"TIMEOUT". If it does then gypsum timed out before converting a .smi. If
it timedout return False. If it completed return True
Inputs:
:param str log_file_path: the path to the log from converting a ligand
with Gypsum.
Returns:
:returns: bool bol: Returns True if the conversion worked (or if it was a
blank .smi with no name or SMILES). Returns False if it timedout. Returns
None if it failed to convert due to errors with the SMILES string, .json,
or .smi.
-examples would be
1) if the SMILE was invalid chemically
2) There was SMILE name but no SMILE string
3) The SMILES wasn't a SMILE but rather a different data type
ie None, False, non-chemical string...
"""
if os.path.exists(log_file_path) is False:
return False
sys.stdout.flush()
with open(log_file_path) as log:
data = log.readlines()
if len(data) == 0:
# For whatever reason it didn't write to the file.
return None
lastline = data[-1]
lastline = lastline.replace("\n", "")
lastline = lastline.replace("\t", "")
lastline = lastline.replace(" ", "")
if str(lastline) == "TIMEOUT":
return False
if str(lastline) == "FAILED ERRORS WITH THE LIGAND":
return None
# passes
return True
def convert_sdf_to_pdbs(vars, gen_folder_path, sdfs_folder_path):
"""
It will find any .sdf files within the folder_path and convert them to
.pdb types using rdkit.Chem. It also makes a subfolder to store the pdb
files if one doesn't already exist in the folder_path.
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param str gen_folder_path: Path of the folder for the current generation
:param str sdfs_folder_path: Path of the folder with all of the 3D .sdf
files to convert
"""
files = []
if os.path.isdir(sdfs_folder_path):
# so it's a directory, go through the directory and find all the sdf files
if sdfs_folder_path[-1:] != os.sep:
sdfs_folder_path = (
sdfs_folder_path + os.sep
) # so add a / to the end of the directory
files.extend(glob.glob(sdfs_folder_path + "*.sdf"))
files.extend(glob.glob(sdfs_folder_path + "*.SDF"))
files = list(set(files))
if len(files) == 0:
printout = "\nThere are no sdf's to convert to PDB's. There may be an issue with Gypsum.\n"
print(printout)
raise Exception(printout)
# create a new subfolder if one doesn't already exist. folder will be with
# the generation and will be titled PDBs pdb_subfolder_path will become
# the the output folder
pdb_subfolder_path = gen_folder_path + "_PDB" + os.sep
if not os.path.isdir(pdb_subfolder_path):
os.makedirs(pdb_subfolder_path)
job_inputs = []
for file_path in files:
if "params" in file_path:
continue
job_inputs.append(tuple([pdb_subfolder_path, file_path]))
job_inputs = tuple(job_inputs)
# Check that there are .sdf files to test. If not raise Exception
if len(job_inputs) == 0:
printout = "\n\nThere are no SDF files were found to convert to PDB. "
printout = printout + "This may be a problem with the Gypsum-DL "
printout = printout + "settings.\nPlease check that the `--gypsum_timeout_limit` "
printout = printout + "is appropriate relative to the `--gypsum_thoroughness` "
printout = printout + "and `--max_variants_per_compound` parameters.\n"
raise Exception(printout)
# Convert sdf files to pdbs in multithread
vars["parallelizer"].run(job_inputs, convert_single_sdf_to_pdb)
def convert_single_sdf_to_pdb(pdb_subfolder_path, sdf_file_path):
"""
This will convert a given .sdf into separate .pdb files.
Inputs:
:param str pdb_subfolder_path: Path of the folder to place all created pdb
files
:param str sdf_file_path: Path of the sdf_file_path to convert to pdb
files
"""
if os.path.exists(sdf_file_path) is True:
file_basename = basename(sdf_file_path)
file_basename = file_basename.split("__input1")[0]
file_output_name = "{}{}_".format(pdb_subfolder_path, file_basename)
try:
mols = Chem.SDMolSupplier(sdf_file_path, sanitize=False, removeHs=False, strictParsing=False)
except:
mols = None
# if mols is None rdkit couldn't import the sdf so we will not do anything else
if mols is None:
pass
elif len(mols) == 0:
pass
else:
try:
mols_no_hydrogen = Chem.SDMolSupplier(
sdf_file_path, sanitize=True, removeHs=True, strictParsing=False
)
except:
mols_no_hydrogen = [None for x in range(0, len(mols))]
# if len(mols)==0 gypsum output a blank file by accident
# if mols is None rdkit couldn't import the sdf
if len(mols) != 0:
counter = 0
for i in range(0, len(mols)):
mol = mols[i]
# Extra precaution to prevent None's within a set of good
# mols
if mol is None:
continue
mol = MOH.check_sanitization(mol)
# Filter out any which failed
if mol is None:
continue
# pdb_name indexed to 1
pdb_name = "{}_{}.pdb".format(file_output_name, counter + 1)
if mol is not None: # For extra precaution...
Chem.MolToPDBFile(mol, pdb_name, flavor=32)
# Add header to PDB file with SMILES containing
# protanation and stereochem
no_hydrogen_smiles = mols_no_hydrogen[i]
if no_hydrogen_smiles is None:
no_hydrogen_smiles = Chem.MolToSmiles(mol)
if no_hydrogen_smiles is None:
print("SMILES was None for: ", pdb_name)
printout = "REMARK Final SMILES string: {}\n".format("None")
elif type(no_hydrogen_smiles) == str:
printout = "REMARK Final SMILES string: {}\n".format(
no_hydrogen_smiles
)
elif type(no_hydrogen_smiles) == type(Chem.MolFromSmiles("C")):
printout = "REMARK Final SMILES string: {}\n".format(
Chem.MolToSmiles(no_hydrogen_smiles)
)
with open(pdb_name) as f:
printout = printout + f.read()
with open(pdb_name, "w") as f:
f.write(printout)
printout = ""
counter = counter + 1
else:
pass
| 19,850 | 36.243902 | 105 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/run_gypsum_dl.py | #!/usr/bin/env python
# Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Gypsum-DL 1.1.7 is a conversion script to transform smiles strings and 2D SDFs
into 3D models.
"""
def print_gypsum_citation():
"""
Print out the citation for the Gypsum-DL paper.
Because this is before the Parallelizer is initiallized it requires
limiting the print statement to the cpu ranked=0.
Without this check, in MPI mode it would print once per available cpu.
"""
import sys
# And always report citation information.
citation_print = "\nIf you use Gypsum-DL in your research, please cite:\n\n"
citation_print = (
citation_print
+ "Ropp, Patrick J., Jacob O. Spiegel, Jennifer L. Walker, Harrison Green,\n"
)
citation_print = (
citation_print
+ "Guillermo A. Morales, Katherine A. Milliken, John J. Ringe, and Jacob D. Durrant.\n"
)
citation_print = (
citation_print
+ "(2019) Gypsum-DL: An Open-source Program for Preparing Small-molecule Libraries for \n"
)
citation_print = (
citation_print
+ "Structure-based Virtual Screening. Journal of Cheminformatics 11:1. "
)
citation_print = citation_print + "\ndoi:10.1186/s13321-019-0358-3.\n"
try:
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.rank
if rank == 0:
print(citation_print)
except:
print(citation_print)
# print out the citation of Gypsum-DL paper.
print_gypsum_citation()
import argparse
import copy
from gypsum_dl.Start import prepare_molecules
from gypsum_dl.Test.Tester import run_test
from gypsum_dl import Utils
PARSER = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description="""
Gypsum-DL 1.1.7, a free, open-source program for preparing 3D small-molecule
models. Beyond simply assigning atomic coordinates, Gypsum-DL accounts for
alternate ionization, tautomeric, chiral, cis/trans isomeric, and
ring-conformational forms.""",
epilog="""
EXAMPLES OF USE:
1. Prepare a virtual library and save all 3D models to a single SDF file in the
present directory:
python run_gypsum_dl.py --source ./examples/sample_molecules.smi
2. Instead save all 3D models to a different, existing folder:
python run_gypsum_dl.py --source ./examples/sample_molecules.smi \\
--output_folder /my/folder/
3. Additionally save the models associated with each input molecule to
separate files:
python run_gypsum_dl.py --source ./examples/sample_molecules.smi \\
--output_folder /my/folder/ --separate_output_files
4. In addition to saving a 3D SDF file, also save 3D PDB files and an HTML file
with 2D structures (for debugging).
python run_gypsum_dl.py --source ./examples/sample_molecules.smi \\
--output_folder /my/folder/ --add_pdb_output --add_html_output
5. Save at most two variants per input molecule:
python run_gypsum_dl.py --source ./examples/sample_molecules.smi \\
--output_folder /my/folder/ --max_variants_per_compound 2
6. Control how Gypsum-DL ionizes the input molecules:
python run_gypsum_dl.py --source ./examples/sample_molecules.smi \\
--output_folder /my/folder/ --min_ph 12 --max_ph 14 --pka_precision 1
7. Run Gypsum-DL in serial mode (using only one processor):
python run_gypsum_dl.py --source ./examples/sample_molecules.smi \\
--job_manager serial
8. Run Gypsum-DL in multiprocessing mode, using 4 processors:
python run_gypsum_dl.py --source ./examples/sample_molecules.smi \\
--job_manager multiprocessing --num_processors 4
9. Run Gypsum-DL in mpi mode using all available processors:
mpirun -n $NTASKS python -m mpi4py run_gypsum_dl.py \\
--source ./examples/sample_molecules.smi \\
--job_manager mpi --num_processors -1
10. Gypsum-DL can also take parameters from a JSON file:
python run_gypsum_dl.py --json myparams.json
Where myparams.json might look like:
{
"source": "./examples/sample_molecules.smi",
"separate_output_files": true,
"job_manager": "multiprocessing",
"output_folder": "/my/folder/",
"add_pdb_output": true,
"add_html_output": true,
"num_processors": -1
}
""",
)
PARSER.add_argument(
"--json",
"-j",
type=str,
metavar="param.json",
help="Name of a json file containing all parameters. \
Overrides all other arguments specified at the commandline.",
)
PARSER.add_argument(
"--source",
"-s",
type=str,
metavar="input.smi",
help="Name of the source file (e.g., input.smi).",
)
PARSER.add_argument(
"--output_folder",
"-o",
type=str,
help="The path to an existing folder where the Gypsum-DL "
+ "output file(s) will be saved.",
)
PARSER.add_argument(
"--job_manager",
type=str,
default="multiprocessing",
choices=["mpi", "multiprocessing", "serial"],
help="Determine what style of multiprocessing to use: mpi, \
multiprocessing, or serial. Serial will override the \
num_processors flag, forcing it to be one. MPI mode \
requires mpi4py 2.1.0 or higher and should be executed \
as: mpirun -n $NTASKS python -m mpi4py run_gypsum_dl.py \
...-settings...",
)
PARSER.add_argument(
"--num_processors",
"-p",
type=int,
metavar="N",
default=1,
help="Number of processors to use for parallel \
calculations.",
)
PARSER.add_argument(
"--max_variants_per_compound",
"-m",
type=int,
metavar="V",
help="The maximum number of variants to create per input \
molecule.",
)
PARSER.add_argument(
"--thoroughness",
"-t",
type=int,
help="How widely to search for low-energy conformers. \
Larger values increase run times but can produce better \
results.",
)
PARSER.add_argument(
"--separate_output_files",
action="store_true",
help="Indicates that the outputs should be split between \
files. If true, each output .sdf file will correspond to a \
single input file, but different 3D conformers will still \
be stored in the same file.",
)
PARSER.add_argument(
"--add_pdb_output",
action="store_true",
help="Indicates that the outputs should also be written in \
the .pdb format. Creates one PDB file for each molecular \
variant.",
)
PARSER.add_argument(
"--add_html_output",
action="store_true",
help="Indicates that the outputs should also be written in \
the .html format, for debugging. Attempts to open a \
browser for viewing.",
)
PARSER.add_argument(
"--min_ph", metavar="MIN", type=float, help="Minimum pH to consider."
)
PARSER.add_argument(
"--max_ph", metavar="MAX", type=float, help="Maximum pH to consider."
)
PARSER.add_argument(
"--pka_precision",
metavar="D",
type=float,
help="Size of pH substructure ranges. See Dimorphite-DL \
publication for details.",
)
PARSER.add_argument(
"--skip_optimize_geometry", action="store_true", help="Skips the optimization step."
)
PARSER.add_argument(
"--skip_alternate_ring_conformations",
action="store_true",
help="Skips the non-aromatic ring-conformation \
generation step.",
)
PARSER.add_argument(
"--skip_adding_hydrogen", action="store_true", help="Skips the ionization step."
)
PARSER.add_argument(
"--skip_making_tautomers",
action="store_true",
help="Skips tautomer-generation step.",
)
PARSER.add_argument(
"--skip_enumerate_chiral_mol",
action="store_true",
help="Skips the ennumeration of unspecified chiral \
centers.",
)
PARSER.add_argument(
"--skip_enumerate_double_bonds",
action="store_true",
help="Skips the ennumeration of double bonds.",
)
PARSER.add_argument(
"--let_tautomers_change_chirality",
action="store_true",
help="Allow tautomers that change \
the total number of chiral centers (see README.md for \
further explanation).",
)
PARSER.add_argument(
"--use_durrant_lab_filters",
action="store_true",
help="Use substructure filters to \
remove molecular variants that, though technically \
possible, were judged improbable by members of the \
Durrant lab. See README.md for more details.",
)
PARSER.add_argument(
"--2d_output_only", action="store_true", help="Skips the generate-3D-models step."
)
PARSER.add_argument(
"--cache_prerun",
"-c",
action="store_true",
help="Run this before running Gypsum-DL in mpi mode.",
)
PARSER.add_argument(
"--test", action="store_true", help="Tests Gypsum-DL to check for programming bugs."
)
ARGS_DICT = vars(PARSER.parse_args())
if ARGS_DICT["test"] == True:
run_test()
elif ARGS_DICT["cache_prerun"] == False:
INPUTS = copy.deepcopy(ARGS_DICT)
for k, v in ARGS_DICT.items():
if v is None:
del INPUTS[k]
prepare_molecules(INPUTS)
Utils.log("Finished Gypsum-DL")
else:
pass
| 9,808 | 30.041139 | 98 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/__init__.py | 2 | 0.5 | 1 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/ChemUtils.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
The module includes definitions to manipulate the molecules.
"""
import __future__
import gypsum_dl.Utils as Utils
try:
from rdkit import Chem
from rdkit.Chem import AllChem
except:
Utils.exception("You need to install rdkit and its dependencies.")
def pick_lowest_enrgy_mols(mol_lst, num, thoroughness):
"""Pick molecules with low energies. If necessary, the definition also
makes a conformer without minimization (so not too computationally
expensive).
:param mol_lst: The list of MyMol.MyMol objects.
:type mol_lst: list
:param num: The number of the lowest-energy ones to keep.
:type num: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:return: Returns a list of MyMol.MyMol, the best ones.
:rtype: list
"""
# Remove identical entries.
mol_lst = list(set(mol_lst))
# If the length of the mol_lst is less than num, just return them all.
if len(mol_lst) <= num:
return mol_lst
# First, generate 3D structures. How many? num * thoroughness. mols_3d is
# a list of Gypsum-DL MyMol.MyMol objects.
mols_3d = Utils.random_sample(mol_lst, num * thoroughness, "")
# Now get the energies
data = []
for i, mol in enumerate(mols_3d):
mol.make_first_3d_conf_no_min() # Make sure at least one conformer exists.
if len(mol.conformers) > 0:
energy = mol.conformers[0].energy
data.append((energy, i))
data.sort()
# Now keep only best top few.
data = data[:num]
# Keep just the mols there.
new_mols_list = [mol_lst[d[1]] for d in data]
# Return those molecules.
return new_mols_list
def remove_highly_charged_molecules(mol_lst):
"""Remove molecules that are highly charged.
:param mol_lst: The list of molecules to consider.
:type mol_lst: list
:return: A list of molecules that are not too charged.
:rtype: list
"""
# First, find the molecule that is closest to being neutral.
charges = [Chem.GetFormalCharge(mol.rdkit_mol) for mol in mol_lst]
abs_charges = [abs(c) for c in charges]
idx_of_closest_to_neutral = abs_charges.index(min(abs_charges))
charge_closest_to_neutral = charges[idx_of_closest_to_neutral]
# Now create a new mol list, where the charges deviation from the most
# neutral by no more than 4. Note that this used to be 2, but I increased
# it to 4 to accommodate ATP.
new_mol_lst = []
for i, charge in enumerate(charges):
if abs(charge - charge_closest_to_neutral) <= 4:
new_mol_lst.append(mol_lst[i])
else:
Utils.log(
"\tWARNING: Discarding highly charged form: "
+ mol_lst[i].smiles()
+ "."
)
return new_mol_lst
def bst_for_each_contnr_no_opt(
contnrs,
mol_lst,
max_variants_per_compound,
thoroughness,
crry_ovr_frm_lst_step_if_no_fnd=True,
):
"""Keep only the top few compound variants in each container, to prevent a
combinatorial explosion. This is run periodically on the growing
containers to keep them in check.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param mol_lst: The list of MyMol.MyMol objects.
:type mol_lst: list
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:param crry_ovr_frm_lst_step_if_no_fnd: If it can't find any low-energy
conformers, determines whether to just keep the old ones. Defaults to
True.
:param crry_ovr_frm_lst_step_if_no_fnd: bool, optional
"""
# Remove duplicate ligands from each container.
for mol_cont in contnrs:
mol_cont.remove_identical_mols_from_contnr()
# Group the smiles by contnr_idx.
data = Utils.group_mols_by_container_index(mol_lst)
# Go through each container.
for contnr_idx, contnr in enumerate(contnrs):
contnr_idx = contnr.contnr_idx
none_generated = False
# Pick just the lowest-energy conformers from the new candidates.
# Possible a compound was eliminated early on, so doesn't exist.
if contnr_idx in list(data.keys()):
mols = data[contnr_idx]
# Remove molecules with unusually high charges.
mols = remove_highly_charged_molecules(mols)
# Pick the lowest-energy molecules. Note that this creates a
# conformation if necessary, but it is not minimized and so is not
# computationally expensive.
mols = pick_lowest_enrgy_mols(mols, max_variants_per_compound, thoroughness)
if len(mols) > 0:
# Now remove all previously determined mols for this
# container.
contnr.mols = []
# Add in the lowest-energy conformers back to the container.
for mol in mols:
contnr.add_mol(mol)
else:
none_generated = True
else:
none_generated = True
# No low-energy conformers were generated.
if none_generated:
if crry_ovr_frm_lst_step_if_no_fnd:
# Just use previous ones.
Utils.log(
"\tWARNING: Unable to find low-energy conformations: "
+ contnr.orig_smi_deslt
+ " ("
+ contnr.name
+ "). Keeping original "
+ "conformers."
)
else:
# Discard the conformation.
Utils.log(
"\tWARNING: Unable to find low-energy conformations: "
+ contnr.orig_smi_deslt
+ " ("
+ contnr.name
+ "). Discarding conformer."
)
contnr.mols = []
def uniq_mols_in_list(mol_lst):
# You need to make new molecules to get it to work.
# new_smiles = [m.smiles() for m in self.mols]
# new_mols = [Chem.MolFromSmiles(smi) for smi in new_smiles]
# new_can_smiles = [Chem.MolToSmiles(new_mol, isomericSmiles=True, canonical=True) for new_mol in new_mols]
can_smiles_already_set = set([])
uniq_mols = []
for m in mol_lst:
smi = m.smiles()
if not smi in can_smiles_already_set:
uniq_mols.append(m)
can_smiles_already_set.add(smi)
# if not new_can_smile in can_smiles_already_set:
# # Never seen before
# can_smiles_already_set.add(new_can_smile)
# else:
# # Seen before. Delete!
# self.mols[i] = None
# while None in self.mols:
# self.mols.remove(None)
return uniq_mols
| 8,444 | 34.93617 | 111 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Utils.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Some helpful utility definitions used throughout the code.
"""
import __future__
import subprocess
import textwrap
import random
import string
def group_mols_by_container_index(mol_lst):
"""Take a list of MyMol.MyMol objects, and place them in lists according to
their associated contnr_idx values. These lists are accessed via
a dictionary, where they keys are the contnr_idx values
themselves.
:param mol_lst: The list of MyMol.MyMol objects.
:type mol_lst: list
:return: A dictionary, where keys are contnr_idx values and values are
lists of MyMol.MyMol objects
:rtype: dict
"""
# Make the dictionary.
grouped_results = {}
for mol in mol_lst:
if mol is None:
# Ignore molecules that are None.
continue
idx = mol.contnr_idx
if not idx in grouped_results:
grouped_results[idx] = []
grouped_results[idx].append(mol)
# Remove redundant entries.
for key in list(grouped_results.keys()):
grouped_results[key] = list(set(grouped_results[key]))
return grouped_results
def random_sample(lst, num, msg_if_cut=""):
"""Randomly selects elements from a list.
:param lst: The list of elements.
:type lst: list
:param num: The number to randomly select.
:type num: int
:param msg_if_cut: The message to display if some elements must be ignored
to construct the list. Defaults to "".
:param msg_if_cut: str, optional
:return: A list that contains at most num elements.
:rtype: list
"""
try:
# Remove redundancies.
lst = list(set(lst))
except:
# Because someitems lst element may be unhashable.
pass
# Shuffle the list.
random.shuffle(lst)
if num < len(lst):
# Keep the top ones.
lst = lst[:num]
if msg_if_cut != "":
log(msg_if_cut)
return lst
def log(txt, trailing_whitespace=""):
"""Prints a message to the screen.
:param txt: The message to print.
:type txt: str
:param trailing_whitespace: White space to add to the end of the
message, after the trim. "" by default.
:type trailing_whitespace: string
"""
whitespace_before = txt[: len(txt) - len(txt.lstrip())].replace("\t", " ")
print(
textwrap.fill(
txt.strip(),
width=80,
initial_indent=whitespace_before,
subsequent_indent=whitespace_before + " ",
) + trailing_whitespace
)
def fnd_contnrs_not_represntd(contnrs, results):
"""Identify containers that have no representative elements in results.
Something likely failed for the containers with no results.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param results: A list of MyMol.MyMol objects.
:type results: list
:return: A list of integers, the indecies of the contnrs that have no
associated elements in the results.
:rtype: list
"""
# Find ones that don't have any generated. In the context of ionization,
# for example, this is because sometimes Dimorphite-DL failes to producce
# valid smiles. In this case, just use the original smiles. Couldn't find
# a good solution to work around.
# Get a dictionary of all the input smiles. Keys are indexes, values are
# smiles.
idx_to_smi = {}
for idx in range(0, len(contnrs)):
contnr = contnrs[idx]
if not idx in idx_to_smi:
idx_to_smi[idx] = contnrs[idx].orig_smi_deslt
# Now remove from those any that have associated ionized smiles strings.
# These are represented, and so you don't want to include them in the
# return.
for m in results:
if m.contnr_idx in idx_to_smi:
del idx_to_smi[m.contnr_idx]
# Return just the container indexes (the keys).
return list(idx_to_smi.keys())
def print_current_smiles(contnrs):
"""Prints the smiles of the current containers. Helpful for debugging.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
"""
# For debugging.
log(" Contents of MolContainers")
for i, mol_cont in enumerate(contnrs):
log("\t\tMolContainer #" + str(i) + " (" + mol_cont.name + ")")
for i, s in enumerate(mol_cont.all_can_noh_smiles()):
log("\t\t\tMol #" + str(i) + ": " + s)
def exception(msg):
"""Prints an error to the screen and raises an exception.
:param msg: The error message.
:type msg: str
:raises Exception: The error.
"""
log(msg)
log("\n" + "=" * 79)
log("For help with usage:")
log("\tpython run_gypsum_dl.py --help")
log("=" * 79)
log("")
raise Exception(msg)
def slug(strng):
"""Converts a string to one that is appropriate for a filename.
:param strng: The input string.
:type strng: str
:return: The filename appropriate string.
:rtype: str
"""
# See
# https://stackoverflow.com/questions/295135/turn-a-string-into-a-valid-filename
valid_chars = "-_.%s%s" % (string.ascii_letters, string.digits)
if strng == "":
return "untitled"
else:
return "".join([c if c in valid_chars else "_" for c in strng])
| 5,886 | 28.883249 | 84 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/__init__.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# gypsum_dl/gypsum_dl/
# Including the below allows other programs to import functions from
# gypsum-DL.
import sys
import os
current_dir = os.path.dirname(os.path.realpath(__file__))
gypsum_gypsum_dir = current_dir
gypsum_top_dir = os.path.dirname(gypsum_gypsum_dir)
sys.path.extend([gypsum_gypsum_dir, gypsum_top_dir])
| 904 | 33.807692 | 74 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/MolContainer.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module describes the MolContainer, which contains different MyMol.MyMol
objects. Each object in this container is derived from the same input molecule
(so they are variants). Note that conformers (3D coordinate sets) live inside
MyMol.MyMol. So, just to clarify:
MolContainer.MolContainer > MyMol.MyMol > MyMol.MyConformers
"""
import __future__
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
import gypsum_dl.MyMol as MyMol
try:
from rdkit import Chem
except:
Utils.exception("You need to install rdkit and its dependencies.")
class MolContainer:
"""The molecucle container class. It stores all the molecules (tautomers,
etc.) associated with a single input SMILES entry."""
def __init__(self, smiles, name, index, properties):
"""The constructor.
:param smiles: A list of SMILES strings.
:type smiles: str
:param name: The name of the molecule.
:type name: str
:param index: The index of this MolContainer in the main MolContainer
list.
:type index: int
:param properties: A dictionary of properties from the sdf.
:type properties: dict
"""
# Set some variables are set on the container level (not the MyMol
# level)
self.contnr_idx = index
self.contnr_idx_orig = index # Because if some circumstances (mpi),
# might be reset. But good to have
# original for filename output.
self.orig_smi = smiles
self.orig_smi_deslt = smiles # initial assumption
self.mols = []
self.name = name
self.properties = properties
self.mol_orig_frm_inp_smi = MyMol.MyMol(smiles, name)
self.mol_orig_frm_inp_smi.contnr_idx = self.contnr_idx
self.frgs = "" # For caching.
# Save the original canonical smiles
self.orig_smi_canonical = self.mol_orig_frm_inp_smi.smiles()
# Get the number of nonaromatic rings
self.num_nonaro_rngs = len(
self.mol_orig_frm_inp_smi.get_idxs_of_nonaro_rng_atms()
)
# Get the number of chiral centers, assigned
self.num_specif_chiral_cntrs = len(
self.mol_orig_frm_inp_smi.chiral_cntrs_only_asignd()
)
# Also get the number of chiral centers, unassigned
self.num_unspecif_chiral_cntrs = len(
self.mol_orig_frm_inp_smi.chiral_cntrs_w_unasignd()
)
# Get the non-acidic carbon-hydrogen footprint.
self.carbon_hydrogen_count = self.mol_orig_frm_inp_smi.count_hyd_bnd_to_carb()
def mol_with_smiles_is_in_contnr(self, smiles):
"""Checks whether or not a given smiles string is already in this
container.
:param smiles: The smiles string to check.
:type smiles: str
:return: True if it is present, otherwise a new MyMol.MyMol object
corresponding to that smiles.
:rtype: bool or MyMol.MyMol
"""
# Checks all the mols in this container to see if a given smiles is
# already present. Returns a new MyMol object if it isn't, True
# otherwise.
# First, get the set of all cannonical smiles.
# TODO: Probably shouldn't be generating this on the fly every time
# you use it!
can_smi_in_this_container = set([m.smiles() for m in self.mols])
# Determine whether it is already in the container, and act
# accordingly.
amol = MyMol.MyMol(smiles)
if amol.smiles() in can_smi_in_this_container:
return True
else:
return amol
def add_smiles(self, smiles):
"""Adds smiles strings to this container. SMILES are always isomeric
and always unique (canonical).
:param smiles: A list of SMILES strings. If it's a string, it is
converted into a list.
:type smiles: str
"""
# Convert it into a list if it comes in as a string.
if isinstance(smiles, str):
smiles = [smiles]
# Keep only the mols with smiles that are not already present.
for s in smiles:
result = self.mol_with_smiles_is_in_contnr(s)
if result != True:
# Much of the contnr info should be passed to each molecule,
# too, for convenience.
result.name = self.name
result.name = self.orig_smi
result.orig_smi_canonical = self.orig_smi_canonical
result.orig_smi_deslt = self.orig_smi_deslt
result.contnr_idx = self.contnr_idx
self.mols.append(result)
def add_mol(self, mol):
"""Adds a molecule to this container. Does NOT check for uniqueness.
:param mol: The MyMol.MyMol object to add.
:type mol: MyMol.MyMol
"""
self.mols.append(mol)
def all_can_noh_smiles(self):
"""Gets a list of all the noh canonical smiles in this container.
:return: The canonical, noh smiles string.
:rtype: str
"""
smiles = []
for m in self.mols:
if m.rdkit_mol is not None:
smiles.append(m.smiles(True)) # True means noh
return smiles
def get_frags_of_orig_smi(self):
"""Gets a list of the fragments found in the original smiles string
passed to this container.
:return: A list of the fragments, as rdkit.Mol objects. Also saves to
self.frgs.
:rtype: list
"""
if self.frgs != "":
return self.frgs
frags = self.mol_orig_frm_inp_smi.get_frags_of_orig_smi()
self.frgs = frags
return frags
def update_orig_smi(self, orig_smi):
"""Updates the orig_smi string. Used by desalter (to replace with
largest fragment).
:param orig_smi: The replacement smiles string.
:type orig_smi: str
"""
# Update the MolContainer object
self.orig_smi = orig_smi
self.orig_smi_deslt = orig_smi
self.mol_orig_frm_inp_smi = MyMol.MyMol(self.orig_smi, self.name)
self.frgs = ""
self.orig_smi_canonical = self.mol_orig_frm_inp_smi.smiles()
self.num_nonaro_rngs = len(
self.mol_orig_frm_inp_smi.get_idxs_of_nonaro_rng_atms()
)
self.num_specif_chiral_cntrs = len(
self.mol_orig_frm_inp_smi.chiral_cntrs_only_asignd()
)
self.num_unspecif_chiral_cntrs = len(
self.mol_orig_frm_inp_smi.chiral_cntrs_w_unasignd()
)
# None of the mols derived to date, if present, are accurate.
self.mols = []
def add_container_properties(self):
"""Adds all properties from the container to the molecules. Used when
saving final files, to keep a record in the file itself."""
for mol in self.mols:
mol.mol_props.update(self.properties)
mol.set_all_rdkit_mol_props()
def remove_identical_mols_from_contnr(self):
"""Removes itentical molecules from this container."""
# For reasons I don't understand, the following doesn't give unique
# canonical smiles:
# Chem.MolToSmiles(self.mols[0].rdkit_mol, isomericSmiles=True,
# canonical=True)
# # This block for debugging. JDD: Needs attention?
# all_can_noh_smiles = [m.smiles() for m in self.mols] # Get all the smiles as stored.
# wrong_cannonical_smiles = [
# Chem.MolToSmiles(
# m.rdkit_mol, # Using the RdKit mol stored in MyMol
# isomericSmiles=True,
# canonical=True
# ) for m in self.mols
# ]
# right_cannonical_smiles = [
# Chem.MolToSmiles(
# Chem.MolFromSmiles( # Regenerating the RdKit mol from the smiles string stored in MyMol
# m.smiles()
# ),
# isomericSmiles=True,
# canonical=True
# ) for m in self.mols]
# if len(set(wrong_cannonical_smiles)) != len(set(right_cannonical_smiles)):
# Utils.log("ERROR!")
# Utils.log("Stored smiles string in this container:")
# Utils.log("\n".join(all_can_noh_smiles))
# Utils.log("")
# Utils.log("""Supposedly cannonical smiles strings generated from stored
# RDKit Mols in this container:""")
# Utils.log("\n".join(wrong_cannonical_smiles))
# Utils.log("""But if you plop these into chemdraw, you'll see some of them
# represent identical structures.""")
# Utils.log("")
# Utils.log("""Cannonical smiles strings generated from RDKit mols that
# were generated from the stored smiles string in this container:""")
# Utils.log("\n".join(right_cannonical_smiles))
# Utils.log("""Now you see the identical molecules. But why didn't the previous
# method catch them?""")
# Utils.log("")
# Utils.log("""Note that the third method identifies duplicates that the second
# method doesn't.""")
# Utils.log("")
# Utils.log("=" * 20)
# # You need to make new molecules to get it to work.
# new_smiles = [m.smiles() for m in self.mols]
# new_mols = [Chem.MolFromSmiles(smi) for smi in new_smiles]
# new_can_smiles = [Chem.MolToSmiles(new_mol, isomericSmiles=True, canonical=True) for new_mol in new_mols]
# can_smiles_already_set = set([])
# for i, new_can_smile in enumerate(new_can_smiles):
# if not new_can_smile in can_smiles_already_set:
# # Never seen before
# can_smiles_already_set.add(new_can_smile)
# else:
# # Seen before. Delete!
# self.mols[i] = None
# while None in self.mols:
# self.mols.remove(None)
self.mols = ChemUtils.uniq_mols_in_list(self.mols)
def update_idx(self, new_idx):
"""Updates the index of this container.
:param new_idx: The new index.
:type new_idx: int
"""
if type(new_idx) != int:
Utils.exception("New idx value must be an int.")
self.contnr_idx = new_idx
self.mol_orig_frm_inp_smi.contnr_idx = self.contnr_idx
| 11,036 | 35.667774 | 115 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Parallelizer.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Parallelizer.py
Abstract parallel computation utility.
The "parallelizer" object exposes a simple map interface that takes a function
and a list of arguments and returns the result of applying the function to
each argument. Internally, the parallelizer class can determine what parallel
capabilities are present on a system and automatically pick between "mpi",
"multiprocessing" or "serial" in order to speed up the map operation. This
approach simplifies development and allows the same program to run on a laptop
or a high-performance computer cluster, utilizing the full resources of each
system. (Description provided by Harrison Green.)
"""
import __future__
import multiprocessing
import sys
MPI_installed = False
try:
import mpi4py
MPI_installed = True
except:
MPI_installed = False
class Parallelizer(object):
"""
Abstract parallelization class
"""
def __init__(self, mode=None, num_procs=None, flag_for_low_level=False):
"""
This will initialize the Parallelizer class and kick off the specific classes for multiprocessing and MPI.
Default num_procs is all the processesors possible
This will also establish:
:self bol self.HAS_MPI: If true it can import mpi4py;
if False it either cant import mpi4py or the mode/flag_for_low_level dictates not to check mpi4py import
due to issues which arrise when mpi is started within a program which is already mpi enabled
:self str self.mode: The mode which will be used for this paralellization. This determined by mode and the enviorment
default is mpi if the enviorment allows, if not mpi then multiprocessing on all processors unless stated otherwise.
:self class self.parallel_obj: This is the obstantiated object of the class of parallizations.
ie) if self.mode=='mpi' self.parallel_obj will be an instance of the mpi class
This style of retained parallel_obj to be used later is important because this is the object which controls the work nodes and maintains the mpi universe
self.parallel_obj will be set to None for simpler parallization methods like serial
:self int self.num_processor: the number of processors or nodes that will be used. If None than we will use all available nodes/processors
This will be overriden and fixed to a single processor if mode==serial
Inputs:
:param str mode: the multiprocess mode to be used, ie) serial, multiprocessing, mpi, or None:
if None then we will try to pick a possible multiprocessing choice. This should only be used for
top level coding. It is best practice to specify which multiprocessing choice to use.
if you have smaller programs used by a larger program, with both mpi enabled there will be problems, so specify multiprocessing is important.
:param int num_procs: the number of processors or nodes that will be used. If None than we will use all available nodes/processors
This will be overriden and fixed to a single processor if mode==serial
:param bol flag_for_low_level: this will override mode and number of processors and set it to a multiprocess as serial. This is useful because
a low-level program in mpi mode referenced by a top level program in mpi mode will have terrible problems. This means you can't mpi-multiprocess inside an mpi-multiprocess.
"""
if mode == "none" or mode == "None":
mode = None
self.HAS_MPI = self.test_import_MPI(mode, flag_for_low_level)
# Pick the mode
self.pick_mode = self.pick_mode()
if mode == None:
if self.pick_mode == "mpi" and self.HAS_MPI == True:
# THIS IS TO BE RUN IN MPI
self.mode = "mpi"
else:
self.mode = "multiprocessing"
elif mode == "mpi":
if self.HAS_MPI == True:
# THIS IS EXPLICITILY CHOSEN TO BE RUN IN MPI AND CAN WORK WITH MPI
self.mode = "mpi"
else:
raise Exception("mpi4py package must be available to use mpi mode")
elif mode == "multiprocessing":
self.mode = "multiprocessing"
elif mode == "Serial" or mode == "serial":
self.mode = "serial"
else:
# Default setting will be multiprocessing
self.mode = "multiprocessing"
# Start MPI MODE if applicable
if self.mode == "mpi":
self.parallel_obj = self.start(self.mode)
else:
self.parallel_obj = None
if self.mode == "serial":
self.num_procs = 1
elif num_procs == None:
self.num_procs = self.compute_nodes()
elif num_procs >= 1:
self.num_procs = num_procs
else:
self.num_procs = self.compute_nodes()
def test_import_MPI(self, mode, flag_for_low_level=False):
"""
This tests for the ability of importing the MPI sublibrary from mpi4py.
This import is problematic when run inside a program which was already mpi parallelized (ie a program run inside an mpi program)
- for some reason from mpi4py import MPI is problematic in this sub-program structuring.
To prevent these errors we do a quick check outside the class with a Try statement to import mpi4py
- if it can't do the import mpi4py than the API isn't installed and we can't run MPI, so we won't even attempt from mpi4py import MPI
it then checks if the mode has been already establish or if there is a low level flag.
If the user explicitly or implicitly asks for mpi (ie mode=None or mode="mpi") without flags and mpi4py is installed, then we will
run the from mpi4py import MPI check. if it passes then we will return a True and run mpi mode; if not we return False and run multiprocess
Inputs:
:param str mode: the multiprocess mode to be used, ie) serial, multiprocessing, mpi, or None:
if None then we will try to pick a possible multiprocessing choice. This should only be used for
top level coding. It is best practice to specify which multiprocessing choice to use.
if you have smaller programs used by a larger program, with both mpi enabled there will be problems, so specify multiprocessing is important.
:param int num_procs: the number of processors or nodes that will be used. If None than we will use all available nodes/processors
This will be overriden and fixed to a single processor if mode==serial
:param bol flag_for_low_level: this will override mode and number of processors and set it to a multiprocess as serial. This is useful because
a low-level program in mpi mode referenced by a top level program in mpi mode will have terrible problems. This means you can't mpi-multiprocess inside an mpi-multiprocess.
Returns:
:returns: bol bol: Returns True if MPI can be run and there aren't any flags against running mpi mode
Returns False if it cannot or should not run mpi mode.
"""
if MPI_installed == False:
# mpi4py isn't installed and we will need to multiprocess
return False
if flag_for_low_level == True:
# Flagged for low level and testing import mpi4py.MPI can be a problem
return False
if mode == "mpi" or mode == "None" or mode == None:
# This must be either mpi or None, mpi4py can be installed and it hasn't been flagged at low level
# Before executing Parallelizer with mpi4py (which override python raise Exceptions)
# We must check that it is being run with the "-m mpi4py" runpy flag
# Although this is lower priority over mpi4py version (as mpi4py.__versions__ less than 2.1.0 do not offer the -m feature)
# This should get checked before loading the mpi4py api
sys_modules = sys.modules
if "runpy" not in sys_modules.keys():
printout = "\nTo run in mpi mode you must run with -m flag. ie) mpirun -n $NTASKS python -m mpi4py run_gypsum_dl.py\n"
print(printout)
return False
try:
import mpi4py
from mpi4py import MPI
mpi4py_version = mpi4py.__version__
mpi4py_version = [int(x) for x in mpi4py_version.split(".")]
if mpi4py_version[0] == 2:
if mpi4py_version[1] < 1:
print(
"\nmpi4py version 2.1.0 or higher is required. Use the 'python -m mpi4py' flag to run in mpi mode.\nPlease update mpi4py to a newer version, or switch job_manager to multiprocessing or serial.\n"
)
return False
elif mpi4py_version[0] < 2:
print(
"\nmpi4py version 2.1.0 or higher is required. Use the 'python -m mpi4py' flag to run in mpi mode.\nPlease update mpi4py to a newer version, or switch job_manager to multiprocessing or serial.\n"
)
return False
return True
except:
return False
return False
def start(self, mode=None):
"""
One must call this method before `run()` in order to configure MPI parallelization
This creates the object for parallizing in a given mode.
mode=None can be used at a top level program, but if using a program enabled
with this multiprocess, referenced by a top level program, make sure the mode
is explicitly chosen.
Inputs:
:param str mode: the multiprocess mode to be used, ie) serial, multiprocessing, mpi, or None:
if None then we will try to pick a possible multiprocessing choice. This should only be used for
top level coding. It is best practice to specify which multiprocessing choice to use.
if you have smaller programs used by a larger program, with both mpi enabled there will be problems, so specify multiprocessing is important.
Returns:
:returns: class parallel_obj: This is the obstantiated object of the class of parallizations.
ie) if self.mode=='mpi' self.parallel_obj will be an instance of the mpi class
This style of retained parallel_obj to be used later is important because this is the object which controls the work nodes and maintains the mpi universe
self.parallel_obj will be set to None for simpler parallization methods like serial
"""
if mode == None:
mode = self.mode
if mode == "mpi":
if self.HAS_MPI == True:
# THIS IS EXPLICITILY CHOSEN TO BE RUN IN MPI AND CAN WORK WITH MPI
ParallelMPI_obj = ParallelMPI()
ParallelMPI_obj.start()
return ParallelMPI_obj
else:
raise Exception("mpi4py package must be available to use mpi mode")
else:
return None
def end(self, mode=None):
"""
Call this method before exit to terminate MPI workers
Inputs:
:param str mode: the multiprocess mode to be used, ie) serial, multiprocessing, mpi, or None:
if None then we will try to pick a possible multiprocessing choice. This should only be used for
top level coding. It is best practice to specify which multiprocessing choice to use.
if you have smaller programs used by a larger program, with both mpi enabled there will be problems, so specify multiprocessing is important.
"""
if mode == None:
mode = self.mode
if mode == "mpi":
if self.HAS_MPI == True and self.parallel_obj != None:
# THIS IS EXPLICITILY CHOSEN TO BE RUN IN MPI AND CAN WORK WITH MPI
self.parallel_obj.end()
else:
raise Exception("mpi4py package must be available to use mpi mode")
def run(self, args, func, num_procs=None, mode=None):
"""
Run a task in parallel across the system.
Mode can be one of 'mpi', 'multiprocessing' or 'none' (serial). If it is not
set, the best value will be determined automatically.
By default, this method will use the full resources of the system. However,
if the mode is set to 'multiprocessing', num_procs can control the number
of threads initialized when it is set to a nonzero value.
Example: If one wants to multiprocess function def foo(x,y) which takes 2 ints and one wants to test all permutations of x and y between 0 and 2:
args = [(0,0),(1,0),(2,0),(0,1),(1,1),(2,1),(0,2),(1,2),(2,2)]
func = foo The namespace of foo
Inputs:
:param python_obj func: This is the object of the function which will be used.
:param list args: a list of lists/tuples, each sublist/tuple must contain all information required by the function for a single object which will be multiprocessed
:param int num_procs: (Primarily for Developers) the number of processors or nodes that will be used. If None than we will use all available nodes/processors
This will be overriden and fixed to a single processor if mode==serial
:param str mode: (Primarily for Developers) the multiprocess mode to be used, ie) serial, multiprocessing, mpi, or None:
if None then we will try to pick a possible multiprocessing choice. This should only be used for
top level coding. It is best practice to specify which multiprocessing choice to use.
if you have smaller programs used by a larger program, with both mpi enabled there will be problems, so specify multiprocessing is important.
BEST TO LEAVE THIS BLANK
Returns:
:returns: list results: A list containing all the results from the multiprocess
"""
# determine the mode
if mode == None:
mode = self.mode
else:
if self.mode != mode:
if mode != "mpi" and mode != "serial" and mode != "multiprocessing":
printout = (
"Overriding function with a multiprocess mode which doesn't match: "
+ mode
)
raise Exception(printout)
if mode == "mpi":
printout = (
"Overriding multiprocess can't go from non-mpi to mpi mode"
)
raise Exception(printout)
if num_procs == None:
num_procs = self.num_procs
if num_procs != self.num_procs:
if mode == "serial":
printout = "Can't override num_procs in serial mode"
raise Exception(printout)
if mode != self.mode:
printout = "changing mode from {} to {} for development purpose".format(
mode, self.mode
)
print(printout)
# compute
if mode == "mpi":
if not self.HAS_MPI:
raise Exception("mpi4py package must be available to use mpi mode")
return self.parallel_obj.run(func, args)
elif mode == "multiprocessing":
return MultiThreading(args, num_procs, func)
else:
# serial is running the ParallelThreading with num_procs=1
return MultiThreading(args, 1, func)
def pick_mode(self):
"""
Determines the parallelization cababilities of the system and returns one
of the following modes depending on the configuration:
Returns:
:returns: str mode: the mode which is to be used 'mpi', 'multiprocessing', 'serial'
"""
# check if mpi4py is loaded and we have more than one processor in our MPI world
if self.HAS_MPI:
try:
if mpi4py.MPI.COMM_WORLD.Get_size() > 1:
return "mpi"
except:
return "multiprocessing"
else:
return "multiprocessing"
# # check if we could utilize more than one processor
# if multiprocessing.cpu_count() > 1:
# return 'multiprocessing'
# default to multiprocessing
return "multiprocessing"
def return_mode(self):
"""
Returns the mode chosen for the parallelization cababilities of the system and returns one
of the following modes depending on the configuration:
:param str mode: the multiprocess mode to be used, ie) serial, multiprocessing, mpi, or None:
if None then we will try to pick a possible multiprocessing choice. This should only be used for
top level coding. It is best practice to specify which multiprocessing choice to use.
if you have smaller programs used by a larger program, with both mpi enabled there will be problems, so specify multiprocessing is important.
BEST TO LEAVE THIS BLANK
Returns:
:returns: str mode: the mode which is to be used 'mpi', 'multiprocessing', 'serial'
"""
return self.mode
def compute_nodes(self, mode=None):
"""
Computes the number of "compute nodes" according to the selected mode.
For mpi, this is the universe size
For multiprocessing this is the number of available cores
For serial, this value is 1
Returns:
:returns: int num_procs: the number of nodes/processors which is to be used
"""
if mode is None:
mode = self.mode
if mode == "mpi":
if not self.HAS_MPI:
raise Exception("mpi4py package must be available to use mpi mode")
return mpi4py.MPI.COMM_WORLD.Get_size()
elif mode == "multiprocessing":
return multiprocessing.cpu_count()
else:
return 1
def return_node(self):
"""
Returns the number of "compute nodes" according to the selected mode.
For mpi, this is the universe size
For multiprocessing this is the number of available cores
For serial, this value is 1
Returns:
:returns: int num_procs: the number of nodes/processors which is to be used
"""
return self.num_procs
class ParallelMPI(object):
"""
Utility code for running tasks in parallel across an MPI cluster.
"""
def __init__(self):
"""
Default num_procs is all the processesors possible
"""
self.COMM = mpi4py.MPI.COMM_WORLD
self.Empty_object = Empty_obj()
def start(self):
"""
Call this method at the beginning of program execution to put non-root processors
into worker mode.
"""
rank = self.COMM.Get_rank()
if rank == 0:
return
else:
worker = self._worker()
def end(self):
"""
Call this method to terminate worker processes
"""
self.COMM.bcast(None, root=0)
def _worker(self):
"""
Worker processors wait in this function to receive new jobs
"""
while True:
# receive function for new job
func = self.COMM.bcast(None, root=0)
# kill signal
if func is None:
exit(0)
# receive arguments
args_chunk = self.COMM.scatter([], root=0)
if type(args_chunk[0]) == type(
self.Empty_object
): # or args_chunk[0] == [[self.Empty_object]]:
result_chunk = [[self.Empty_object]]
result_chunk = self.COMM.gather(result_chunk, root=0)
else:
# perform the calculation and send results
result_chunk = [
func(*arg)
for arg in args_chunk
if type(arg[0]) != type(self.Empty_object)
]
result_chunk = self.COMM.gather(result_chunk, root=0)
def handle_undersized_jobs(self, arr, n):
if len(arr) > n:
printout = "the length of the package is bigger than the length of the number of nodes!"
print(printout)
raise Exception(printout)
filler_slot = [[self.Empty_object]]
while len(arr) < n:
arr.append(filler_slot)
if len(arr) == n:
break
return arr
def _split(self, arr, n):
"""
Takes an array of items and splits it into n "equally" sized
chunks that can be provided to a worker cluster.
"""
s = len(arr) // n
remainder = len(arr) - int(s) * int(n)
chuck_list = []
temp = []
counter = 0
for x in range(0, len(arr)):
# add 1 per group until remainder is removed
if remainder != 0:
r = 1
if counter == s + 1:
remainder = remainder - 1
else:
r = 0
if counter == s + r:
chuck_list.append(temp)
temp = []
counter = 1
else:
counter += 1
temp.append(list(arr[x]))
if x == len(arr) - 1:
chuck_list.append(temp)
if len(chuck_list) != n:
chuck_list = self.handle_undersized_jobs(chuck_list, n)
return chuck_list
def _join(self, arr):
"""
Joins a "list of lists" that was previously split by _split().
Returns a single list.
"""
arr = tuple(arr)
arr = [x for x in arr if type(x) != type(self.Empty_object)]
arr = [a for sub in arr for a in sub]
arr = [x for x in arr if type(x) != type(self.Empty_object)]
return arr
def check_and_format_args(self, args):
# Make sure args is a list of lists
if type(args) != list and type(args) != tuple:
printout = "args must be a list of lists"
print(printout)
raise Exception(printout)
item_type = type(args[0])
for i in range(0, len(args)):
if type(args[i]) == item_type:
continue
else:
printout = "all items within args must be the same type and must be either a list or tuple"
print(printout)
raise Exception(printout)
if item_type == list:
return args
elif item_type == tuple:
args = [list(x) for x in args]
return args
else:
printout = "all items within args must be either a list or tuple"
print(printout)
raise Exception(printout)
def run(self, func, args):
"""
Run a function in parallel across the current MPI cluster.
* func is a pure function of type (A)->(B)
* args is a list of type list(A)
This method batches the computation across the MPI cluster and returns
the result of type list(B) where result[i] = func(args[i]).
Important note: func must exist in the namespace at initialization.
"""
num_of_args_start = len(args)
if len(args) == 0:
return []
args = self.check_and_format_args(args)
size = self.COMM.Get_size()
# broadcast function to worker processors
self.COMM.bcast(func, root=0)
# chunkify the argument list
args_chunk = self._split(args, size)
# scatter argument chunks to workers
args_chunk = self.COMM.scatter(args_chunk, root=0)
if type(args_chunk) != list:
raise Exception("args_chunk needs to be a list")
# perform the calculation and get results
result_chunk = [func(*arg) for arg in args_chunk]
sys.stdout.flush()
result_chunk = self.COMM.gather(result_chunk, root=0)
if type(result_chunk) != list:
raise Exception("result_chunk needs to be a list")
# group results
results = self._join(result_chunk)
if len(results) != num_of_args_start:
results = [x for x in results if type(x) != type(self.Empty_object)]
results = flatten_list(results)
if type(results) != list:
raise Exception("results needs to be a list")
results = [x for x in results if type(x) != type(self.Empty_object)]
sys.stdout.flush()
return results
#
class Empty_obj(object):
"""
Create a unique Empty Object to hand to empty processors
"""
pass
#
"""
Run commands on multiple processors in python.
Adapted from examples on https://docs.python.org/2/library/multiprocessing.html
"""
def MultiThreading(inputs, num_procs, task_name):
"""Initialize this object.
Args:
inputs ([data]): A list of data. Each datum contains the details to
run a single job on a single processor.
num_procs (int): The number of processors to use.
task_class_name (class): The class that governs what to do for each
job on each processor.
"""
results = []
# If there are no inputs, just return an empty list.
if len(inputs) == 0:
return results
inputs = check_and_format_inputs_to_list_of_tuples(inputs)
num_procs = count_processors(len(inputs), num_procs)
tasks = []
for index, item in enumerate(inputs):
if not isinstance(item, tuple):
item = (item,)
task = (index, (task_name, item))
tasks.append(task)
if num_procs == 1:
for item in tasks:
job, args = item[1]
output = job(*args)
results.append(output)
else:
results = start_processes(tasks, num_procs)
return results
###
# Worker function
###
def worker(input, output):
for seq, job in iter(input.get, "STOP"):
func, args = job
result = func(*args)
ret_val = (seq, result)
output.put(ret_val)
def check_and_format_inputs_to_list_of_tuples(args):
# Make sure args is a list of tuples
if type(args) != list and type(args) != tuple:
printout = "args must be a list of tuples"
print(printout)
raise Exception(printout)
item_type = type(args[0])
for i in range(0, len(args)):
if type(args[i]) == item_type:
continue
else:
printout = "all items within args must be the same type and must be either a list or tuple"
print(printout)
raise Exception(printout)
if item_type == tuple:
return args
elif item_type == list:
args = [tuple(x) for x in args]
return args
else:
printout = "all items within args must be either a list or tuple"
print(printout)
raise Exception(printout)
def count_processors(num_inputs, num_procs):
"""
Checks processors available and returns a safe number of them to
utilize.
:param int num_inputs: The number of inputs.
:param int num_procs: The number of desired processors.
:returns: The number of processors to use.
"""
# first, if num_procs <= 0, determine the number of processors to
# use programatically
if num_procs <= 0:
num_procs = multiprocessing.cpu_count()
# reduce the number of processors if too many have been specified
if num_inputs < num_procs:
num_procs = num_inputs
return num_procs
def start_processes(inputs, num_procs):
"""
Creates a queue of inputs and outputs
"""
# Create queues
task_queue = multiprocessing.Queue()
done_queue = multiprocessing.Queue()
# Submit tasks
for item in inputs:
task_queue.put(item)
# Start worker processes
for i in range(num_procs):
multiprocessing.Process(target=worker, args=(task_queue, done_queue)).start()
# Get and print results
results = []
for i in range(len(inputs)):
results.append(done_queue.get())
# Tell child processes to stop
for i in range(num_procs):
task_queue.put("STOP")
results.sort(key=lambda tup: tup[0])
return [item[1] for item in map(list, results)]
###
# Helper functions
###
def flatten_list(tier_list):
"""
Given a list of lists, this returns a flat list of all items.
:params list tier_list: A 2D list.
:returns: A flat list of all items.
"""
if tier_list is None:
return []
already_flattened = False
for item in tier_list:
if type(item) != list:
already_flattened = True
if already_flattened == True:
return tier_list
flat_list = [item for sublist in tier_list for item in sublist]
return flat_list
def strip_none(none_list):
"""
Given a list that might contain None items, this returns a list with no
None items.
:params list none_list: A list that may contain None items.
:returns: A list stripped of None items.
"""
if none_list is None:
return []
results = [x for x in none_list if x != None]
return results
| 30,734 | 36.944444 | 223 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/MyMol.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module contains classes and functions for processing individual molecules
(variants). All variants of the same input molecule are grouped together in
the same MolContainer.MolContainer object. Each MyMol.MyMol is also associated
with conformers described here (3D coordinate sets).
So just to clarify: MolContainer.MolContainer > MyMol.MyMol >
MyMol.MyConformer
"""
import __future__
import sys
import copy
import operator
import gypsum_dl.Utils as Utils
import gypsum_dl.MolObjectHandling as MOH
# Disable the unnecessary RDKit warnings
from rdkit import RDLogger
RDLogger.DisableLog("rdApp.*")
try:
import rdkit
from rdkit.Chem import AllChem
from rdkit import Chem
from rdkit.Chem.rdchem import BondStereo
except:
Utils.exception("You need to install rdkit and its dependencies.")
try:
from gypsum_dl.molvs import standardize_smiles as ssmiles
except:
Utils.exception("You need to install molvs and its dependencies.")
class MyMol:
"""
A class that wraps around a rdkit.Mol object. Includes additional data and
functions.
"""
def __init__(self, starter, name=""):
"""Initialize the MyMol object.
:param starter: The object (smiles or rdkit.Mol) on which to build this
class.
:type starter: str or rdkit.Mol
:param name: An optional string, the name of this molecule. Defaults to "".
:param name: str, optional
"""
if isinstance(starter, str):
# It's a SMILES string.
self.rdkit_mol = ""
self.can_smi = ""
smiles = starter
else:
# So it's an rdkit mol object.
self.rdkit_mol = (
starter # No need to regenerate this, since already provided.
)
# Get the smiles too from the rdkit mol object.
try:
smiles = Chem.MolToSmiles(
self.rdkit_mol, isomericSmiles=True, canonical=True
)
# In this case you know it's cannonical.
self.can_smi = smiles
except:
# Sometimes this conversion just can't happen. Happened once
# with this beast, for example:
# CC(=O)NC1=CC(=C=[N+]([O-])O)C=C1O
self.can_smi = False
id_to_print = name if name != "" else str(starter)
Utils.log(
"\tERROR: Could not generate one of the structures "
+ "for ("
+ id_to_print
+ ")."
)
self.can_smi_noh = ""
self.orig_smi = smiles
# Default assumption is that they are the same.
self.orig_smi_deslt = smiles
self.name = name
self.conformers = []
self.nonaro_ring_atom_idx = ""
self.chiral_cntrs_only_assigned = ""
self.chiral_cntrs_include_unasignd = ""
self.bizarre_substruct = ""
self.enrgy = {} # different energies for different conformers.
self.minimized_enrgy = {}
self.contnr_idx = ""
self.frgs = ""
self.stdrd_smiles = ""
self.mol_props = {}
self.idxs_low_energy_confs_no_opt = {}
self.idxs_of_confs_to_min = set([])
self.genealogy = [] # Keep track of how the molecule came to be.
# Makes the molecule if a smiles was provided. Sanitizes the molecule
# regardless.
self.make_mol_frm_smiles_sanitze()
def standardize_smiles(self):
"""Standardize the smiles string if you can."""
if self.stdrd_smiles != "":
return self.stdrd_smiles
try:
self.stdrd_smiles = ssmiles(self.smiles())
except:
Utils.log("\tCould not standardize " + self.smiles(True) + ". Skipping.")
self.stdrd_smiles = self.smiles()
return self.stdrd_smiles
def __hash__(self):
"""Allows you to compare MyMol.MyMol objects.
:return: The hashed canonical smiles.
:rtype: str
"""
can_smi = self.smiles()
# So it hashes based on the cannonical smiles.
return hash(can_smi)
def __eq__(self, other):
"""Allows you to compare MyMol.MyMol objects.
:param other: The other molecule.
:type other: MyMol.MyMol
:return: Whether the other molecule is the same as this one.
:rtype: bool
"""
if other is None:
return False
else:
return self.__hash__() == other.__hash__()
def __ne__(self, other):
"""Allows you to compare MyMol.MyMol objects.
:param other: The other molecule.
:type other: MyMol.MyMol
:return: Whether the other molecule is different from this one.
:rtype: bool
"""
return not self.__eq__(other)
def __lt__(self, other):
"""Is this MyMol less than another one? Gypsum-DL often sorts
molecules by sorting tuples of the form (energy, MyMol). On rare
occasions, the energies are identical, and the sorting algorithm
attempts to compare MyMol directly.
:param other: The other molecule.
:type other: MyMol.MyMol
:return: True or False, if less than or not.
:rtype: boolean
"""
return self.__hash__() < other.__hash__()
def __le__(self, other):
"""Is this MyMol less than or equal to another one? Gypsum-DL often
sorts molecules by sorting tuples of the form (energy, MyMol). On rare
occasions, the energies are identical, and the sorting algorithm
attempts to compare MyMol directly.
:param other: The other molecule.
:type other: MyMol.MyMol
:return: True or False, if less than or equal to, or not.
:rtype: boolean
"""
return self.__hash__() <= other.__hash__()
def __gt__(self, other):
"""Is this MyMol greater than another one? Gypsum-DL often sorts
molecules by sorting tuples of the form (energy, MyMol). On rare
occasions, the energies are identical, and the sorting algorithm
attempts to compare MyMol directly.
:param other: The other molecule.
:type other: MyMol.MyMol
:return: True or False, if greater than or not.
:rtype: boolean
"""
return self.__hash__() > other.__hash__()
def __ge__(self, other):
"""Is this MyMol greater than or equal to another one? Gypsum-DL often
sorts molecules by sorting tuples of the form (energy, MyMol). On rare
occasions, the energies are identical, and the sorting algorithm
attempts to compare MyMol directly.
:param other: The other molecule.
:type other: MyMol.MyMol
:return: True or False, if greater than or equal to, or not.
:rtype: boolean
"""
return self.__hash__() >= other.__hash__()
def make_mol_frm_smiles_sanitze(self):
"""Construct a rdkit.mol for this object, in case you only received
the smiles. Also, sanitize the molecule regardless.
:return: Returns the rdkit.mol object, though it's also stored in
self.rdkit_mol.
:rtype: rdkit.mol object.
"""
# If given a SMILES string.
if self.rdkit_mol == "":
try:
# sanitize = False makes it respect double-bond stereochemistry
m = Chem.MolFromSmiles(self.orig_smi_deslt, sanitize=False)
except:
m = None
else: # If given a RDKit Mol Obj
m = self.rdkit_mol
if m is not None:
# Sanitize and hopefully correct errors in the smiles string such
# as incorrect nitrogen charges.
m = MOH.check_sanitization(m)
self.rdkit_mol = m
return m
def make_first_3d_conf_no_min(self):
"""Makes the associated rdkit.mol object 3D by adding the first
conformer. This also adds hydrogen atoms to the associated rdkit.mol
object. Note that it does not perform a minimization, so it is not
too expensive."""
# Set the first 3D conformer
if len(self.conformers) > 0:
# It's already been done.
return
# Add hydrogens. This adds explicit hydrogens, while respecting
# Dimorphite-DL protonation states.
self.rdkit_mol = MOH.try_reprotanation(self.rdkit_mol)
# Add a single conformer. RMSD cutoff very small so all conformers
# will be accepted. And not minimizing (False).
self.add_conformers(1, 1e60, False)
def smiles(self, noh=False):
"""Get the desalted, canonical smiles string associated with this
object. (Not the input smiles!)
:param noh: Whether or not hydrogen atoms should be included in the
canonical smiles string., defaults to False
:param noh: bool, optional
:return: The canonical smiles string, or None if it cannot be
determined.
:rtype: str or None
"""
# See if it's already been calculated.
if noh == False:
# They want the hydrogen atoms.
if self.can_smi != "":
# Return previously determined canonical SMILES.
return self.can_smi
else:
# Need to determine canonical SMILES.
try:
can_smi = Chem.MolToSmiles(
self.rdkit_mol, isomericSmiles=True, canonical=True
)
except:
# Sometimes this conversion just can't happen. Happened
# once with this beast, for example:
# CC(=O)NC1=CC(=C=[N+]([O-])O)C=C1O
Utils.log(
"Warning: Couldn't put "
+ self.orig_smi
+ " ("
+ self.name
+ ") in canonical form. Got this error: "
+ str(sys.exc_info()[0])
+ ". This molecule will be "
+ "discarded."
)
self.can_smi = None
return None
self.can_smi = can_smi
return can_smi
else:
# They don't want the hydrogen atoms.
if self.can_smi_noh != "":
# Return previously determined string.
return self.can_smi_noh
# So remove hydrogens. Note that this assumes you will have called
# this function previously with noh = False
amol = copy.copy(self.rdkit_mol)
amol = MOH.try_deprotanation(amol)
self.can_smi_noh = Chem.MolToSmiles(
amol, isomericSmiles=True, canonical=True
)
return self.can_smi_noh
def get_idxs_of_nonaro_rng_atms(self):
"""Identifies which rings in a given molecule are nonaromatic, if any.
:return: A [[int, int, int]]. A list of lists, where each inner list is
a list of the atom indecies of the members of a non-aromatic ring.
Also saved to self.nonaro_ring_atom_idx.
:rtype: list
"""
if self.nonaro_ring_atom_idx != "":
# Already determined...
return self.nonaro_ring_atom_idx
# There are no rings if the molecule is None.
if self.rdkit_mol is None:
return []
# Get the number of symmetric smallest set of rings
ssr = Chem.GetSymmSSSR(self.rdkit_mol)
# Get the rings
ring_indecies = [list(ssr[i]) for i in range(len(ssr))]
# Are the atoms in any of those rings nonaromatic?
nonaro_rngs = []
for rng_indx_set in ring_indecies:
for atm_idx in rng_indx_set:
if self.rdkit_mol.GetAtomWithIdx(atm_idx).GetIsAromatic() == False:
# One of the ring atoms is not aromatic! Let's keep it.
nonaro_rngs.append(rng_indx_set)
break
self.nonaro_ring_atom_idx = nonaro_rngs
return nonaro_rngs
def chiral_cntrs_w_unasignd(self):
"""Get the chiral centers that haven't been assigned.
:return: The chiral centers. Also saved to
self.chiral_cntrs_include_unasignd. Looks like [(10, '?')]
:rtype: list
"""
# No chiral centers if the molecule is None.
if self.rdkit_mol is None:
return []
if self.chiral_cntrs_include_unasignd != "":
# Already been determined...
return self.chiral_cntrs_include_unasignd
# Get the chiral centers that are not defined.
ccs = Chem.FindMolChiralCenters(self.rdkit_mol, includeUnassigned=True)
self.chiral_cntrs_include_unasignd = ccs
return ccs
def chiral_cntrs_only_asignd(self):
"""Get the chiral centers that have been assigned.
:return: The chiral centers. Also saved to self.chiral_cntrs_only_assigned.
:rtype: list
"""
if self.chiral_cntrs_only_assigned != "":
return self.chiral_cntrs_only_assigned
if self.rdkit_mol is None:
return []
ccs = Chem.FindMolChiralCenters(self.rdkit_mol, includeUnassigned=False)
self.chiral_cntrs_only_assigned = ccs
return ccs
def get_double_bonds_without_stereochemistry(self):
"""Get the double bonds that don't have specified stereochemistry.
:return: The unasignd double bonds (indexes). Looks like this:
[2, 4, 7]
:rtype: list
"""
if self.rdkit_mol is None:
return []
unasignd = []
for b in self.rdkit_mol.GetBonds():
if b.GetBondTypeAsDouble() == 2 and b.GetStereo() is BondStereo.STEREONONE:
unasignd.append(b.GetIdx())
return unasignd
def remove_bizarre_substruc(self):
"""Removes molecules with improbable substuctures, likely generated
from the tautomerization process. Used to find artifacts.
:return: Boolean, whether or not there are impossible substructures.
Also saves to self.bizarre_substruct.
:rtype: bool
"""
if self.bizarre_substruct != "":
# Already been determined.
return self.bizarre_substruct
if self.rdkit_mol is None:
# It is bizarre to have a molecule with no atoms in it.
return True
# These are substrutures that can't be easily corrected using
# fix_common_errors() below.
# , "[C+]", "[C-]", "[c+]", "[c-]", "[n-]", "[N-]"] # ,
# "[*@@H]1(~[*][*]~2)~[*]~[*]~[*@@H]2~[*]~[*]~1",
# "[*@@H]1~2~*~*~[*@@H](~*~*2)~*1",
# "[*@@H]1~2~*~*~*~[*@@H](~*~*2)~*1",
# "[*@@H]1~2~*~*~*~*~[*@@H](~*~*2)~*1",
# "[*@@H]1~2~*~[*@@H](~*~*2)~*1", "[*@@H]~1~2~*~*~*~[*@H]1O2",
# "[*@@H]~1~2~*~*~*~*~[*@H]1O2"]
# Note that C(O)=N, C and N mean they are aliphatic. Does not match
# c(O)n, when aromatic. So this form is acceptable if in aromatic
# structure.
prohibited_substructures = ["O(=*)-*"] # , "C(O)=N"]
prohibited_substructures.append(
"C(=[CH2])[OH]"
) # Enol forms with terminal alkenes are unlikely.
prohibited_substructures.append(
"C(=[CH2])[O-]"
) # Enol forms with terminal alkenes are unlikely.
prohibited_substructures.append(
"C=C([OH])[OH]"
) # A geminal vinyl diol is not a tautomer of a carboxylate group.
prohibited_substructures.append(
"C=C([O-])[OH]"
) # A geminal vinyl diol is not a tautomer of a carboxylate group.
prohibited_substructures.append(
"C=C([O-])[O-]"
) # A geminal vinyl diol is not a tautomer of a carboxylate group.
prohibited_substructures.append("[C-]") # No carbanions.
prohibited_substructures.append("[c-]") # No carbanions.
for s in prohibited_substructures:
# First just match strings... could be faster, but not 100%
# accurate.
if s in self.orig_smi:
Utils.log("\tDetected unusual substructure: " + s)
self.bizarre_substruct = True
return True
if s in self.orig_smi_deslt:
Utils.log("\tDetected unusual substructure: " + s)
self.bizarre_substruct = True
return True
if s in self.can_smi:
Utils.log("\tDetected unusual substructure: " + s)
self.bizarre_substruct = True
return True
# Now do actual substructure matching
for s in prohibited_substructures:
pattrn = Chem.MolFromSmarts(s)
if self.rdkit_mol.HasSubstructMatch(pattrn):
# Utils.log("\tRemoving a molecule because it has an odd
# substructure: " + s)
Utils.log("\tDetected unusual substructure: " + s)
self.bizarre_substruct = True
return True
# Now certin patterns that are more complex.
# TODO in the future?
self.bizarre_substruct = False
return False
def get_frags_of_orig_smi(self):
"""Divide the current molecule into fragments.
:return: A list of the fragments, as rdkit.Mol objects.
:rtype: list
"""
if self.frgs != "":
# Already been determined...
return self.frgs
if not "." in self.orig_smi:
# There are no fragments. Just return this object.
self.frgs = [self]
return [self]
# Get the fragments.
frags = Chem.GetMolFrags(self.rdkit_mol, asMols=True)
self.frgs = frags
return frags
def inherit_contnr_props(self, other):
"""Copies a few key properties from a different MyMol.MyMol object to
this one.
:param other: The other MyMol.MyMol object to copy these properties to.
:type other: MyMol.MyMol
"""
# other can be a contnr or MyMol.MyMol object. These are properties
# that should be the same for every MyMol.MyMol object in this
# MolContainer.
self.contnr_idx = other.contnr_idx
self.orig_smi = other.orig_smi
self.orig_smi_deslt = other.orig_smi_deslt # initial assumption
self.name = other.name
def set_rdkit_mol_prop(self, key, val):
"""Set a molecular property.
:param key: The name of the molecular property.
:type key: str
:param val: The value of that property.
:type val: str
"""
val = str(val)
self.rdkit_mol.SetProp(key, val)
self.rdkit_mol.SetProp(key, val)
try:
self.rdkit_mol.SetProp(key, val)
except:
pass
def set_all_rdkit_mol_props(self):
"""Set all the stored molecular properties. Copies ones from the
MyMol.MyMol object to the MyMol.rdkit_mol object."""
self.set_rdkit_mol_prop("SMILES", self.smiles(True))
# self.set_rdkit_mol_prop("SOURCE_SMILES", self.orig_smi)
for prop in list(self.mol_props.keys()):
self.set_rdkit_mol_prop(prop, self.mol_props[prop])
genealogy = "\n".join(self.genealogy)
self.set_rdkit_mol_prop("Genealogy", genealogy)
self.set_rdkit_mol_prop("_Name", self.name)
def add_conformers(self, num, rmsd_cutoff=0.1, minimize=True):
"""Add conformers to this molecule.
:param num: The total number of conformers to generate, including ones
that have been generated previously.
:type num: int
:param rmsd_cutoff: Don't keep conformers that come within this rms
distance of other conformers. Defaults to 0.1
:param rmsd_cutoff: float, optional
:param minimize: Whether or not to minimize the geometry of all these
conformers. Defaults to True.
:param minimize: bool, optional
"""
# First, do you need to add new conformers? Some might have already
# been added. Just add enough to meet the requested amount.
num_new_confs = max(0, num - len(self.conformers))
for i in range(num_new_confs):
if len(self.conformers) == 0:
# For the first one, don't start from random coordinates.
new_conf = MyConformer(self)
else:
# For all subsequent ones, do start from random coordinates.
new_conf = MyConformer(self, None, False, True)
if new_conf.mol is not False:
self.conformers.append(new_conf)
# Are the current ones minimized if necessary?
if minimize == True:
for conf in self.conformers:
conf.minimize() # Won't reminimize if it's already been done.
# Automatically sort by the energy.
self.conformers.sort(key=operator.attrgetter("energy"))
# Remove ones that are very structurally similar.
self.eliminate_structurally_similar_conformers(rmsd_cutoff)
def eliminate_structurally_similar_conformers(self, rmsd_cutoff=0.1):
"""Eliminates conformers that are very geometrically similar.
:param rmsd_cutoff: The RMSD cutoff to use. Defaults to 0.1
:param rmsd_cutoff: float, optional
"""
# Eliminate redundant ones.
for i1 in range(0, len(self.conformers) - 1):
if self.conformers[i1] is not None:
for i2 in range(i1 + 1, len(self.conformers)):
if self.conformers[i2] is not None:
# Align them.
self.conformers[i2] = self.conformers[i1].align_to_me(
self.conformers[i2]
)
# Calculate the RMSD.
rmsd = self.conformers[i1].rmsd_to_me(self.conformers[i2])
# Replace the second one with None if it's too similar
# to the first.
if rmsd <= rmsd_cutoff:
self.conformers[i2] = None
# Remove all the None entries.
while None in self.conformers:
self.conformers.remove(None)
# Those that remains are only the distinct conformers.
def count_hyd_bnd_to_carb(self):
"""Count the number of Hydrogens bound to carbons."""
if self.rdkit_mol is None:
# Doesn't have any atoms at all.
return 0
total_hydrogens_counted = 0
for atom in self.rdkit_mol.GetAtoms():
if atom.GetSymbol() == "C":
total_hydrogens_counted = total_hydrogens_counted + atom.GetTotalNumHs(
includeNeighbors=True
)
return total_hydrogens_counted
def load_conformers_into_rdkit_mol(self):
"""Load the conformers stored as MyConformers objects (in
self.conformers) into the rdkit Mol object."""
self.rdkit_mol.RemoveAllConformers()
for conformer in self.conformers:
self.rdkit_mol.AddConformer(conformer.conformer())
class MyConformer:
"""A wrapper around a rdkit Conformer object. Allows me to associate extra
values with conformers. These are 3D coordinate sets for a given
MyMol.MyMol object (different molecule conformations).
"""
def __init__(
self, mol, conformer=None, second_embed=False, use_random_coordinates=False
):
"""Create a MyConformer objects.
:param mol: The MyMol.MyMol associated with this conformer.
:type mol: MyMol.MyMol
:param conformer: An optional variable specifying the conformer to use.
If not specified, it will create a new conformer. Defaults to None.
:type conformer: rdkit.Conformer, optional
:param second_embed: Whether to try to generate 3D coordinates using an
older algorithm if the better (default) algorithm fails. This can add
run time, but sometimes converts certain molecules that would
otherwise fail. Defaults to False.
:type second_embed: bool, optional
:param use_random_coordinates: The first conformer should not start
from random coordinates, but rather the eigenvalues-based
coordinates rdkit defaults to. But Gypsum-DL generates subsequent
conformers to try to consider alternate geometries. So they should
start from random coordinates. Defaults to False.
:type use_random_coordinates: bool, optional
"""
# Save some values to the object.
self.mol = copy.deepcopy(mol.rdkit_mol)
self.smiles = mol.smiles()
# Remove any previous conformers.
self.mol.RemoveAllConformers()
if conformer is None:
# The user is providing no conformer. So we must generate it.
# Note that I have confirmed that the below respects chirality.
# params is a list of ETKDGv2 parameters generated by this command
# Description of these parameters can be found at
# help(AllChem.EmbedMolecule)
try:
# Try to use ETKDGv2, but it is only present in the python 3.6
# version of RDKit.
params = AllChem.ETKDGv2()
except:
# Use the original version of ETKDG if python 2.7 RDKit. This
# may be resolved in next RDKit update so we encased this in a
# try statement.
params = AllChem.ETKDG()
# The default, but just a sanity check.
params.enforcechiral = True
# Set a max number of times it will try to calculate the 3D
# coordinates. Will save a little time.
params.maxIterations = 0 # This should be the default but lets
# set it anyway
# Also set whether to start from random coordinates.
params.useRandomCoords = use_random_coordinates
# AllChem.EmbedMolecule uses geometry to create inital molecule
# coordinates. This sometimes takes a very long time.
AllChem.EmbedMolecule(self.mol, params)
# On rare occasions, the new conformer generating algorithm fails
# because params.useRandomCoords = False. So if it fails, try
# again with True.
if self.mol.GetNumConformers() == 0 and use_random_coordinates == False:
params.useRandomCoords = True
AllChem.EmbedMolecule(self.mol, params)
# On very rare occasions, the new conformer generating algorithm
# fails. For example, COC(=O)c1cc(C)nc2c(C)cc3[nH]c4ccccc4c3c12 .
# In this case, the old one still works. So if no coordinates are
# assigned, try that one. Parameters must have second_embed set to
# True for this to happen.
if second_embed == True and self.mol.GetNumConformers() == 0:
AllChem.EmbedMolecule(self.mol, useRandomCoords=use_random_coordinates)
# On rare occasions, both methods fail. For example,
# O=c1cccc2[C@H]3C[NH2+]C[C@@H](C3)Cn21 Another example:
# COc1cccc2c1[C@H](CO)[N@H+]1[C@@H](C#N)[C@@H]3C[C@@H](C(=O)[O-])[C@H]([C@H]1C2)[N@H+]3C
if self.mol.GetNumConformers() == 0:
self.mol = False
else:
# The user has provided a conformer. Just add it.
conformer.SetId(0)
self.mol.AddConformer(conformer, assignId=True)
# Calculate some energies, other housekeeping.
if self.mol is not False:
try:
ff = AllChem.UFFGetMoleculeForceField(self.mol)
self.energy = ff.CalcEnergy()
except:
Utils.log(
"Warning: Could not calculate energy for molecule "
+ Chem.MolToSmiles(self.mol)
)
# Example of smiles that cause problem here without try...catch:
# NC1=NC2=C(N[C@@H]3[C@H](N2)O[C@@H](COP(O)(O)=O)C2=C3S[Mo](S)(=O)(=O)S2)C(=O)N1
self.energy = 9999
self.minimized = False
self.ids_hvy_atms = [
a.GetIdx() for a in self.mol.GetAtoms() if a.GetAtomicNum() != 1
]
def conformer(self, conf=None):
"""Get or set the conformer. An optional variable can specify the
conformer to set. If not specified, this function acts as a get for
the conformer.
:param conf: The conformer to set, defaults to None
:param conf: rdkit.Conformer, optional
:return: An rdkit.Conformer object, if conf is not specified.
:rtype: rdkit.Conformer
"""
if conf is None:
return self.mol.GetConformers()[0]
else:
self.mol.RemoveAllConformers()
self.mol.AddConformer(conf)
def minimize(self):
"""Minimize (optimize) the geometry of the current conformer if it
hasn't already been optimized."""
if self.minimized == True:
# Already minimized. Don't do it again.
return
# Perform the minimization, and save the energy.
try:
ff = AllChem.UFFGetMoleculeForceField(self.mol)
ff.Minimize()
self.energy = ff.CalcEnergy()
except:
Utils.log(
"Warning: Could not calculate energy for molecule "
+ Chem.MolToSmiles(self.mol)
)
self.energy = 9999
self.minimized = True
def align_to_me(self, other_conf):
"""Align another conformer to this one.
:param other_conf: The other conformer to align.
:type other_conf: MyConformer
:return: The aligned MyConformer object.
:rtype: MyConformer
"""
# Add the conformer of the other MyConformer object.
self.mol.AddConformer(other_conf.conformer(), assignId=True)
# Align them.
AllChem.AlignMolConformers(self.mol, atomIds=self.ids_hvy_atms)
# Reset the conformer of the other MyConformer object.
last_conf = self.mol.GetConformers()[-1]
other_conf.conformer(last_conf)
# Remove the added conformer.
self.mol.RemoveConformer(last_conf.GetId())
# Return that other object.
return other_conf
def MolToMolBlock(self):
"""Prints out the first 500 letters of the molblock version of this
conformer. Good for debugging."""
mol_copy = copy.deepcopy(self.mol_copy) # Use it as a template.
mol_copy.RemoveAllConformers()
mol_copy.AddConformer(self.conformer)
Utils.log(Chem.MolToMolBlock(mol_copy)[:500])
def rmsd_to_me(self, other_conf):
"""Calculate the rms distance between this conformer and another one.
:param other_conf: The other conformer to align.
:type other_conf: MyConformer
:return: The rmsd, a float.
:rtype: float
"""
# Make a new molecule.
amol = Chem.MolFromSmiles(self.smiles, sanitize=False)
amol = MOH.check_sanitization(amol)
amol = MOH.try_reprotanation(amol)
# Add the conformer of the other MyConformer object.
amol.AddConformer(self.conformer(), assignId=True)
amol.AddConformer(other_conf.conformer(), assignId=True)
# Get the two confs.
first_conf = amol.GetConformers()[0]
last_conf = amol.GetConformers()[-1]
# Return the RMSD.
amol = MOH.try_deprotanation(amol)
rmsd = AllChem.GetConformerRMS(
amol, first_conf.GetId(), last_conf.GetId(), prealigned=True
)
return rmsd
| 32,750 | 36.048643 | 100 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/MolObjectHandling.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##### MolObjectHandling.py
import __future__
import rdkit
from rdkit import Chem
# Disable the unnecessary RDKit warnings
from rdkit import RDLogger
RDLogger.DisableLog("rdApp.*")
def check_sanitization(mol):
"""
Given a rdkit.Chem.rdchem.Mol this script will sanitize the molecule.
It will be done using a series of try/except statements so that if it fails it will return a None
rather than causing the outer script to fail.
Nitrogen Fixing step occurs here to correct for a common RDKit valence error in which Nitrogens with
with 4 bonds have the wrong formal charge by setting it to -1.
This can be a place to add additional correcting features for any discovered common sanitation failures.
Handled here so there are no problems later.
Inputs:
:param rdkit.Chem.rdchem.Mol mol: an rdkit molecule to be sanitized
Returns:
:returns: rdkit.Chem.rdchem.Mol mol: A sanitized rdkit molecule or None if it failed.
"""
if mol is None:
return None
# easiest nearly everything should get through
try:
sanitize_string = Chem.SanitizeMol(
mol,
sanitizeOps=rdkit.Chem.rdmolops.SanitizeFlags.SANITIZE_ALL,
catchErrors=True,
)
except:
return None
if sanitize_string.name == "SANITIZE_NONE":
return mol
else:
# try to fix the nitrogen (common problem that 4 bonded Nitrogens improperly lose their + charges)
mol = Nitrogen_charge_adjustment(mol)
Chem.SanitizeMol(
mol,
sanitizeOps=rdkit.Chem.rdmolops.SanitizeFlags.SANITIZE_ALL,
catchErrors=True,
)
sanitize_string = Chem.SanitizeMol(
mol,
sanitizeOps=rdkit.Chem.rdmolops.SanitizeFlags.SANITIZE_ALL,
catchErrors=True,
)
if sanitize_string.name == "SANITIZE_NONE":
return mol
# run a sanitation Filter 1 more time incase something slipped through
# ie. if there are any forms of sanition which fail ie. KEKULIZE then return None
sanitize_string = Chem.SanitizeMol(
mol,
sanitizeOps=rdkit.Chem.rdmolops.SanitizeFlags.SANITIZE_ALL,
catchErrors=True,
)
if sanitize_string.name != "SANITIZE_NONE":
return None
else:
return mol
#
def handleHs(mol, protanate_step):
"""
Given a rdkit.Chem.rdchem.Mol this script will sanitize the molecule, remove all non-explicit H's
and add back on all implicit H's. This is to control for any discrepencies in the smiles strings or presence and
absense of H's.
If it fails it will return a None rather than causing the outer script to fail. Handled here so there are no problems later.
Inputs:
:param rdkit.Chem.rdchem.Mol sanitized_deprotanated_mol: an rdkit molecule already sanitized and deprotanated.
:param bol protanate_step: True if mol needs to be protanated; False if deprotanated
-Note if Protanated, SmilesMerge takes up to 10times longer
Returns:
:returns: rdkit.Chem.rdchem.Mol mol: an rdkit molecule with H's handled (either added or removed) and sanitized.
it returns None if H's can't be added or if sanitation fails
"""
mol = check_sanitization(mol)
if mol is None:
# mol failed Sanitation
return None
mol = try_deprotanation(mol)
if mol is None:
# mol failed deprotanation
return None
if protanate_step is True:
# PROTANTION IS ON
mol = try_reprotanation(mol)
if mol is None:
# mol failed reprotanation
return None
return mol
#
def try_deprotanation(sanitized_mol):
"""
Given an already sanitize rdkit.Chem.rdchem.Mol object, we will try to deprotanate the mol of all non-explicit
Hs. If it fails it will return a None rather than causing the outer script to fail.
Inputs:
:param rdkit.Chem.rdchem.Mol mol: an rdkit molecule already sanitized.
Returns:
:returns: rdkit.Chem.rdchem.Mol mol_sanitized: an rdkit molecule with H's removed and sanitized.
it returns None if H's can't be added or if sanitation fails
"""
try:
mol = Chem.RemoveHs(sanitized_mol, sanitize=False)
except:
return None
mol_sanitized = check_sanitization(mol)
return mol_sanitized
#
def try_reprotanation(sanitized_deprotanated_mol):
"""
Given an already sanitize and deprotanate rdkit.Chem.rdchem.Mol object, we will try to reprotanate the mol with
implicit Hs. If it fails it will return a None rather than causing the outer script to fail.
Inputs:
:param rdkit.Chem.rdchem.Mol sanitized_deprotanated_mol: an rdkit molecule already sanitized and deprotanated.
Returns:
:returns: rdkit.Chem.rdchem.Mol mol_sanitized: an rdkit molecule with H's added and sanitized.
it returns None if H's can't be added or if sanitation fails
"""
if sanitized_deprotanated_mol is not None:
try:
mol = Chem.AddHs(sanitized_deprotanated_mol)
except:
mol = None
mol_sanitized = check_sanitization(mol)
return mol_sanitized
else:
return None
#
def remove_atoms(mol, list_of_idx_to_remove):
"""
This function removes atoms from an rdkit mol based on
a provided list. The RemoveAtom function in Rdkit requires
converting the mol to an more editable version of the rdkit mol
object (Chem.EditableMol).
Inputs:
:param rdkit.Chem.rdchem.Mol mol: any rdkit mol
:param list list_of_idx_to_remove: a list of idx values to remove
from mol
Returns:
:returns: rdkit.Chem.rdchem.Mol new_mol: the rdkit mol as input but with
the atoms from the list removed
"""
if mol is None:
return None
try:
atomsToRemove = list_of_idx_to_remove
atomsToRemove.sort(reverse=True)
except:
return None
try:
em1 = Chem.EditableMol(mol)
for atom in atomsToRemove:
em1.RemoveAtom(atom)
new_mol = em1.GetMol()
return new_mol
except:
return None
#
def Nitrogen_charge_adjustment(mol):
"""
When importing ligands with sanitation turned off, one can successfully import
import a SMILES in which a Nitrogen (N) can have 4 bonds, but no positive charge.
Any 4-bonded N lacking a positive charge will fail a sanitiation check.
-This could be an issue with importing improper SMILES, reactions, or crossing a nuetral nitrogen
with a side chain which adds an extra bond, but doesn't add the extra positive charge.
To correct for this, this function will find all N atoms with a summed bond count of 4
(ie. 4 single bonds;2 double bonds; a single and a triple bond; two single and a double bond)
and set the formal charge of those N's to +1.
RDkit treats aromatic bonds as a bond count of 1.5. But we will not try to correct for
Nitrogens labeled as Aromatic. As precaution, any N which is aromatic is skipped in this function.
Inputs:
:param rdkit.Chem.rdchem.Mol mol: any rdkit mol
Returns:
:returns: rdkit.Chem.rdchem.Mol mol: the same rdkit mol with the N's adjusted
"""
if mol is None:
return None
# makes sure its an rdkit obj
try:
atoms = mol.GetAtoms()
except:
return None
for atom in atoms:
if atom.GetAtomicNum() == 7:
bonds = [bond.GetBondTypeAsDouble() for bond in atom.GetBonds()]
# If aromatic skip as we do not want assume the charge.
if 1.5 in bonds:
continue
# GetBondTypeAsDouble prints out 1 for single, 2.0 for double,
# 3.0 for triple, 1.5 for AROMATIC but if AROMATIC WE WILL SKIP THIS ATOM
num_bond_sums = sum(bonds)
# Check if the octet is filled
if num_bond_sums == 4.0:
atom.SetFormalCharge(+1)
return mol
#
def check_for_unassigned_atom(mol):
"""
Check there isn't a missing atom group ie. '*'
A '*' in a SMILES string is an atom with an atomic num of 0
"""
if mol is None:
return None
try:
atoms = mol.GetAtoms()
except:
return None
for atom in atoms:
if atom.GetAtomicNum() == 0:
return None
return mol
#
def handle_frag_check(mol):
"""
This will take a RDKit Mol object. It will check if it is fragmented.
If it has fragments it will return the largest of the two fragments.
If it has no fragments it will return the molecule on harmed.
"""
if mol is None:
return None
try:
frags = Chem.GetMolFrags(mol, asMols=True, sanitizeFrags=False)
except:
return None
if len(frags) == 1:
return mol
else:
frag_info_list = []
frag_index = 0
for frag in frags:
# Check for unassigned breaks ie. a '*'
frag = check_for_unassigned_atom(frag)
if frag is None:
frag_index = frag_index + 1
continue
else:
num_atoms = frag.GetNumAtoms()
frag_info = [frag_index, num_atoms]
frag_info_list.append(frag_info)
frag_index = frag_index + 1
if len(frag_info_list) == 0:
return None
# Get the largest Fragment
frag_info_list.sort(key=lambda x: float(x[-1]), reverse=True)
largest_frag_idx = frag_info_list[0][0]
largest_frag = frags[largest_frag_idx]
return largest_frag
#
| 10,436 | 30.627273 | 128 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Start.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Contains the prepare_molecules definition which reads, prepares, and writes
small molecules.
"""
import __future__
import sys
import json
import os
from datetime import datetime
from collections import OrderedDict
import gypsum_dl.Utils as Utils
from gypsum_dl.Parallelizer import Parallelizer
from gypsum_dl.Parallelizer import flatten_list
try:
from rdkit.Chem import AllChem
from rdkit import Chem
except:
Utils.exception("You need to install rdkit and its dependencies.")
try:
import numpy
except:
Utils.exception("You need to install numpy and its dependencies.")
try:
from scipy.cluster.vq import kmeans2
except:
Utils.exception("You need to install scipy and its dependencies.")
from gypsum_dl.MolContainer import MolContainer
from gypsum_dl.Steps.SMILES.PrepareSmiles import prepare_smiles
from gypsum_dl.Steps.ThreeD.PrepareThreeD import prepare_3d
from gypsum_dl.Steps.IO.ProcessOutput import proccess_output
from gypsum_dl.Steps.IO.LoadFiles import load_smiles_file
from gypsum_dl.Steps.IO.LoadFiles import load_sdf_file
# see http://www.rdkit.org/docs/GettingStartedInPython.html#working-with-3d-molecules
def prepare_molecules(args):
"""A function for preparing small-molecule models for docking. To work, it
requires that the python module rdkit be installed on the system.
:param args: The arguments, from the commandline.
:type args: dict
"""
# Keep track of the tim the program starts.
start_time = datetime.now()
# A list of command-line parameters that will be ignored if using a json
# file.
json_warning_list = [
"source",
"output_folder",
"num_processors",
"min_ph",
"max_ph",
"delta_ph_increment",
"thoroughness",
"max_variants_per_compound",
"pka_precision",
]
# Whether to warn the user that the above parameters, if specified, will
# be ignored.
need_to_print_override_warning = False
if "json" in args:
# "json" is one of the parameters, so we'll be ignoring the rest.
try:
params = json.load(open(args["json"]))
except:
Utils.exception("Is your input json file properly formed?")
params = set_parameters(params)
if [i for i in json_warning_list if i in list(args.keys())]:
need_to_print_override_warning = True
else:
# We're actually going to use all the command-line parameters. No
# warning necessary.
params = set_parameters(args)
# If running in serial mode, make sure only one processor is used.
if params["job_manager"] == "serial":
if params["num_processors"] != 1:
Utils.log(
"Because --job_manager was set to serial, this will be run on a single processor."
)
params["num_processors"] = 1
# Handle mpi errors if mpi4py isn't installed
if params["job_manager"] == "mpi":
# Before executing Parallelizer with mpi4py (which override python raise Exceptions)
# We must check that it is being run with the "-m mpi4py" runpy flag
sys_modules = sys.modules
if "runpy" not in sys_modules.keys():
printout = "\nTo run in mpi mode you must run with -m flag. ie) mpirun -n $NTASKS python -m mpi4py run_gypsum_dl.py\n"
print(printout)
Utils.exception(printout)
# Check mpi4py import
try:
import mpi4py
except:
printout = "\nmpi4py not installed but --job_manager is set to mpi. \n Either install mpi4py or switch job_manager to multiprocessing or serial.\n"
print(printout)
Utils.exception(printout)
# Check mpi4py import version. This must be at version 2.1.0 and higher
mpi4py_version = mpi4py.__version__
mpi4py_version = [int(x) for x in mpi4py_version.split(".")]
if mpi4py_version[0] == 2:
if mpi4py_version[1] < 1:
printout = "\nmpi4py version 2.1.0 or higher is required. Use the 'python -m mpi4py' flag to run in mpi mode.\nPlease update mpi4py to a newer version, or switch job_manager to multiprocessing or serial.\n"
print(printout)
Utils.exception(printout)
elif mpi4py_version[0] < 2:
printout = "\nmpi4py version 2.1.0 or higher is required. Use the 'python -m mpi4py' flag to run in mpi mode.\nPlease update mpi4py to a newer version, or switch job_manager to multiprocessing or serial.\n"
print(printout)
Utils.exception(printout)
# Throw a message if running on windows. Windows doesn't deal with with
# multiple processors, so use only 1.
if sys.platform == "win32":
Utils.log(
"WARNING: Multiprocessing is not supported on Windows. Tasks will be run in Serial mode."
)
params["num_processors"] = 1
params["job_manager"] = "serial"
# Launch mpi workers if that's what's specified.
if params["job_manager"] == "mpi":
params["Parallelizer"] = Parallelizer(
params["job_manager"], params["num_processors"]
)
else:
# Lower-level mpi (i.e. making a new Parallelizer within an mpi) has
# problems with importing the MPI environment and mpi4py. So we will
# flag it to skip the MPI mode and just go to multiprocess/serial.
# This is a saftey precaution
params["Parallelizer"] = Parallelizer(
params["job_manager"], params["num_processors"], True
)
# Let the user know that their command-line parameters will be ignored, if
# they have specified a json file.
if need_to_print_override_warning == True:
Utils.log("WARNING: Using the --json flag overrides all other flags.")
# If running in mpi mode, separate_output_files must be set to true.
if params["job_manager"] == "mpi" and params["separate_output_files"] == False:
Utils.log(
"WARNING: Running in mpi mode, but separate_output_files is not set to True. Setting separate_output_files to True anyway."
)
params["separate_output_files"] = True
# Outputing HTML files not supported in mpi mode.
if params["job_manager"] == "mpi" and params["add_html_output"] == True:
Utils.log(
"WARNING: Running in mpi mode, but add_html_output is set to True. HTML output is not supported in mpi mode."
)
params["add_html_output"] = False
# Warn the user if he or she is not using the Durrant lab filters.
if params["use_durrant_lab_filters"] ==- False:
Utils.log(
"WARNING: Running Gypsum-DL without the Durrant-lab filters. In looking over many Gypsum-DL-generated " +
"variants, we have identified a number of substructures that, though technically possible, strike us " +
"as improbable or otherwise poorly suited for virtual screening. We strongly recommend removing these " +
"by running Gypsum-DL with the --use_durrant_lab_filters option.",
trailing_whitespace="\n"
)
# Load SMILES data
if isinstance(params["source"], str):
Utils.log("Loading molecules from " + os.path.basename(params["source"]) + "...")
# Smiles must be array of strs.
src = params["source"]
if src.lower().endswith(".smi") or src.lower().endswith(".can"):
# It's an smi file.
smiles_data = load_smiles_file(src)
elif params["source"].lower().endswith(".sdf"):
# It's an sdf file. Convert it to a smiles.
smiles_data = load_sdf_file(src)
else:
smiles_data = [params["source"]]
else:
pass # It's already in the required format.
# Make the output directory if necessary.
if os.path.exists(params["output_folder"]) == False:
os.mkdir(params["output_folder"])
if os.path.exists(params["output_folder"]) == False:
Utils.exception("Output folder directory couldn't be found or created.")
# For Debugging
# print("")
# print("###########################")
# print("num_procs : ", params["num_processors"])
# print("chosen mode : ", params["job_manager"])
# print("Parallel style: ", params["Parallelizer"].return_mode())
# print("Number Nodes: ", params["Parallelizer"].return_node())
# print("###########################")
# print("")
# Make the molecule containers.
contnrs = []
idx_counter = 0
for i in range(0, len(smiles_data)):
try:
smiles, name, props = smiles_data[i]
except:
msg = 'Unexpected error. Does your "source" parameter specify a '
msg = msg + "filename that ends in a .can, .smi, or .sdf extension?"
Utils.exception(msg)
if detect_unassigned_bonds(smiles) is None:
Utils.log(
"WARNING: Throwing out SMILES because of unassigned bonds: " + smiles
)
continue
new_contnr = MolContainer(smiles, name, idx_counter, props)
if (
new_contnr.orig_smi_canonical == None
or type(new_contnr.orig_smi_canonical) != str
):
Utils.log(
"WARNING: Throwing out SMILES because of it couldn't convert to mol: "
+ smiles
)
continue
contnrs.append(new_contnr)
idx_counter += 1
# Remove None types from failed conversion
contnrs = [x for x in contnrs if x.orig_smi_canonical != None]
if len(contnrs) != idx_counter:
Utils.exception("There is a corrupted container")
# In multiprocessing mode, Gypsum-DL parallelizes each small-molecule
# preparation step separately. But this scheme is inefficient in MPI mode
# because it increases the amount of communication required between nodes.
# So for MPI mode, we will run all the preparation steps for a given
# molecule container on a single thread.
if params["Parallelizer"].return_mode() != "mpi":
# Non-MPI (e.g., multiprocessing)
execute_gypsum_dl(contnrs, params)
else:
# MPI mode. Group the molecule containers so they can be passed to the
# parallelizer.
job_input = []
temp_param = {}
for key in list(params.keys()):
if key == "Parallelizer":
temp_param["Parallelizer"] = None
else:
temp_param[key] = params[key]
for contnr in contnrs:
contnr.contnr_idx = 0 # Because each container being run in isolation.
job_input.append(tuple([[contnr], temp_param]))
job_input = tuple(job_input)
params["Parallelizer"].run(job_input, execute_gypsum_dl)
# Calculate the total run time.
end_time = datetime.now()
run_time = end_time - start_time
params["start_time"] = str(start_time)
params["end_time"] = str(end_time)
params["run_time"] = str(run_time)
Utils.log("\nStart time at: " + str(start_time))
Utils.log("End time at: " + str(end_time))
Utils.log("Total time at: " + str(run_time))
# Kill mpi workers if necessary.
params["Parallelizer"].end(params["job_manager"])
def execute_gypsum_dl(contnrs, params):
"""A function for doing all of the manipulations to each molecule.
:param contnrs: A list of all molecules.
:type contnrs: list
:param params: A dictionary containing all of the parameters.
:type params: dict
"""
# Start creating the models.
# Prepare the smiles. Desalt, consider alternate ionization, tautometeric,
# stereoisomeric forms, etc.
prepare_smiles(contnrs, params)
# Convert the processed SMILES strings to 3D.
prepare_3d(contnrs, params)
# Add in name and unique id to each molecule.
add_mol_id_props(contnrs)
# Output the current SMILES.
Utils.print_current_smiles(contnrs)
# Write any mols that fail entirely to a file.
deal_with_failed_molecules(contnrs, params) ####
# Process the output.
proccess_output(contnrs, params)
def detect_unassigned_bonds(smiles):
"""Detects whether a give smiles string has unassigned bonds.
:param smiles: The smiles string.
:type smiles: string
:return: None if it has bad bonds, or the input smiles string otherwise.
:rtype: None|string
"""
mol = Chem.MolFromSmiles(smiles, sanitize=False)
if mol is None:
# Apparently the bonds are particularly bad, because couldn't even
# create the molecule.
return None
for bond in mol.GetBonds():
if bond.GetBondTypeAsDouble() == 0:
return None
return smiles
def set_parameters(params_unicode):
"""Set the parameters that will control this ConfGenerator object.
:param params_unicode: The parameters, with keys and values possibly in
unicode.
:type params_unicode: dict
:return: The parameters, properly processed, with defaults used when no
value specified.
:rtype: dict
"""
# Set the default values.
default = OrderedDict(
{
"source": "",
"output_folder": "./",
"separate_output_files": False,
"add_pdb_output": False,
"add_html_output": False,
"num_processors": -1,
"start_time": 0,
"end_time": 0,
"run_time": 0,
"min_ph": 6.4,
"max_ph": 8.4,
"pka_precision": 1.0,
"thoroughness": 3,
"max_variants_per_compound": 5,
"second_embed": False,
"2d_output_only": False,
"skip_optimize_geometry": False,
"skip_alternate_ring_conformations": False,
"skip_adding_hydrogen": False,
"skip_making_tautomers": False,
"skip_enumerate_chiral_mol": False,
"skip_enumerate_double_bonds": False,
"let_tautomers_change_chirality": False,
"use_durrant_lab_filters": False,
"job_manager": "multiprocessing",
"cache_prerun": False,
"test": False,
}
)
# Modify params so that they keys are always lower case.
# Also, rdkit doesn't play nice with unicode, so convert to ascii
# Because Python2 & Python3 use different string objects, we separate their
# usecases here.
params = {}
if sys.version_info < (3,):
# For Python3
for param in params_unicode:
val = params_unicode[param]
if isinstance(val, unicode):
val = str(val).encode("utf8")
key = param.lower().encode("utf8")
params[key] = val
else:
# For Python2
for param in params_unicode:
val = params_unicode[param]
key = param.lower()
params[key] = val
# Overwrites values with the user parameters where they exit.
merge_parameters(default, params)
# Checks and prepares the final parameter list.
final_params = finalize_params(default)
return final_params
def merge_parameters(default, params):
"""Add default values if missing from parameters.
:param default: The parameters.
:type default: dict
:param params: The default values
:type params: dict
:raises KeyError: Unrecognized parameter.
:raises TypeError: Input parameter has a different type than the default.
"""
# Generate a dictionary with the same keys, but the types for the values.
type_dict = make_type_dict(default)
# Move user-specified values into the parameter.
for param in params:
# Throw an error if there's an unrecognized parameter.
if param not in default:
Utils.log('Parameter "' + str(param) + '" not recognized!')
Utils.log("Here are the options:")
Utils.log(" ".join(sorted(list(default.keys()))))
Utils.exception("Unrecognized parameter: " + str(param))
# Throw an error if the input parameter has a different type than
# the default one.
if not isinstance(params[param], type_dict[param]):
# Cast int to float if necessary
if type(params[param]) is int and type_dict[param] is float:
params[param] = float(params[param])
else:
# Seems to be a type mismatch.
Utils.exception(
'The parameter "'
+ param
+ '" must be of '
+ "type "
+ str(type_dict[param])
+ ", but it is of type "
+ str(type(params[param]))
+ "."
)
# Update the parameter value with the user-defined one.
default[param] = params[param]
def make_type_dict(dictionary):
"""Creates a types dictionary from an existant dictionary. Keys are
preserved, but values are the types.
:param dictionary: A dictionary, with keys are values.
:type dictionary: dict
:return: A dictionary with the same keys, but the values are the types.
:rtype: dict
"""
type_dict = {}
allowed_types = [int, float, bool, str]
# Go through the dictionary keys.
for key in dictionary:
# Get the the type of the value.
val = dictionary[key]
for allowed in allowed_types:
if isinstance(val, allowed):
# Add it to the type_dict.
type_dict[key] = allowed
# The value ha san unacceptable type. Throw an error.
if key not in type_dict:
Utils.exception(
"ERROR: There appears to be an error in your parameter "
+ "JSON file. No value can have type "
+ str(type(val))
+ "."
)
return type_dict
def finalize_params(params):
"""Checks and updates parameters to their final values.
:param params: The parameters.
:type params: dict
:raises NotImplementedError: Missing parameter.
:return: The parameters, corrected/updated where needed.
:rtype: dict
"""
# Throw an error if there's a missing parameter.
if params["source"] == "":
Utils.exception(
'Missing parameter "source". You need to specify '
+ "the source of the input molecules (probably a SMI or SDF "
+ "file)."
)
# Note on parameter "source", the data source. If it's a string that
# ends in ".smi", it's treated as a smiles file. If it's a string that
# ends in ".sdf", it's treated as an sdf file. If it's any other
# string, it's assumed to be a smiles string itself and is assigned a
# name of "". If it's a list, it's assumed to be a list of tuples,
# [SMILES, Name].
# Check some required variables.
try:
params["source"] = os.path.abspath(params["source"])
except:
Utils.exception("Source file doesn't exist.")
source_dir = params["source"].strip(os.path.basename(params["source"]))
if params["output_folder"] == "" and params["source"] != "":
params["output_folder"] = source_dir + "output" + str(os.sep)
if params["add_pdb_output"] == True and params["output_folder"] == "":
Utils.exception("To output files as .pdbs, specify the output_folder.")
if params["separate_output_files"] == True and params["output_folder"] == "":
Utils.exception("For separate_output_files, specify the output_folder.")
# if not os.path.exists(params["output_folder"]) or not os.path.isdir(params["output_folder"]):
# Utils.exception(
# "The specified \"output_folder\", " + params["output_folder"] +
# ", either does not exist or is a file rather than a folder. " +
# "Please provide the path to an existing folder instead."
# )
# Make sure job_manager is always lower case.
params["job_manager"] = params["job_manager"].lower()
return params
def add_mol_id_props(contnrs):
"""Once all molecules have been generated, go through each and add the
name and a unique id (for writing to the SDF file, for example).
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
"""
cont_id = 0
for contnr in contnrs:
for mol in contnr.mols:
cont_id = cont_id + 1
mol.set_rdkit_mol_prop("UniqueID", str(cont_id))
mol.set_all_rdkit_mol_props()
def deal_with_failed_molecules(contnrs, params):
"""Removes and logs failed molecules.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param params: The parameters, used to determine the filename that will
contain the failed molecules.
:type params: dict
"""
failed_ones = [] # To keep track of failed molecules
for contnr in contnrs:
if len(contnr.mols) == 0:
astr = contnr.orig_smi + "\t" + contnr.name
failed_ones.append(astr)
# Let the user know if there's more than one failed molecule.
if len(failed_ones) > 0:
Utils.log("\n3D models could not be generated for the following entries:")
Utils.log("\n".join(failed_ones))
Utils.log("\n")
# Write the failures to an smi file.
outfile = open(params["output_folder"] + os.sep + "gypsum_dl_failed.smi", "w")
outfile.write("\n".join(failed_ones))
outfile.close()
| 22,182 | 35.848837 | 222 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Test/Tester.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module is for testing Gypsum-DL. Not quite unit tests, but good enough
for now.
"""
import os
import shutil
import glob
from gypsum_dl import Utils
from gypsum_dl.Start import prepare_molecules
def run_test():
script_dir = os.path.dirname(os.path.realpath(__file__))
output_folder = script_dir + os.sep + "gypsum_dl_test_output" + os.sep
# Delete test output directory if it exists.
if os.path.exists(output_folder):
shutil.rmtree(output_folder)
# Make the directory
os.mkdir(output_folder)
# Make the Gypsum-DL parameters.
params = {
"source": script_dir + os.sep + "sample_molecules.smi",
"separate_output_files": True,
"job_manager": "serial", # multiprocessing
"output_folder": output_folder,
"add_pdb_output": False,
"max_variants_per_compound": 8,
"thoroughness": 1,
"min_ph": 4,
"max_ph": 10,
"pka_precision": 1,
"use_durrant_lab_filters": True,
}
# Prepare the molecules.
prepare_molecules(params)
Utils.log("")
Utils.log("TEST RESULTS")
Utils.log("============")
# Get the output sdf files.
sdf_files = glob.glob(output_folder + "*")
# There should be seven sdf files.
msg = "Expected 15 output files, got " + str(len(sdf_files)) + "."
if len(sdf_files) != 15:
Utils.exception("FAILED. " + msg)
else:
Utils.log("PASSED. " + msg)
# Get all the smiles from the files.
all_smiles = set([])
for sdf_file in sdf_files:
lines = open(sdf_file).readlines()
for i, line in enumerate(lines):
if "<SMILES>" in line:
all_smiles.add(lines[i + 1].strip())
# List what the smiles should be.
target_smiles = set([])
# salt_and_ionization should produce two models (ionized and
# deionized).
target_smiles |= set(["[O-]c1ccccc1", "Oc1ccccc1"])
# tautomer_and_cis_trans should produce three models (two tautomers, one
# of them with alternate cis/trans).
target_smiles |= set([r"C/C=C\O", "C/C=C/O", "CCC=O"])
# two_chiral_one_unspecified_and_tautomer should produce four models.
target_smiles |= set(
[
"CC(C)C(=O)[C@@](F)(Cl)C[C@@](C)(F)Cl",
"CC(C)=C(O)[C@@](F)(Cl)C[C@@](C)(F)Cl",
"CC(C)C(=O)[C@](F)(Cl)C[C@@](C)(F)Cl",
"CC(C)=C(O)[C@](F)(Cl)C[C@@](C)(F)Cl",
]
)
# two_double_bonds_one_chiral_center should produce eight models.
target_smiles |= set(
[
r"CC/C(C[C@@](C)(Cl)I)=C(I)\C(F)=C(/C)Cl",
"CC/C(C[C@](C)(Cl)I)=C(I)/C(F)=C(/C)Cl",
r"CC/C(C[C@](C)(Cl)I)=C(I)/C(F)=C(\C)Cl",
r"CC/C(C[C@](C)(Cl)I)=C(I)\C(F)=C(\C)Cl",
r"CC/C(C[C@@](C)(Cl)I)=C(I)/C(F)=C(\C)Cl",
r"CC/C(C[C@@](C)(Cl)I)=C(I)\C(F)=C(\C)Cl",
"CC/C(C[C@@](C)(Cl)I)=C(I)/C(F)=C(/C)Cl",
r"CC/C(C[C@](C)(Cl)I)=C(I)\C(F)=C(/C)Cl",
]
)
# two_double_bonds_one_unspecified should produce two models.
target_smiles |= set(
[r"CC/C(C)=C(\Cl)C/C(I)=C(\C)F", r"CC/C(C)=C(/Cl)C/C(I)=C(\C)F"]
)
# non_aromatic_ring should produce one model. It will list it several
# times, because different ring conformations of the same model.
target_smiles |= set(["CC(C)(C)[C@H]1CC[C@@H](C(C)(C)C)CC1"])
# There should be no =[N-] if Durrant lab filters are turned on. Note:
# Removed "CC(=N)O" from below list because durrant lab filters now remove
# iminols.
target_smiles |= set(["CC([NH-])=O", "CC(N)=O"])
# There should be no [N-]C=[N+] (CC(=O)[N-]C=[N+](C)C).
target_smiles |= set(
[
r"C/C(O)=N\C=[N+](C)C",
r"CC(=O)/N=C\[NH+](C)C",
"CC(=O)/N=C/[NH+](C)C",
"CC(=O)NC=[N+](C)C",
"C/C(O)=N/C=[N+](C)C",
]
)
# There should be no [nH+]c[n-] (c1c[nH+]c[n-]1)
target_smiles |= set(["c1c[n-]cn1", "c1c[nH+]c[nH]1", "c1c[nH]cn1"])
# There should be no [#7+]~[#7+] (c1cc[nH+][nH+]c1)
target_smiles |= set(["c1ccnnc1", "c1cc[nH+]nc1"])
# There should be no [#7-]~[#7-] (CC(=O)[N-][N-]C(C)=O). Note that some
# are commented out because Python2 and Python3 given different SMILES
# strings that are all valid. See below to see how things are
# consolodated. (Really this was probably a bad example to pick because
# there are so many forms...)
target_smiles |= set(
[
"CC(=O)NNC(C)=O",
# r"CC(=O)N/N=C(\C)O",
# r"CC(=O)[N-]/N=C(/C)O",
# r"C/C(O)=N/N=C(\C)O",
r"C/C(O)=N\N=C(/C)O",
# r"CC(=O)[N-]/N=C(\C)O",
# "CC(=O)[N-]NC(C)=O",
# "CC(=O)N/N=C(/C)O"
]
)
# There should be no [!#7]~[#7+]~[#7-]~[!#7] (c1c[n-][nH+]c1)
target_smiles |= set(["c1cn[n-]c1", "c1cn[nH]c1", "c1c[nH][nH+]c1"])
# Azides can have adjacent +/- nitrogens.
target_smiles |= set(["CN=[N+]=[N-]", "CN=[N+]=N"])
# msg = "Expected " + str(len(target_smiles)) + " total SMILES, got " + \
# str(len(all_smiles)) + "."
# if len(all_smiles) != len(target_smiles):
# Utils.exception("FAILED. " + msg)
# else:
# Utils.log("PASSED. " + msg)
# Python3 gives some smiles that are different than thsoe obtain with
# Python2. But they are just different representations of the same thing.
# Let's make the switch to the Python2 form for this test.
all_smiles = set(["CN=[N+]=N" if s == "[H]N=[N+]=NC" else s for s in all_smiles])
# Note: Commented out below because durrant lab filters now remove
# iminols.
# all_smiles = set(
# ["CC(=N)O" if s in [r"[H]/N=C(\C)O", "[H]/N=C(/C)O"] else s for s in all_smiles]
# )
all_smiles = set(
[
r"C/C(O)=N\N=C(/C)O"
if s == r"C/C(O)=N/N=C(/C)O"
else s # Different one that turns up sometimes
for s in all_smiles
]
)
all_smiles = set(
[
r"CC(=O)NNC(C)=O"
if s
in [
r"CC(=O)[N-]/N=C(\C)O",
r"C/C(O)=N/N=C(\C)O",
r"CC(=O)N/N=C(\C)O",
r"CC(=O)[N-]/N=C(/C)O",
r"CC(=O)[N-]NC(C)=O",
r"CC(=O)N/N=C(/C)O",
]
else s # Different one that turns up sometimes
for s in all_smiles
]
)
if len(all_smiles ^ target_smiles) > 0:
print(all_smiles)
print(target_smiles)
import pdb; pdb.set_trace()
Utils.exception(
"FAILED. "
+ "Got some SMILES I didn't expect (either in output or target list): "
+ " ".join(list(all_smiles ^ target_smiles))
)
else:
Utils.log("PASSED. Gypsum-DL output the very SMILES strings I was expecting.")
Utils.log("")
# Delete test output directory if it exists.
if os.path.exists(output_folder):
shutil.rmtree(output_folder)
| 7,621 | 32.429825 | 90 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Test/__init__.py | 0 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/__init__.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# gypsum_dl/gypsum_dl/Steps/ThreeD
# Including the below allows other programs to import functions from
# gypsum-DL.
import sys
import os
current_dir = os.path.dirname(os.path.realpath(__file__))
Steps = os.path.dirname(current_dir)
gypsum_gypsum_dir = os.path.dirname(Steps)
gypsum_top_dir = os.path.dirname(gypsum_gypsum_dir)
sys.path.extend([current_dir, Steps, gypsum_gypsum_dir, gypsum_top_dir])
import gypsum_dl.Steps.SMILES
import gypsum_dl.Steps.ThreeD
import gypsum_dl.Steps.IO
| 1,071 | 33.580645 | 74 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/ThreeD/GenerateAlternate3DNonaromaticRingConfs.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module generates alternate non-aromatic ring conformations on the fly,
since most modern docking programs (e.g., Vina) can't consider alternate ring
conformations.
"""
import __future__
import copy
import warnings
import gypsum_dl.Parallelizer as Parallelizer
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
from gypsum_dl.MyMol import MyConformer
try:
from rdkit import Chem
from rdkit.Chem import AllChem
except:
Utils.exception("You need to install rdkit and its dependencies.")
try:
import numpy
except:
Utils.exception("You need to install numpy and its dependencies.")
try:
from scipy.cluster.vq import kmeans2
except:
Utils.exception("You need to install scipy and its dependencies.")
def generate_alternate_3d_nonaromatic_ring_confs(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
second_embed,
job_manager,
parallelizer_obj,
):
"""Docking programs like Vina rotate chemical moieties around their
rotatable bonds, so it's not necessary to generate a larger rotomer
library for each molecule. The one exception to this rule is
non-aromatic rings, which can assume multiple conformations (boat vs.
chair, etc.). This function generates a few low-energy ring structures
for each molecule with a non-aromatic ring(s).
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:param num_procs: The number of processors to use.
:type num_procs: int
:param second_embed: Whether to try to generate 3D coordinates using an
older algorithm if the better (default) algorithm fails. This can add
run time, but sometimes converts certain molecules that would
otherwise fail.
:type second_embed: bool
:param job_manager: The multiprocess mode.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
:return: Returns None if no ring conformers are generated
:rtype: None
"""
# Let the user know you've started this step.
Utils.log(
"Generating several conformers of molecules with non-aromatic "
+ "rings (boat vs. chair, etc.)..."
)
# Create parameters (inputs) to feed to the parallelizer.
params = []
ones_with_nonaro_rngs = set([]) # This is just to keep track of which
# ones have non-aromatic rings.
for contnr_idx, contnr in enumerate(contnrs):
if contnr.num_nonaro_rngs > 0:
ones_with_nonaro_rngs.add(contnr_idx)
for mol in contnr.mols:
params.append(
tuple([mol, max_variants_per_compound, thoroughness, second_embed])
)
params = tuple(params)
# If there are no compounds with non-aromatic rings, no need to continue.
if len(ones_with_nonaro_rngs) == 0:
return # There are no such ligands to process.
# Run it through the parallelizer
tmp = []
if parallelizer_obj != None:
tmp = parallelizer_obj.run(
params, parallel_get_ring_confs, num_procs, job_manager
)
else:
for i in params:
tmp.append(parallel_get_ring_confs(i[0], i[1], i[2], i[3]))
# Flatten the results.
results = Parallelizer.flatten_list(tmp)
# Group by mol. You can't use existing functions because they would
# require you to recalculate already calculated energies.
grouped = {} # Index will be container index. Value is list of
# (energy, mol) pairs.
for mol in results:
# Save the energy as a prop while you're here.
energy = mol.conformers[0].energy
mol.mol_props["Energy"] = energy
# Add the mol with it's energy to the appropriate entry in grouped.
# Make that entry if needed.
contnr_idx = mol.contnr_idx
if not contnr_idx in grouped:
grouped[contnr_idx] = []
grouped[contnr_idx].append((energy, mol))
# Now, for each container, keep only the best ones.
for contnr_idx in grouped:
lst_enrgy_mol_pairs = grouped[contnr_idx]
if len(lst_enrgy_mol_pairs) != 0:
contnrs[contnr_idx].mols = [] # Note that only affects ones that
# had non-aromatic rings.
lst_enrgy_mol_pairs.sort() # Sorting by energy (first item in
# pair).
# Keep only the top ones.
lst_enrgy_mol_pairs = lst_enrgy_mol_pairs[:max_variants_per_compound]
# Add the top ones to the container mol list.
for energy, mol in lst_enrgy_mol_pairs:
contnrs[contnr_idx].add_mol(mol)
else:
# There are no entries in the list. It apparently wasn't able to
# generate any alternate conformers. Let the user know.
for i in range(len(contnrs[contnr_idx].mols)):
contnrs[contnr_idx].mols[i].genealogy.append(
"(WARNING: Could not generate alternate conformations "
+ "of nonaromatic ring)"
)
def parallel_get_ring_confs(mol, max_variants_per_compound, thoroughness, second_embed):
"""Gets alternate ring conformations. Meant to run with the parallelizer class.
:param mol: The molecule to process (with non-aromatic ring(s)).
:type mol: MyMol.MyMol
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:param second_embed: Whether to try to generate 3D coordinates using an
older algorithm if the better (default) algorithm fails. This can add
run time, but sometimes converts certain molecules that would
otherwise fail.
:type second_embed: bool
:return: A list of MyMol.MyMol objects, with alternate ring conformations.
:rtype: list
"""
# Make it easier to access the container index.
contnr_idx = mol.contnr_idx
# All the molecules in this container must have nonatomatic rings (because
# they are all variants of the same source molecule). So just make a new
# mols list.
# Get the ring atom indecies
rings = mol.get_idxs_of_nonaro_rng_atms()
# Convert that into the bond indecies.
rings_by_bond_indexes = [] # A list of lists, where each inner list has
# the indexes of the bonds that comprise a
# ring.
for ring_atom_indecies in rings:
bond_indexes = []
for ring_atm_idx in ring_atom_indecies:
a = mol.rdkit_mol.GetAtomWithIdx(ring_atm_idx)
bonds = a.GetBonds()
for bond in bonds:
atom_indecies = [bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()]
atom_indecies.remove(ring_atm_idx)
other_atm_idx = atom_indecies[0]
if other_atm_idx in ring_atom_indecies:
bond_indexes.append(bond.GetIdx())
bond_indexes = list(set(bond_indexes))
bond_indexes.sort()
rings_by_bond_indexes.append(bond_indexes)
# Generate a bunch of conformations, ordered from best energy to worst.
# Note that this is cached. Minimizing too.
mol.add_conformers(thoroughness * max_variants_per_compound, 0.1, True)
if len(mol.conformers) > 0:
# Sometimes there are no conformers if it's an impossible structure.
# Like
# [H]c1nc(N2C(=O)[C@@]3(C([H])([H])[H])[C@@]4([H])O[C@@]([H])(C([H])([H])C4([H])[H])[C@]3(C([H])([H])[H])C2=O)sc1[H]
# So don't save this one anyway.
# Get the scores (lowest energy) of these minimized conformers.
mol.load_conformers_into_rdkit_mol()
# Extract just the rings.
ring_mols = [
Chem.PathToSubmol(mol.rdkit_mol, bi) for bi in rings_by_bond_indexes
]
# Align get the rmsds relative to the first conformation, for each
# ring separately.
list_of_rmslists = [[]] * len(ring_mols)
for k in range(len(ring_mols)):
list_of_rmslists[k] = []
AllChem.AlignMolConformers(ring_mols[k], RMSlist=list_of_rmslists[k])
# Get points for each conformer (rmsd_ring1, rmsd_ring2, rmsd_ring3)
pts = numpy.array(list_of_rmslists).T
pts = numpy.vstack((numpy.array([[0.0] * pts.shape[1]]), pts))
# Cluster those points, get lowest-energy member of each.
if len(pts) < max_variants_per_compound:
num_clusters = len(pts)
else:
num_clusters = max_variants_per_compound
# When kmeans2 runs on insufficient clusters, it can sometimes throw
# an error about empty clusters. This is not necessary to throw for
# the user and so we have supressed it here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
groups = kmeans2(pts, num_clusters, minit="points")[1]
# Note that you have some geometrically diverse conformations here,
# but there could be other versions (enantiomers, tautomers, etc.)
# that also contribute similar conformations. In the end, you'll be
# selecting from all these together, so similar ones could end up
# together.
best_ones = {} # Key is group id from kmeans (int). Values are the
# MyMol.MyConformers objects.
conformers = mol.rdkit_mol.GetConformers()
for k, grp in enumerate(groups):
if not grp in list(best_ones.keys()):
best_ones[grp] = mol.conformers[k]
best_confs = best_ones.values() # best_confs has the
# MyMol.MyConformers objects.
# Convert rdkit mols to MyMol.MyMol and save those MyMol.MyMol objects
# for returning.
results = []
for conf in best_confs:
new_mol = copy.deepcopy(mol)
c = MyConformer(new_mol, conf.conformer(), second_embed)
new_mol.conformers = [c]
energy = c.energy
new_mol.genealogy = mol.genealogy[:]
new_mol.genealogy.append(
new_mol.smiles(True)
+ " (nonaromatic ring conformer: "
+ str(energy)
+ " kcal/mol)"
)
results.append(new_mol) # i is mol index
return results
# If you get here, something went wrong.
return None
| 12,269 | 38.967427 | 124 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/ThreeD/Minimize3D.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module performs a final 3D minimization to improve the small-molecule
geometry.
"""
import __future__
import copy
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
from gypsum_dl.MyMol import MyConformer
def minimize_3d(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
second_embed,
job_manager,
parallelizer_obj,
):
"""This function minimizes a 3D molecular conformation. In an attempt to
not get trapped in a local minimum, it actually generates a number of
conformers, minimizes the best ones, and then saves the best of the
best.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:param num_procs: The number of processors to use.
:type num_procs: int
:param second_embed: Whether to try to generate 3D coordinates using an
older algorithm if the better (default) algorithm fails. This can add
run time, but sometimes converts certain molecules that would
otherwise fail.
:type second_embed: bool
:param job_manager: The multithred mode to use.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
"""
# Let the user know you're on this step.
Utils.log("Minimizing all 3D molecular structures...")
# Create the parameters (inputs) for the parallelizer.
params = []
ones_without_nonaro_rngs = set([])
for contnr in contnrs:
if contnr.num_nonaro_rngs == 0:
# Because ones with nonaromatic rings have already been minimized,
# so they can be skipped here.
for mol in contnr.mols:
ones_without_nonaro_rngs.add(mol.contnr_idx)
params.append(
tuple([mol, max_variants_per_compound, thoroughness, second_embed])
)
params = tuple(params)
# Run the inputs through the parallelizer.
tmp = []
if parallelizer_obj != None:
tmp = parallelizer_obj.run(params, parallel_minit, num_procs, job_manager)
else:
for i in params:
tmp.append(parallel_minit(i[0], i[1], i[2], i[3]))
# Save energy into MyMol object, and get a list of just those objects.
contnr_list_not_empty = set([]) # To keep track of which container lists
# are not empty. These are the ones
# you'll be repopulating with better
# optimized structures.
results = [] # Will contain MyMol.MyMol objects, with the saved energies
# inside.
for mol in tmp:
mol.mol_props["Energy"] = mol.conformers[0].energy
results.append(mol)
contnr_list_not_empty.add(mol.contnr_idx)
# Go through each of the containers that are not empty and remove current
# ones. Because you'll be replacing them with optimized versions.
for i in contnr_list_not_empty:
contnrs[i].mols = []
# Go through each of the minimized mols, and populate containers they
# belong to.
for mol in results:
contnrs[mol.contnr_idx].add_mol(mol)
# Alert the user to any errors.
for contnr in contnrs:
for mol in contnr.mols:
if mol.rdkit_mol == "":
mol.genealogy.append("(WARNING: Could not optimize 3D geometry)")
mol.conformers = []
def parallel_minit(mol, max_variants_per_compound, thoroughness, second_embed):
"""Minimizes the geometries of a MyMol.MyMol object. Meant to be run
within parallelizer.
:param mol: The molecule to minimize.
:type mol: MyMol.MyMol
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:param second_embed: Whether to try to generate 3D coordinates using an
older algorithm if the better (default) algorithm fails. This can add
run time, but sometimes converts certain molecules that would
otherwise fail.
:type second_embed: bool
:return: A molecule with the minimized conformers inside it.
:rtype: MyMol.MyMol
"""
# Not minimizing. Just adding the conformers.
mol.add_conformers(thoroughness * max_variants_per_compound, 0.1, False)
if len(mol.conformers) > 0:
# Because it is possible to find a molecule that has no
# acceptable conformers (i.e., is not possible geometrically).
# Consider this:
# O=C([C@@]1([C@@H]2O[C@@H]([C@@]1(C3=O)C)CC2)C)N3c4sccn4
# Further minimize the unoptimized conformers that were among the best
# scoring.
max_vars_per_cmpd = max_variants_per_compound
for i in range(len(mol.conformers[:max_vars_per_cmpd])):
mol.conformers[i].minimize()
# Remove similar conformers
# mol.eliminate_structurally_similar_conformers()
# Get the best scoring (lowest energy) of these minimized conformers
new_mol = copy.deepcopy(mol)
c = MyConformer(new_mol, mol.conformers[0].conformer(), second_embed)
new_mol.conformers = [c]
best_energy = c.energy
# Save to the genealogy record.
new_mol.genealogy = mol.genealogy[:]
new_mol.genealogy.append(
new_mol.smiles(True)
+ " (optimized conformer: "
+ str(best_energy)
+ " kcal/mol)"
)
# Save best conformation. For some reason molecular properties
# attached to mol are lost when returning from multiple
# processors. So save the separately so they can be readded to
# the molecule in a bit.
# JDD: Still any issue?
return new_mol
| 7,568 | 38.421875 | 87 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/ThreeD/__init__.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# gypsum_dl/gypsum_dl/Steps/ThreeD
# Including the below allows other programs to import functions from
# gypsum-DL.
import sys
import os
current_dir = os.path.dirname(os.path.realpath(__file__))
Steps = os.path.dirname(current_dir)
gypsum_gypsum_dir = os.path.dirname(Steps)
gypsum_top_dir = os.path.dirname(gypsum_gypsum_dir)
sys.path.extend([current_dir, Steps, gypsum_gypsum_dir, gypsum_top_dir])
| 984 | 35.481481 | 74 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/ThreeD/Convert2DTo3D.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A module to so the 2D to 3D conversion, though the actual code for that
conversion is in MyMol.MyMol.make_first_3d_conf_no_min()
"""
import __future__
import copy
import gypsum_dl.Parallelizer as Parallelizer
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
try:
from rdkit import Chem
from rdkit.Chem import AllChem
except:
Utils.exception("You need to install rdkit and its dependencies.")
def convert_2d_to_3d(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
job_manager,
parallelizer_obj,
):
"""Converts the 1D smiles strings into 3D small-molecule models.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:param num_procs: The number of processors to use.
:type num_procs: int
:param job_manager: The multithred mode to use.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
"""
Utils.log("Converting all molecules to 3D structures.")
# Make the inputs to pass to the parallelizer.
params = []
for contnr in contnrs:
for mol in contnr.mols:
params.append(tuple([mol]))
params = tuple(params)
# Run the parallelizer
tmp = []
if parallelizer_obj != None:
tmp = parallelizer_obj.run(params, parallel_make_3d, num_procs, job_manager)
else:
for i in params:
tmp.append(parallel_make_3d(i[0]))
# Remove and Nones from the output, which represent failed molecules.
clear = Parallelizer.strip_none(tmp)
# Keep only the top few compound variants in each container, to prevent a
# combinatorial explosion.
ChemUtils.bst_for_each_contnr_no_opt(
contnrs, clear, max_variants_per_compound, thoroughness, False
)
def parallel_make_3d(mol):
"""Does the 2D to 3D conversion. Meant to run within parallelizer.
:param mol: The molecule to be converted.
:type mol: MyMol.MyMol
:return: A MyMol.MyMol object with the 3D coordinates inside, or None if
it fails.
:rtype: MyMol.MyMol | None
"""
# Initially assume you won't show an error message.
show_error_msg = False
if mol.rdkit_mol is None:
# The rdkit mol is None. Something's gone wrong. Show an error
# message.
show_error_msg = True
else:
# Check if it has strange substructures.
if mol.remove_bizarre_substruc() == False:
# Perform the conversion.
mol.make_first_3d_conf_no_min()
# If there are some conformations, make note of that in the
# genealogy record.
if len(mol.conformers) > 0:
mol.genealogy.append(mol.smiles(True) + " (3D coordinates assigned)")
return mol
else:
# No conformers? Show an error. Something's gone wrong.
show_error_msg = True
if show_error_msg:
# Something's gone wrong, so show this error.
Utils.log(
"\tWARNING: Could not generate 3D geometry for "
+ str(mol.smiles())
+ " ("
+ mol.name
+ "). Molecule "
+ "discarded."
)
# If you get here, something's gone wrong...
return None
| 4,645 | 32.666667 | 85 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/ThreeD/PrepareThreeD.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Runs the 3D preparation process.
"""
import __future__
from gypsum_dl.Steps.ThreeD.Convert2DTo3D import convert_2d_to_3d
from gypsum_dl.Steps.ThreeD.GenerateAlternate3DNonaromaticRingConfs import (
generate_alternate_3d_nonaromatic_ring_confs,
)
from gypsum_dl.Steps.ThreeD.Minimize3D import minimize_3d
def prepare_3d(contnrs, params):
"""Runs the pipeline for generating the 3D small-molecule models.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param params: The parameters.
:type params: dict
"""
# Unpack some of the user parameters.
max_variants_per_compound = params["max_variants_per_compound"]
thoroughness = params["thoroughness"]
second_embed = params["second_embed"]
num_procs = params["num_processors"]
job_manager = params["job_manager"]
parallelizer_obj = params["Parallelizer"]
# Do the 2d to 3d conversionl, if requested.
if not params["2d_output_only"]:
# Make the 3D model.
convert_2d_to_3d(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
job_manager,
parallelizer_obj,
)
# Generate alternate non-aromatic ring conformations, if requested.
if not params["skip_alternate_ring_conformations"]:
generate_alternate_3d_nonaromatic_ring_confs(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
second_embed,
job_manager,
parallelizer_obj,
)
# Minimize the molecules, if requested.
if not params["skip_optimize_geometry"]:
minimize_3d(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
second_embed,
job_manager,
parallelizer_obj,
)
| 2,581 | 31.275 | 76 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/SMILES/EnumerateDoubleBonds.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Module for enumerating unspecified double bonds (cis vs. trans).
"""
import __future__
import itertools
import copy
import random
import gypsum_dl.Parallelizer as Parallelizer
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
import gypsum_dl.MyMol as MyMol
import math
try:
from rdkit import Chem
except:
Utils.exception("You need to install rdkit and its dependencies.")
def enumerate_double_bonds(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
job_manager,
parallelizer_obj,
):
"""Enumerates all possible cis-trans isomers. If the stereochemistry of a
double bond is specified, it is not varied. All unspecified double bonds
are varied.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: A list.
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:param num_procs: The number of processors to use.
:type num_procs: int
:param job_manager: The multithred mode to use.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
"""
# No need to continue if none are requested.
if max_variants_per_compound == 0:
return
Utils.log("Enumerating all possible cis-trans isomers for all molecules...")
# Group the molecule containers so they can be passed to the parallelizer.
params = []
for contnr in contnrs:
for mol in contnr.mols:
params.append(tuple([mol, max_variants_per_compound, thoroughness]))
params = tuple(params)
# Ruin it through the parallelizer.
tmp = []
if parallelizer_obj != None:
tmp = parallelizer_obj.run(
params, parallel_get_double_bonded, num_procs, job_manager
)
else:
for i in params:
tmp.append(parallel_get_double_bonded(i[0], i[1], i[2]))
# Remove Nones (failed molecules)
clean = Parallelizer.strip_none(tmp)
# Flatten the data into a single list.
flat = Parallelizer.flatten_list(clean)
# Get the indexes of the ones that failed to generate.
contnr_idxs_of_failed = Utils.fnd_contnrs_not_represntd(contnrs, flat)
# Go through the missing ones and throw a message.
for miss_indx in contnr_idxs_of_failed:
Utils.log(
"\tCould not generate valid double-bond variant for "
+ contnrs[miss_indx].orig_smi
+ " ("
+ contnrs[miss_indx].name
+ "), so using existing "
+ "(unprocessed) structures."
)
for mol in contnrs[miss_indx].mols:
mol.genealogy.append("(WARNING: Unable to generate double-bond variant)")
clean.append(mol)
flat = ChemUtils.uniq_mols_in_list(flat)
# Keep only the top few compound variants in each container, to prevent a
# combinatorial explosion.
ChemUtils.bst_for_each_contnr_no_opt(
contnrs, flat, max_variants_per_compound, thoroughness
)
def parallel_get_double_bonded(mol, max_variants_per_compound, thoroughness):
"""A parallelizable function for enumerating double bonds.
:param mol: The molecule with a potentially unspecified double bond.
:type mol: MyMol.MyMol
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:return: [description]
:rtype: [type]
"""
# For this to work, you need to have explicit hydrogens in place.
mol.rdkit_mol = Chem.AddHs(mol.rdkit_mol)
# Get all double bonds that don't have defined stereochemistry. Note that
# these are the bond indexes, not the atom indexes.
unasignd_dbl_bnd_idxs = mol.get_double_bonds_without_stereochemistry()
if len(unasignd_dbl_bnd_idxs) == 0:
# There are no unassigned double bonds, so move on.
return [mol]
# Throw out any bond that is in a small ring.
unasignd_dbl_bnd_idxs = [
i
for i in unasignd_dbl_bnd_idxs
if not mol.rdkit_mol.GetBondWithIdx(i).IsInRingSize(3)
]
unasignd_dbl_bnd_idxs = [
i
for i in unasignd_dbl_bnd_idxs
if not mol.rdkit_mol.GetBondWithIdx(i).IsInRingSize(4)
]
unasignd_dbl_bnd_idxs = [
i
for i in unasignd_dbl_bnd_idxs
if not mol.rdkit_mol.GetBondWithIdx(i).IsInRingSize(5)
]
unasignd_dbl_bnd_idxs = [
i
for i in unasignd_dbl_bnd_idxs
if not mol.rdkit_mol.GetBondWithIdx(i).IsInRingSize(6)
]
unasignd_dbl_bnd_idxs = [
i
for i in unasignd_dbl_bnd_idxs
if not mol.rdkit_mol.GetBondWithIdx(i).IsInRingSize(7)
]
# Previously, I fully enumerated all double bonds. When there are many
# such bonds, that leads to a combinatorial explosion that causes problems
# in terms of speed and memory. Now, enumerate only enough bonds to make
# sure you generate at least thoroughness * max_variants_per_compound.
unasignd_dbl_bnd_idxs_orig_count = len(unasignd_dbl_bnd_idxs)
num_bonds_to_keep = int(math.ceil(math.log(thoroughness * max_variants_per_compound, 2)))
random.shuffle(unasignd_dbl_bnd_idxs)
unasignd_dbl_bnd_idxs = sorted(unasignd_dbl_bnd_idxs[:num_bonds_to_keep])
# Get a list of all the single bonds that come off each double-bond atom.
all_sngl_bnd_idxs = set([])
dbl_bnd_count = 0
for dbl_bnd_idx in unasignd_dbl_bnd_idxs:
bond = mol.rdkit_mol.GetBondWithIdx(dbl_bnd_idx)
atom1 = bond.GetBeginAtom()
atom1_bonds = atom1.GetBonds()
if len(atom1_bonds) == 1:
# The only bond is the one you already know about. So don't save.
continue
atom2 = bond.GetEndAtom()
atom2_bonds = atom2.GetBonds()
if len(atom2_bonds) == 1:
# The only bond is the one you already know about. So don't save.
continue
dbl_bnd_count = dbl_bnd_count + 1
# Suffice it to say, RDKit does not deal with cis-trans isomerization
# in an intuitive way...
idxs_of_other_bnds_frm_atm1 = [b.GetIdx() for b in atom1.GetBonds()]
idxs_of_other_bnds_frm_atm1.remove(dbl_bnd_idx)
idxs_of_other_bnds_frm_atm2 = [b.GetIdx() for b in atom2.GetBonds()]
idxs_of_other_bnds_frm_atm2.remove(dbl_bnd_idx)
all_sngl_bnd_idxs |= set(idxs_of_other_bnds_frm_atm1)
all_sngl_bnd_idxs |= set(idxs_of_other_bnds_frm_atm2)
# Now come up with all possible up/down combinations for those bonds.
all_sngl_bnd_idxs = list(all_sngl_bnd_idxs)
all_atom_config_options = list(
itertools.product([True, False], repeat=len(all_sngl_bnd_idxs))
)
# Let the user know.
if dbl_bnd_count > 0:
Utils.log(
"\t"
+ mol.smiles(True)
+ " has "
# + str(dbl_bnd_count)
+ str(
# Not exactly right, I think, because should be dbl_bnd_count, but ok.
unasignd_dbl_bnd_idxs_orig_count
)
+ " double bond(s) with unspecified stereochemistry."
)
# Go through and consider each of the retained combinations.
smiles_to_consider = set([])
for atom_config_options in all_atom_config_options:
# Make a copy of the original RDKit molecule.
a_rd_mol = copy.copy(mol.rdkit_mol)
# a_rd_mol = Chem.MolFromSmiles(mol.smiles())
for bond_idx, direc in zip(all_sngl_bnd_idxs, atom_config_options):
# Always done with reference to the atom in the double bond.
if direc:
a_rd_mol.GetBondWithIdx(bond_idx).SetBondDir(Chem.BondDir.ENDUPRIGHT)
else:
a_rd_mol.GetBondWithIdx(bond_idx).SetBondDir(Chem.BondDir.ENDDOWNRIGHT)
# Assign the StereoChemistry. Required to actually set it.
a_rd_mol.ClearComputedProps()
Chem.AssignStereochemistry(a_rd_mol, force=True)
# Add to list of ones to consider
try:
smiles_to_consider.add(
Chem.MolToSmiles(a_rd_mol, isomericSmiles=True, canonical=True)
)
except:
# Some molecules still give troubles. Unfortunate, but these are
# rare cases. Let's just skip these. Example:
# CN1C2=C(C=CC=C2)C(C)(C)[C]1=[C]=[CH]C3=CC(=C(O)C(=C3)I)I
continue
# Remove ones that don't have "/" or "\". These are not real enumerated ones.
smiles_to_consider = [s for s in smiles_to_consider if "/" in s or "\\" in s]
# Get the maximum number of / + \ in any string.
cnts = [s.count("/") + s.count("\\") for s in smiles_to_consider]
if len(cnts) == 0:
# There are no appropriate double bonds. Move on...
return [mol]
max_cnts = max(cnts)
# Only keep those with that same max count. The others have double bonds
# that remain unspecified.
smiles_to_consider = [
s[0] for s in zip(smiles_to_consider, cnts) if s[1] == max_cnts
]
results = []
for smile_to_consider in smiles_to_consider:
# Make a new MyMol.MyMol object with the specified smiles.
new_mol = MyMol.MyMol(smile_to_consider)
if new_mol.can_smi != False and new_mol.can_smi != None:
# Sometimes you get an error if there's a bad structure otherwise.
# Add the new molecule to the list of results, if it does not have
# a bizarre substructure.
if not new_mol.remove_bizarre_substruc():
new_mol.contnr_idx = mol.contnr_idx
new_mol.name = mol.name
new_mol.genealogy = mol.genealogy[:]
new_mol.genealogy.append(
new_mol.smiles(True) + " (cis-trans isomerization)"
)
results.append(new_mol)
# Return the results.
return results
| 11,736 | 36.61859 | 93 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/SMILES/DurrantLabFilter.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module removes molecules with prohibited substructures, per Durrant-lab
filters.
"""
import __future__
import gypsum_dl.Parallelizer as Parallelizer
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
try:
from rdkit import Chem
except:
Utils.exception("You need to install rdkit and its dependencies.")
# Get the substructures you won't permit (per substructure matching, not
# substring matching)
prohibited_smi_substrs_for_substruc = [
"C=[N-]",
"[N-]C=[N+]",
"[nH+]c[n-]",
"[#7+]~[#7+]",
"[#7-]~[#7-]",
"[!#7]~[#7+]~[#7-]~[!#7]", # Doesn't hit azide.
# Vina can't process boron anyway...
"[#5]", # B
"O=[PH](=O)([#8])([#8])", # molvs does odd tautomer: OP(O)(O)=O => O=[PH](=O)(O)O
"[#7]=C1[#7]=C[#7]C=C1", # Prevents an odd tautomer sometimes seen with adenine.
"N=c1cc[#7]c[#7]1", # Variant of above
"[$([NX2H1]),$([NX3H2])]=C[$([OH]),$([O-])]", # Terminal iminol
]
# Get the substrings you won't permit (per substring matching)
prohibited_smi_substrs_for_substr = [
# Let's eliminate ones with common metals too (not really druglike)
# "[#13]", # Al
# "[#23]", # V
# "[#26]", # Fe
# "[#27]", # Co
# "[#29]", # Cu
# "[#30]", # Zn
# "[#42]", # Mo
# "[#48]", # Cd
# "[#79]", # Au
# "[#82]" # Pb
# "[#83]", # Bi
"[Al", # Al
"[V", # V
"[Fe", # Fe
"[Co", # Co
"[Cu", # Cu
"[Zn", # Zn
"[Mo", # Mo
"[Cd", # Cd
"[Au", # Au
"[Pb", # Pb
"[Bi", # Bi
]
def durrant_lab_contains_bad_substr(smiles):
"""Determines if a smiles string contains a prohibitive substring. Faster
than substructure matching.
:param smiles: The SMILES string.
:type smiles: A string.
:return: True if it contains the substring. False otherwise.
:rtype: boolean
"""
for s in prohibited_smi_substrs_for_substr:
if s in smiles:
return True
return False
def durrant_lab_filters(contnrs, num_procs, job_manager, parallelizer_obj):
"""Removes any molecules that contain prohibited substructures, per the
durrant-lab filters.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: A list.
:param num_procs: The number of processors to use.
:type num_procs: int
:param job_manager: The multithred mode to use.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
"""
Utils.log("Applying Durrant-lab filters to all molecules...")
prohibited_substructs = [
Chem.MolFromSmarts(s) for s in prohibited_smi_substrs_for_substruc
]
# Get the parameters to pass to the parallelizer object.
params = [[c, prohibited_substructs] for c in contnrs]
# Run the tautomizer through the parallel object.
tmp = []
if parallelizer_obj != None:
tmp = parallelizer_obj.run(
params, parallel_durrant_lab_filter, num_procs, job_manager
)
else:
for c in params:
tmp.append(parallel_durrant_lab_filter(c, prohibited_substructs))
# Note that results is a list of containers.
# Stripping out None values (failed).
results = Parallelizer.strip_none(tmp)
# You need to get the molecules as a flat array so you can run it through
# bst_for_each_contnr_no_opt
mols = []
for contnr in results:
mols.extend(contnr.mols)
# Also clear contnrs, because they will be re-added using
# bst_for_each_contnr_no_opt below.
for contnr in contnrs:
contnr.mols = []
# contnrs = results
# Using this function just to make the changes. Doesn't do energy
# minimization or anything (as it does later) because max variants
# and thoroughness maxed out.
ChemUtils.bst_for_each_contnr_no_opt(
contnrs, mols, 1000, 1000 # max_variants_per_compound, thoroughness
)
def parallel_durrant_lab_filter(contnr, prohibited_substructs):
"""A parallelizable helper function that checks that tautomers do not
break any nonaromatic rings present in the original object.
:param contnr: The molecule container.
:type contnr: MolContainer.MolContainer
:param prohibited_substructs: A list of the prohibited substructures.
:type prohibited_substructs: list
:return: Either the container with bad molecules removed, or a None
object.
:rtype: MolContainer.MolContainer | None
"""
# Replace any molecules that have prohibited substructure with None.
for mi, m in enumerate(contnr.mols):
for pattrn in prohibited_substructs:
if durrant_lab_contains_bad_substr(
m.orig_smi_deslt
) or m.rdkit_mol.HasSubstructMatch(pattrn):
Utils.log(
"\t"
+ m.smiles(True)
+ ", a variant generated "
+ "from "
+ contnr.orig_smi
+ " ("
+ m.name
+ "), contains a prohibited substructure, so I'm "
+ "discarding it."
)
contnr.mols[mi] = None
# continue # JDD: this was wrong, wasn't it?
break # On to next mol in mols.
# Now go back and remove those Nones
contnr.mols = Parallelizer.strip_none(contnr.mols)
# If there are no molecules, mark this container for deletion.
if len(contnr.mols) == 0:
return None
# Return the container
return contnr
| 6,203 | 30.653061 | 86 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/SMILES/AddHydrogens.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module identifies and enumerates the possible protonation sites of
molecules.
"""
from rdkit import Chem
import gypsum_dl.Parallelizer as Parallelizer
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
import gypsum_dl.MyMol as MyMol
import gypsum_dl.MolContainer as MolCont
from gypsum_dl.Steps.SMILES.dimorphite_dl.dimorphite_dl import Protonate
def add_hydrogens(
contnrs,
min_pH,
max_pH,
st_dev,
max_variants_per_compound,
thoroughness,
num_procs,
job_manager,
parallelizer_obj,
):
"""Adds hydrogen atoms to molecule containers, as appropriate for a given
pH.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: A list.
:param min_pH: The minimum pH to consider.
:type min_pH: float
:param max_pH: The maximum pH to consider.
:type max_pH: float
:param st_dev: The standard deviation. See Dimorphite-DL paper.
:type st_dev: float
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:param num_procs: The number of processors to use.
:type num_procs: int
:param job_manager: The multithred mode to use.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
"""
Utils.log("Ionizing all molecules...")
# Make a simple directory with the ionization parameters.
protonation_settings = {
"min_ph": min_pH,
"max_ph": max_pH,
"pka_precision": st_dev,
"max_variants": thoroughness * max_variants_per_compound,
}
# Format the inputs for use in the parallelizer.
inputs = tuple(
[
tuple([cont, protonation_settings])
for cont in contnrs
if type(cont.orig_smi_canonical) == str
]
)
# Run the parallelizer and collect the results.
results = []
if parallelizer_obj != None:
results = parallelizer_obj.run(inputs, parallel_add_H, num_procs, job_manager)
else:
for i in inputs:
results.append(parallel_add_H(i[0], i[1]))
results = Parallelizer.flatten_list(results)
# Dimorphite-DL might not have generated ionization states for some
# molecules. Identify those that are missing.
contnr_idxs_of_failed = Utils.fnd_contnrs_not_represntd(contnrs, results)
# For those molecules, just use the original SMILES string, with hydrogen
# atoms added using RDKit.
for miss_indx in contnr_idxs_of_failed:
Utils.log(
"\tWARNING: Gypsum-DL produced no valid ionization states for "
+ contnrs[miss_indx].orig_smi
+ " ("
+ contnrs[miss_indx].name
+ "), so using the original "
+ "smiles."
)
amol = contnrs[miss_indx].mol_orig_frm_inp_smi
amol.contnr_idx = miss_indx
# Save this failure to the genealogy record.
amol.genealogy = [
amol.orig_smi + " (source)",
amol.orig_smi_deslt + " (desalted)",
"(WARNING: Gypsum-DL could not assign ionization states)",
]
# Save this one to the results too, even though not processed
# properly.
results.append(amol)
# Keep only the top few compound variants in each container, to prevent a
# combinatorial explosion.
ChemUtils.bst_for_each_contnr_no_opt(
contnrs, results, max_variants_per_compound, thoroughness
)
def parallel_add_H(contnr, protonation_settings):
"""Creates alternate ionization variants for a given molecule container.
This is the function that gets fed into the parallelizer.
:param contnr: The molecule container.
:type contnr: MolContainer.MolContainer
:param protonation_settings: Protonation settings to pass to Dimorphite-DL.
:type protonation_settings: dict
:return: [description]
:rtype: [type]
"""
# Make sure the canonical SMILES is actually a string.
if type(contnr.orig_smi_canonical) != str:
Utils.log("container.orig_smi_canonical: " + contnr.orig_smi_canonical)
Utils.log(
"type container.orig_smi_canonical: " + str(type(contnr.orig_smi_canonical))
)
Utils.exception("container.orig_smi_canonical: " + contnr.orig_smi_canonical)
# Add the SMILES string to the protonation parameters.
protonation_settings["smiles"] = contnr.orig_smi_canonical
# Protonate the SMILESstring. This is Dimorphite-DL.
smis = Protonate(protonation_settings)
# Convert the protonated SMILES strings into a list of rdkit molecule
# objects.
rdkit_mols = [Chem.MolFromSmiles(smi.strip()) for smi in smis]
# Convert from rdkit mols to MyMol.MyMol.
addH_mols = [MyMol.MyMol(mol) for mol in rdkit_mols if mol is not None]
# Remove MyMols with odd substructures.
addH_mols = [mol for mol in addH_mols if mol.remove_bizarre_substruc() is False]
# I once saw it add a "C+"" here. So do a secondary check at this point to
# make sure it's valid. Recreate the list, moving new MyMol.MyMol objects
# into the return_values list.
return_values = []
orig_mol = contnr.mol_orig_frm_inp_smi
for Hm in addH_mols:
Hm.inherit_contnr_props(contnr)
Hm.genealogy = orig_mol.genealogy[:]
Hm.name = orig_mol.name
if Hm.smiles() != orig_mol.smiles():
Hm.genealogy.append(Hm.smiles(True) + " (protonated)")
return_values.append(Hm)
return return_values
| 6,779 | 34.129534 | 88 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/SMILES/EnumerateChiralMols.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A module for generating alternate chiralities.
"""
import __future__
import copy
import itertools
import random
import gypsum_dl.Parallelizer as Parallelizer
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
import gypsum_dl.MyMol as MyMol
try:
from rdkit import Chem
except:
Utils.exception("You need to install rdkit and its dependencies.")
def enumerate_chiral_molecules(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
job_manager,
parallelizer_obj,
):
"""Enumerates all possible enantiomers of a molecule. If the chirality of
an atom is given, that chiral center is not varied. Only the chirality
of unspecified chiral centers is varied.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:param num_procs: The number of processors to use.
:type num_procs: int
:param job_manager: The multiprocess mode.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
"""
# No point in continuing none requested.
if max_variants_per_compound == 0:
return
Utils.log("Enumerating all possible enantiomers for all molecules...")
# Group the molecules so you can feed them to parallelizer.
params = []
for contnr in contnrs:
for mol in contnr.mols:
params.append(tuple([mol, thoroughness, max_variants_per_compound]))
params = tuple(params)
# Run it through the parallelizer.
tmp = []
if parallelizer_obj != None:
tmp = parallelizer_obj.run(params, parallel_get_chiral, num_procs, job_manager)
else:
for i in params:
tmp.append(parallel_get_chiral(i[0], i[1], i[2]))
# Remove Nones (failed molecules)
clean = Parallelizer.strip_none(tmp)
# Flatten the data into a single list.
flat = Parallelizer.flatten_list(clean)
# Get the indexes of the ones that failed to generate.
contnr_idxs_of_failed = Utils.fnd_contnrs_not_represntd(contnrs, flat)
# Go through the missing ones and throw a message.
for miss_indx in contnr_idxs_of_failed:
Utils.log(
"\tCould not generate valid enantiomers for "
+ contnrs[miss_indx].orig_smi
+ " ("
+ contnrs[miss_indx].name
+ "), so using existing "
+ "(unprocessed) structures."
)
for mol in contnrs[miss_indx].mols:
mol.genealogy.append("(WARNING: Unable to generate enantiomers)")
clean.append(mol)
# Keep only the top few compound variants in each container, to prevent a
# combinatorial explosion.
ChemUtils.bst_for_each_contnr_no_opt(
contnrs, flat, max_variants_per_compound, thoroughness
)
def parallel_get_chiral(mol, max_variants_per_compound, thoroughness):
"""A parallelizable function for enumerating chiralities.
:param mol: The input molecule.
:type mol: MyMol.MyMol
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:return: A list of MyMol.MyMol objects.
:rtype: list
"""
# Get all chiral centers that aren't assigned explicitly in the input
# molecules.
unasignd = [p[0] for p in mol.chiral_cntrs_w_unasignd() if p[1] == "?"]
num = len(unasignd)
# Get all possible chiral assignments. If the chirality is specified,
# retain it.
results = []
if num == 0:
# There are no unspecified chiral centers, so just keep existing.
results.append(mol)
return results
elif num == 1:
# There's only one chiral center.
options = ["R", "S"]
else:
# There are multiple chiral centers.
starting = [["R"], ["S"]]
options = [["R"], ["S"]]
for i in range(num - 1):
if len(options) > thoroughness * max_variants_per_compound:
# Unfortunately, this section lends itself to a combinatorial
# explosion if there are many chiral centers. Necessary to
# control that or it can become problematic. So truncate early
# if you already have a enough (so some will unfortunately
# never be evaluated).
break
options = list(itertools.product(options, starting))
options = [list(itertools.chain(c[0], c[1])) for c in options]
# Let the user know the number of chiral centers.
Utils.log(
"\t"
+ mol.smiles(True)
+ " ("
+ mol.name
+ ") has "
# + str(len(options))
+ str(2 ** num)
+ " enantiomers when chiral centers with "
+ "no specified chirality are systematically varied."
)
# Randomly select a few of the chiral combinations to examine. This is to
# reduce the potential combinatorial explosion.
num_to_keep_initially = thoroughness * max_variants_per_compound
options = Utils.random_sample(options, num_to_keep_initially, "")
# Go through the chirality combinations and make a molecule with that
# chirality.
for option in options:
# Copy the initial rdkit molecule.
a_rd_mol = copy.copy(mol.rdkit_mol)
# Set its chirality.
for idx, chiral in zip(unasignd, option):
if chiral == "R":
a_rd_mol.GetAtomWithIdx(idx).SetChiralTag(
Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CW
)
elif chiral == "S":
a_rd_mol.GetAtomWithIdx(idx).SetChiralTag(
Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CCW
)
# Make a new MyMol.MyMol object from that rdkit molecule.
new_mol = MyMol.MyMol(a_rd_mol)
# Add the new molecule to the list of results, if it does not have a
# bizarre substructure.
if not new_mol.remove_bizarre_substruc():
new_mol.contnr_idx = mol.contnr_idx
new_mol.name = mol.name
new_mol.genealogy = mol.genealogy[:]
new_mol.genealogy.append(new_mol.smiles(True) + " (chirality)")
results.append(new_mol)
# Return the results.
return results
| 8,191 | 36.067873 | 87 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/SMILES/MakeTautomers.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module makes alternate tautomeric states, using MolVS.
"""
import __future__
import random
import gypsum_dl.Parallelizer as Parallelizer
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
import gypsum_dl.MyMol as MyMol
import gypsum_dl.MolObjectHandling as MOH
try:
from rdkit import Chem
except:
Utils.exception("You need to install rdkit and its dependencies.")
try:
from gypsum_dl.molvs import tautomer
except:
Utils.exception("You need to install molvs and its dependencies.")
def make_tauts(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
job_manager,
let_tautomers_change_chirality,
parallelizer_obj,
):
"""Generates tautomers of the molecules. Note that some of the generated
tautomers are not realistic. If you find a certain improbable
substructure keeps popping up, add it to the list in the
`prohibited_substructures` definition found with MyMol.py, in the function
remove_bizarre_substruc().
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: A list.
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:param thoroughness: How many molecules to generate per variant (molecule)
retained, for evaluation. For example, perhaps you want to advance five
molecules (max_variants_per_compound = 5). You could just generate five
and advance them all. Or you could generate ten and advance the best
five (so thoroughness = 2). Using thoroughness > 1 increases the
computational expense, but it also increases the chances of finding good
molecules.
:type thoroughness: int
:param num_procs: The number of processors to use.
:type num_procs: int
:param let_tautomers_change_chirality: Whether to allow tautomers that
change the total number of chiral centers.
:type let_tautomers_change_chirality: bool
:param job_manager: The multithred mode to use.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
"""
# No need to proceed if there are no max variants.
if max_variants_per_compound == 0:
return
Utils.log("Generating tautomers for all molecules...")
# Create the parameters to feed into the parallelizer object.
params = []
for contnr in contnrs:
for mol_index, mol in enumerate(contnr.mols):
params.append(tuple([contnr, mol_index, max_variants_per_compound]))
params = tuple(params)
# Run the tautomizer through the parallel object.
tmp = []
if parallelizer_obj != None:
tmp = parallelizer_obj.run(params, parallel_make_taut, num_procs, job_manager)
else:
for i in params:
tmp.append(parallel_make_taut(i[0], i[1], i[2]))
# Flatten the resulting list of lists.
none_data = tmp
taut_data = Parallelizer.flatten_list(none_data)
# Remove bad tautomers.
taut_data = tauts_no_break_arom_rngs(
contnrs, taut_data, num_procs, job_manager, parallelizer_obj
)
if not let_tautomers_change_chirality:
taut_data = tauts_no_elim_chiral(
contnrs, taut_data, num_procs, job_manager, parallelizer_obj
)
# taut_data = tauts_no_change_hs_to_cs_unless_alpha_to_carbnyl(
# contnrs, taut_data, num_procs, job_manager, parallelizer_obj
# )
# Keep only the top few compound variants in each container, to prevent a
# combinatorial explosion.
ChemUtils.bst_for_each_contnr_no_opt(
contnrs, taut_data, max_variants_per_compound, thoroughness
)
def parallel_make_taut(contnr, mol_index, max_variants_per_compound):
"""Makes alternate tautomers for a given molecule container. This is the
function that gets fed into the parallelizer.
:param contnr: The molecule container.
:type contnr: MolContainer.MolContainer
:param mol_index: The molecule index.
:type mol_index: int
:param max_variants_per_compound: To control the combinatorial explosion,
only this number of variants (molecules) will be advanced to the next
step.
:type max_variants_per_compound: int
:return: A list of MyMol.MyMol objects, containing the alternate
tautomeric forms.
:rtype: list
"""
# Get the MyMol.MyMol within the molecule container corresponding to the
# given molecule index.
mol = contnr.mols[mol_index]
# Create a temporary RDKit mol object, since that's what MolVS works with.
# TODO: There should be a copy function
m = MyMol.MyMol(mol.smiles()).rdkit_mol
# For tautomers to work, you need to not have any explicit hydrogens.
m = Chem.RemoveHs(m)
# Make sure it's not None.
if m is None:
Utils.log(
"\tCould not generate tautomers for "
+ contnr.orig_smi
+ ". I'm deleting it."
)
return
# Molecules should be kekulized already, but let's double check that.
# Because MolVS requires kekulized input.
Chem.Kekulize(m)
m = MOH.check_sanitization(m)
if m is None:
return None
# Limit to max_variants_per_compound tauts. Note that another batch could
# add more, so you'll need to once again trim to this number later. But
# this could at least help prevent the combinatorial explosion at this
# stage.
enum = tautomer.TautomerEnumerator(max_tautomers=max_variants_per_compound)
tauts_rdkit_mols = enum.enumerate(m)
# Make all those tautomers into MyMol objects.
tauts_mols = [MyMol.MyMol(m) for m in tauts_rdkit_mols]
# Keep only those that have reasonable substructures.
tauts_mols = [t for t in tauts_mols if t.remove_bizarre_substruc() == False]
# If there's more than one, let the user know that.
if len(tauts_mols) > 1:
Utils.log("\t" + mol.smiles(True) + " has tautomers.")
# Now collect the final results.
results = []
for tm in tauts_mols:
tm.inherit_contnr_props(contnr)
tm.genealogy = mol.genealogy[:]
tm.name = mol.name
if tm.smiles() != mol.smiles():
tm.genealogy.append(tm.smiles(True) + " (tautomer)")
results.append(tm)
return results
def tauts_no_break_arom_rngs(
contnrs, taut_data, num_procs, job_manager, parallelizer_obj
):
"""For a given molecule, the number of atomatic rings should never change
regardless of tautization, ionization, etc. Any taut that breaks
aromaticity is unlikely to be worth pursuing. So remove it.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: A list.
:param taut_data: A list of MyMol.MyMol objects.
:type taut_data: list
:param num_procs: The number of processors to use.
:type num_procs: int
:param job_manager: The multithred mode to use.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
:return: A list of MyMol.MyMol objects, with certain bad ones removed.
:rtype: list
"""
# You need to group the taut_data by container to pass it to the
# paralleizer.
params = []
for taut_mol in taut_data:
for contnr in contnrs:
if contnr.contnr_idx == taut_mol.contnr_idx:
container = contnr
params.append(tuple([taut_mol, container]))
params = tuple(params)
# Run it through the parallelizer to remove non-aromatic rings.
tmp = []
if parallelizer_obj != None:
tmp = parallelizer_obj.run(
params, parallel_check_nonarom_rings, num_procs, job_manager
)
else:
for i in params:
tmp.append(parallel_check_nonarom_rings(i[0], i[1]))
# Stripping out None values (failed).
results = Parallelizer.strip_none(tmp)
return results
def tauts_no_elim_chiral(contnrs, taut_data, num_procs, job_manager, parallelizer_obj):
"""Unfortunately, molvs sees removing chiral specifications as being a
distinct taut. I imagine there are cases where tautization could
remove a chiral center, but I think these cases are rare. To compensate
for the error in other folk's code, let's just require that the number
of chiral centers remain unchanged with isomerization.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param taut_data: A list of MyMol.MyMol objects.
:type taut_data: list
:param num_procs: The number of processors to use.
:type num_procs: int
:param job_manager: The multithred mode to use.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
:return: A list of MyMol.MyMol objects, with certain bad ones removed.
:rtype: list
"""
# You need to group the taut_data by contnr to pass to paralleizer.
params = []
for taut_mol in taut_data:
taut_mol_idx = int(taut_mol.contnr_idx)
for contnr in contnrs:
if contnr.contnr_idx == taut_mol.contnr_idx:
container = contnr
params.append(tuple([taut_mol, container]))
params = tuple(params)
# Run it through the parallelizer.
tmp = []
if parallelizer_obj != None:
tmp = parallelizer_obj.run(
params, parallel_check_chiral_centers, num_procs, job_manager
)
else:
for i in params:
tmp.append(parallel_check_chiral_centers(i[0], i[1]))
# Stripping out None values
results = [x for x in tmp if x != None]
return results
def tauts_no_change_hs_to_cs_unless_alpha_to_carbnyl(
contnrs, taut_data, num_procs, job_manager, parallelizer_obj
):
"""Generally speaking, only carbons that are alpha to a carbonyl are
sufficiently acidic to participate in tautomer formation. The
tautomer-generating code you use makes these inappropriate tautomers.
Remove them here.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param taut_data: A list of MyMol.MyMol objects.
:type taut_data: list
:param num_procs: The number of processors to use.
:type num_procs: int
:param job_manager: The multithred mode to use.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
:return: A list of MyMol.MyMol objects, with certain bad ones removed.
:rtype: list
"""
# Group the taut_data by container to run it through the parallelizer.
params = []
for taut_mol in taut_data:
params.append(tuple([taut_mol, contnrs[taut_mol.contnr_idx]]))
params = tuple(params)
# Run it through the parallelizer.
tmp = []
if parallelizer_obj != None:
tmp = parallelizer_obj.run(
params, parallel_check_carbon_hydrogens, num_procs, job_manager
)
else:
for i in params:
tmp.append(parallel_check_carbon_hydrogens(i[0], i[1]))
# Strip out the None values.
results = [x for x in tmp if x != None]
return results
def parallel_check_nonarom_rings(taut, contnr):
"""A parallelizable helper function that checks that tautomers do not
break any nonaromatic rings present in the original object.
:param taut: The tautomer to evaluate.
:type taut: MyMol.MyMol
:param contnr: The original molecule container.
:type contnr: MolContainer.MolContainer
:return: Either the tautomer or a None object.
:rtype: MyMol.MyMol | None
"""
# How many nonaromatic rings in the original smiles?
num_nonaro_rngs_orig = contnr.num_nonaro_rngs
# Check if it breaks aromaticity.
get_idxs_of_nonaro_rng_atms = len(taut.get_idxs_of_nonaro_rng_atms())
if get_idxs_of_nonaro_rng_atms == num_nonaro_rngs_orig:
# Same number of nonaromatic rings as original molecule. Saves the
# good ones.
return taut
else:
Utils.log(
"\t"
+ taut.smiles(True)
+ ", a tautomer generated "
+ "from "
+ contnr.orig_smi
+ " ("
+ taut.name
+ "), broke an aromatic ring, so I'm discarding it."
)
def parallel_check_chiral_centers(taut, contnr):
"""A parallelizable helper function that checks that tautomers do not break
any chiral centers in the original molecule.
:param taut: The tautomer to evaluate.
:type taut: MyMol.MyMol
:param contnr: The original molecule container.
:type contnr: MolContainer.MolContainer
:return: Either the tautomer or a None object.
:rtype: MyMol.MyMol | None
"""
# How many chiral centers in the original smiles?
num_specif_chiral_cntrs_orig = (
contnr.num_specif_chiral_cntrs + contnr.num_unspecif_chiral_cntrs
)
# Make a new list containing only the ones that don't break chiral centers
# (or that add new chiral centers).
m_num_specif_chiral_cntrs = len(taut.chiral_cntrs_only_asignd()) + len(
taut.chiral_cntrs_w_unasignd()
)
if m_num_specif_chiral_cntrs == num_specif_chiral_cntrs_orig:
# Same number of chiral centers as original molecule. Save this good
# one.
return taut
else:
Utils.log(
"\t"
+ contnr.orig_smi
+ " ==> "
+ taut.smiles(True)
+ " (tautomer transformation on "
+ taut.name
+ ") "
+ "changed the molecules total number of specified "
+ "chiral centers from "
+ str(num_specif_chiral_cntrs_orig)
+ " to "
+ str(m_num_specif_chiral_cntrs)
+ ", so I'm deleting it."
)
def parallel_check_carbon_hydrogens(taut, contnr):
"""A parallelizable helper function that checks that tautomers do not
change the hydrogens on inappropriate carbons.
:param taut: The tautomer to evaluate.
:type taut: MyMol.MyMol
:param contnr: The original molecule container.
:type contnr: MolContainer.MolContainer
:return: Either the tautomer or a None object.
:rtype: MyMol.MyMol | None
"""
# What's the carbon-hydrogen fingerprint of the original smiles?
orig_carbon_hydrogen_count = contnr.carbon_hydrogen_count
# How about this tautomer?
this_carbon_hydrogen_count = taut.count_hyd_bnd_to_carb()
# Only keep if they are the same.
if orig_carbon_hydrogen_count == this_carbon_hydrogen_count:
return taut
else:
Utils.log(
"\t"
+ contnr.orig_smi
+ " ==> "
+ taut.smiles(True)
+ " (tautomer transformation on "
+ taut.name
+ ") "
+ "changed the number of hydrogen atoms bound to a "
+ "carbon, so I'm deleting it."
)
| 15,760 | 33.563596 | 87 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/SMILES/PrepareSmiles.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Runs the smile preparation process. Generates alternate ionization,
tautomeric, chiral forms, etc.
"""
import __future__
from gypsum_dl import Utils
from gypsum_dl.Steps.SMILES.DeSaltOrigSmiles import desalt_orig_smi
from gypsum_dl.Steps.SMILES.AddHydrogens import add_hydrogens
from gypsum_dl.Steps.SMILES.MakeTautomers import make_tauts
from gypsum_dl.Steps.SMILES.DurrantLabFilter import (
durrant_lab_filters,
durrant_lab_contains_bad_substr,
)
from gypsum_dl.Steps.SMILES.EnumerateChiralMols import enumerate_chiral_molecules
from gypsum_dl.Steps.SMILES.EnumerateDoubleBonds import enumerate_double_bonds
def prepare_smiles(contnrs, params):
"""Runs the appropriate steps for processing the SMILES strings.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param params: The user parameters.
:type params: dict
"""
# Unpack some of the parameter values.
min_ph = params["min_ph"]
max_ph = params["max_ph"]
std_dev = params["pka_precision"]
max_variants_per_compound = params["max_variants_per_compound"]
thoroughness = params["thoroughness"]
num_procs = params["num_processors"]
job_manager = params["job_manager"]
let_tautomers_change_chirality = params["let_tautomers_change_chirality"]
parallelizer_obj = params["Parallelizer"]
debug = True
# Desalt the molecules. Note that the program always desalts (can't turn it
# off).
# Utils.log("Begin Desaltings")
desalt_orig_smi(contnrs, num_procs, job_manager, parallelizer_obj)
# Utils.log("Done with Desalting")
# Filter the containers to remove ones that have bad substrings (metal,
# etc.) in the desalted smiles, assuming durrant lab filter turned on. Note
# that some compounds aren't filtered until later.
if params["use_durrant_lab_filters"] == True:
contnrs = [
c for c in contnrs if not durrant_lab_contains_bad_substr(c.orig_smi_deslt)
]
if debug:
Utils.print_current_smiles(contnrs)
# Add hydrogens for user-specified pH, if requested.
if not params["skip_adding_hydrogen"]:
# Utils.log("Ionizing Molecules")
add_hydrogens(
contnrs,
min_ph,
max_ph,
std_dev,
max_variants_per_compound,
thoroughness,
num_procs,
job_manager,
parallelizer_obj,
)
# Utils.log("Done with Ionization")
else:
Utils.log("Skipping ionization")
wrap_molecules(contnrs)
if debug:
Utils.print_current_smiles(contnrs)
# Make alternate tautomeric forms, if requested.
if not params["skip_making_tautomers"]:
# Utils.log("Tautomerizing Molecules")
make_tauts(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
job_manager,
let_tautomers_change_chirality,
parallelizer_obj,
)
# Utils.log("Done with Tautomerization")
else:
Utils.log("Skipping tautomerization")
if debug:
Utils.print_current_smiles(contnrs)
# Apply Durrant-lab filters if requested
if params["use_durrant_lab_filters"]:
# Utils.log("Applying Durrant-Lab Filters")
durrant_lab_filters(contnrs, num_procs, job_manager, parallelizer_obj)
# Utils.log("Done Applying Durrant-Lab Filters")
else:
Utils.log("Not applying Durrant-lab filters")
if debug:
Utils.print_current_smiles(contnrs)
# Make alternate chiral forms, if requested.
if not params["skip_enumerate_chiral_mol"]:
# Utils.log("Enumerating Chirality")
enumerate_chiral_molecules(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
job_manager,
parallelizer_obj,
)
# Utils.log("Done with Chirality Enumeration")
else:
Utils.log("Skipping chirality enumeration")
if debug:
Utils.print_current_smiles(contnrs)
# Make alternate double-bond isomers, if requested.
if not params["skip_enumerate_double_bonds"]:
# Utils.log("Enumerating Double Bonds")
enumerate_double_bonds(
contnrs,
max_variants_per_compound,
thoroughness,
num_procs,
job_manager,
parallelizer_obj,
)
# Utils.log("Done with Double Bond Enumeration")
else:
Utils.log("Skipping double bond enumeration")
if debug:
Utils.print_current_smiles(contnrs)
def wrap_molecules(contnrs):
"""Each molecule container holds only one SMILES string
(corresponding to the input structure). Dimorphite-DL can potentially
produce multiple SMILES strings with different ionization states. There is
no way to store muliple smiles in a molecule container.
But those containers are designed to store multiple RDKit molecule
objects. Gypsum-DL stores Dimorphite-DL output molecules as RdKit molecule
objects, in the container's mol list.
But Gypsum-DL gives the user the option of skipping the ionization step.
In this case, the one SMILES needs to be converted to a RDKit mol object
for subsequent steps to work. Let's do that here.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
"""
for mol_cont in contnrs:
if len(mol_cont.mols) == 0:
smi = mol_cont.orig_smi_canonical
mol_cont.add_smiles(smi)
| 6,193 | 32.663043 | 87 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/SMILES/DeSaltOrigSmiles.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Desalts the input SMILES strings. If an input SMILES string contains to
molecule, keep the larger one.
"""
import __future__
import gypsum_dl.Parallelizer as Parallelizer
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
import gypsum_dl.MyMol as MyMol
try:
from rdkit import Chem
except:
Utils.exception("You need to install rdkit and its dependencies.")
def desalt_orig_smi(
contnrs, num_procs, job_manager, parallelizer_obj, durrant_lab_filters=False
):
"""If an input molecule has multiple unconnected fragments, this removes
all but the largest fragment.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param num_procs: The number of processors to use.
:type num_procs: int
:param job_manager: The multiprocess mode.
:type job_manager: string
:param parallelizer_obj: The Parallelizer object.
:type parallelizer_obj: Parallelizer.Parallelizer
"""
Utils.log("Desalting all molecules (i.e., keeping only largest fragment).")
# Desalt each of the molecule containers. This step is very fast, so let's
# just run it on a single processor always.
tmp = [desalter(x) for x in contnrs]
# Go through each contnr and update the orig_smi_deslt. If we update it,
# also add a note in the genealogy record.
tmp = Parallelizer.strip_none(tmp)
for idx in range(0, len(tmp)):
desalt_mol = tmp[idx]
# idx = desalt_mol.contnr_idx
cont = contnrs[idx]
if contnrs[idx].orig_smi != desalt_mol.orig_smi:
desalt_mol.genealogy.append(desalt_mol.orig_smi_deslt + " (desalted)")
cont.update_orig_smi(desalt_mol.orig_smi_deslt)
cont.add_mol(desalt_mol)
def desalter(contnr):
"""Desalts molecules in a molecule container.
:param contnr: The molecule container.
:type contnr: MolContainer.MolContainer
:return: A molecule object.
:rtype: MyMol.MyMol
"""
# Split it into fragments
frags = contnr.get_frags_of_orig_smi()
if len(frags) == 1:
# It's only got one fragment, so default assumption that
# orig_smi = orig_smi_deslt is correct.
return contnr.mol_orig_frm_inp_smi
else:
Utils.log(
"\tMultiple fragments found in "
+ contnr.orig_smi
+ " ("
+ contnr.name
+ ")"
)
# Find the biggest fragment
num_heavy_atoms = []
num_heavy_atoms_to_frag = {}
for i, f in enumerate(frags):
num = f.GetNumHeavyAtoms()
num_heavy_atoms.append(num)
num_heavy_atoms_to_frag[num] = f
max_num = max(num_heavy_atoms)
biggest_frag = num_heavy_atoms_to_frag[max_num]
# Return info about that biggest fragment.
new_mol = MyMol.MyMol(biggest_frag)
new_mol.contnr_idx = contnr.contnr_idx
new_mol.name = contnr.name
new_mol.genealogy = contnr.mol_orig_frm_inp_smi.genealogy
new_mol.make_mol_frm_smiles_sanitze() # Need to update the mol.
return new_mol
| 3,703 | 31.491228 | 82 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/SMILES/__init__.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# gypsum_dl/gypsum_dl/Steps/SMILES/
# Including the below allows other programs to import functions from
# gypsum-DL.
import sys
import os
current_dir = os.path.dirname(os.path.realpath(__file__))
Steps = os.path.dirname(current_dir)
gypsum_gypsum_dir = os.path.dirname(Steps)
gypsum_top_dir = os.path.dirname(gypsum_gypsum_dir)
sys.path.extend([current_dir, Steps, gypsum_gypsum_dir, gypsum_top_dir])
| 985 | 35.518519 | 74 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/SMILES/dimorphite_dl/dimorphite_dl.py | # Copyright 2020 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This script identifies and enumerates the possible protonation sites of SMILES
strings.
"""
from __future__ import print_function
import copy
import os
import argparse
import sys
try:
# Python2
from StringIO import StringIO
except ImportError:
# Python3
from io import StringIO
def print_header():
"""Prints out header information."""
# Always let the user know a help file is available.
print("\nFor help, use: python dimorphite_dl.py --help")
# And always report citation information.
print("\nIf you use Dimorphite-DL in your research, please cite:")
print("Ropp PJ, Kaminsky JC, Yablonski S, Durrant JD (2019) Dimorphite-DL: An")
print(
"open-source program for enumerating the ionization states of drug-like small"
)
print("molecules. J Cheminform 11:14. doi:10.1186/s13321-019-0336-9.\n")
try:
import rdkit
from rdkit import Chem
from rdkit.Chem import AllChem
# Disable the unnecessary RDKit warnings
from rdkit import RDLogger
RDLogger.DisableLog("rdApp.*")
except:
msg = "Dimorphite-DL requires RDKit. See https://www.rdkit.org/"
print(msg)
raise Exception(msg)
def main(params=None):
"""The main definition run when you call the script from the commandline.
:param params: The parameters to use. Entirely optional. If absent,
defaults to None, in which case argments will be taken from
those given at the command line.
:param params: dict, optional
:return: Returns a list of the SMILES strings return_as_list parameter is
True. Otherwise, returns None.
"""
parser = ArgParseFuncs.get_args()
args = vars(parser.parse_args())
if not args["silent"]:
print_header()
# Add in any parameters in params.
if params is not None:
for k, v in params.items():
args[k] = v
# If being run from the command line, print out all parameters.
if __name__ == "__main__":
if not args["silent"]:
print("\nPARAMETERS:\n")
for k in sorted(args.keys()):
print(k.rjust(13) + ": " + str(args[k]))
print("")
if args["test"]:
# Run tests.
TestFuncs.test()
else:
# Run protonation
if "output_file" in args and args["output_file"] is not None:
# An output file was specified, so write to that.
with open(args["output_file"], "w") as file:
for protonated_smi in Protonate(args):
file.write(protonated_smi + "\n")
elif "return_as_list" in args and args["return_as_list"] == True:
return list(Protonate(args))
else:
# No output file specified. Just print it to the screen.
for protonated_smi in Protonate(args):
print(protonated_smi)
class MyParser(argparse.ArgumentParser):
"""Overwrite default parse so it displays help file on error. See
https://stackoverflow.com/questions/4042452/display-help-message-with-python-argparse-when-script-is-called-without-any-argu"""
def error(self, message):
"""Overwrites the default error message.
:param message: The default error message.
"""
self.print_help()
msg = "ERROR: %s\n\n" % message
print(msg)
raise Exception(msg)
def print_help(self, file=None):
"""Overwrite the default print_help function
:param file: Output file, defaults to None
"""
print("")
if file is None:
file = sys.stdout
self._print_message(self.format_help(), file)
print(
"""
examples:
python dimorphite_dl.py --smiles_file sample_molecules.smi
python dimorphite_dl.py --smiles "CCC(=O)O" --min_ph -3.0 --max_ph -2.0
python dimorphite_dl.py --smiles "CCCN" --min_ph -3.0 --max_ph -2.0 --output_file output.smi
python dimorphite_dl.py --smiles_file sample_molecules.smi --pka_precision 2.0 --label_states
python dimorphite_dl.py --test"""
)
print("")
class ArgParseFuncs:
"""A namespace for storing functions that are useful for processing
command-line arguments. To keep things organized."""
@staticmethod
def get_args():
"""Gets the arguments from the command line.
:return: A parser object.
"""
parser = MyParser(
description="Dimorphite 1.2.4: Creates models of "
+ "appropriately protonated small moleucles. "
+ "Apache 2.0 License. Copyright 2020 Jacob D. "
+ "Durrant."
)
parser.add_argument(
"--min_ph",
metavar="MIN",
type=float,
default=6.4,
help="minimum pH to consider (default: 6.4)",
)
parser.add_argument(
"--max_ph",
metavar="MAX",
type=float,
default=8.4,
help="maximum pH to consider (default: 8.4)",
)
parser.add_argument(
"--pka_precision",
metavar="PRE",
type=float,
default=1.0,
help="pKa precision factor (number of standard devations, default: 1.0)",
)
parser.add_argument(
"--smiles", metavar="SMI", type=str, help="SMILES string to protonate"
)
parser.add_argument(
"--smiles_file",
metavar="FILE",
type=str,
help="file that contains SMILES strings to protonate",
)
parser.add_argument(
"--output_file",
metavar="FILE",
type=str,
help="output file to write protonated SMILES (optional)",
)
parser.add_argument(
"--max_variants",
metavar="MXV",
type=int,
default=128,
help="limit number of variants per input compound (default: 128)",
)
parser.add_argument(
"--label_states",
action="store_true",
help="label protonated SMILES with target state "
+ '(i.e., "DEPROTONATED", "PROTONATED", or "BOTH").',
)
parser.add_argument(
"--silent",
action="store_true",
help="do not print any messages to the screen",
)
parser.add_argument(
"--test", action="store_true", help="run unit tests (for debugging)"
)
return parser
@staticmethod
def clean_args(args):
"""Cleans and normalizes input parameters
:param args: A dictionary containing the arguments.
:type args: dict
:raises Exception: No SMILES in params.
"""
defaults = {
"min_ph": 6.4,
"max_ph": 8.4,
"pka_precision": 1.0,
"label_states": False,
"test": False,
"max_variants": 128,
}
for key in defaults:
if key not in args:
args[key] = defaults[key]
keys = list(args.keys())
for key in keys:
if args[key] is None:
del args[key]
if not "smiles" in args and not "smiles_file" in args:
msg = "Error: No SMILES in params. Use the -h parameter for help."
print(msg)
raise Exception(msg)
# If the user provides a smiles string, turn it into a file-like StringIO
# object.
if "smiles" in args:
if isinstance(args["smiles"], str):
args["smiles_file"] = StringIO(args["smiles"])
args["smiles_and_data"] = LoadSMIFile(args["smiles_file"], args)
return args
class UtilFuncs:
"""A namespace to store functions for manipulating mol objects. To keep
things organized."""
@staticmethod
def neutralize_mol(mol):
"""All molecules should be neuralized to the extent possible. The user
should not be allowed to specify the valence of the atoms in most cases.
:param rdkit.Chem.rdchem.Mol mol: The rdkit Mol objet to be neutralized.
:return: The neutralized Mol object.
"""
# Get the reaction data
rxn_data = [
[
"[Ov1-1:1]",
"[Ov2+0:1]-[H]",
], # To handle O- bonded to only one atom (add hydrogen).
[
"[#7v4+1:1]-[H]",
"[#7v3+0:1]",
], # To handle N+ bonded to a hydrogen (remove hydrogen).
[
"[Ov2-:1]",
"[Ov2+0:1]",
], # To handle O- bonded to two atoms. Should not be Negative.
[
"[#7v3+1:1]",
"[#7v3+0:1]",
], # To handle N+ bonded to three atoms. Should not be positive.
[
"[#7v2-1:1]",
"[#7+0:1]-[H]",
], # To handle N- Bonded to two atoms. Add hydrogen.
# ['[N:1]=[N+0:2]=[N:3]-[H]', '[N:1]=[N+1:2]=[N+0:3]-[H]'], # To handle bad azide. Must be
# protonated. (Now handled
# elsewhere, before SMILES
# converted to Mol object.)
[
"[H]-[N:1]-[N:2]#[N:3]",
"[N:1]=[N+1:2]=[N:3]-[H]",
] # To handle bad azide. R-N-N#N should
# be R-N=[N+]=N
]
# Add substructures and reactions (initially none)
for i, rxn_datum in enumerate(rxn_data):
rxn_data[i].append(Chem.MolFromSmarts(rxn_datum[0]))
rxn_data[i].append(None)
# Add hydrogens (respects valence, so incomplete).
mol.UpdatePropertyCache(strict=False)
mol = Chem.AddHs(mol)
while True: # Keep going until all these issues have been resolved.
current_rxn = None # The reaction to perform.
current_rxn_str = None
for i, rxn_datum in enumerate(rxn_data):
(
reactant_smarts,
product_smarts,
substruct_match_mol,
rxn_placeholder,
) = rxn_datum
if mol.HasSubstructMatch(substruct_match_mol):
if rxn_placeholder is None:
current_rxn_str = reactant_smarts + ">>" + product_smarts
current_rxn = AllChem.ReactionFromSmarts(current_rxn_str)
rxn_data[i][3] = current_rxn # Update the placeholder.
else:
current_rxn = rxn_data[i][3]
break
# Perform the reaction if necessary
if current_rxn is None: # No reaction left, so break out of while loop.
break
else:
mol = current_rxn.RunReactants((mol,))[0][0]
mol.UpdatePropertyCache(strict=False) # Update valences
# The mols have been altered from the reactions described above, we
# need to resanitize them. Make sure aromatic rings are shown as such
# This catches all RDKit Errors. without the catchError and
# sanitizeOps the Chem.SanitizeMol can crash the program.
sanitize_string = Chem.SanitizeMol(
mol,
sanitizeOps=rdkit.Chem.rdmolops.SanitizeFlags.SANITIZE_ALL,
catchErrors=True,
)
return mol if sanitize_string.name == "SANITIZE_NONE" else None
@staticmethod
def convert_smiles_str_to_mol(smiles_str):
"""Given a SMILES string, check that it is actually a string and not a
None. Then try to convert it to an RDKit Mol Object.
:param string smiles_str: The SMILES string.
:return: A rdkit.Chem.rdchem.Mol object, or None if it is the wrong type or
if it fails to convert to a Mol Obj
"""
# Check that there are no type errors, ie Nones or non-string A
# non-string type will cause RDKit to hard crash
if smiles_str is None or type(smiles_str) is not str:
return None
# Try to fix azides here. They are just tricky to deal with.
smiles_str = smiles_str.replace("N=N=N", "N=[N+]=N")
smiles_str = smiles_str.replace("NN#N", "N=[N+]=N")
# Now convert to a mol object. Note the trick that is necessary to
# capture RDKit error/warning messages. See
# https://stackoverflow.com/questions/24277488/in-python-how-to-capture-the-stdout-from-a-c-shared-library-to-a-variable
stderr_fileno = sys.stderr.fileno()
stderr_save = os.dup(stderr_fileno)
stderr_pipe = os.pipe()
os.dup2(stderr_pipe[1], stderr_fileno)
os.close(stderr_pipe[1])
mol = Chem.MolFromSmiles(smiles_str)
os.close(stderr_fileno)
os.close(stderr_pipe[0])
os.dup2(stderr_save, stderr_fileno)
os.close(stderr_save)
# Check that there are None type errors Chem.MolFromSmiles has
# sanitize on which means if there is even a small error in the SMILES
# (kekulize, nitrogen charge...) then mol=None. ie.
# Chem.MolFromSmiles("C[N]=[N]=[N]") = None this is an example of an
# nitrogen charge error. It is cased in a try statement to be overly
# cautious.
return None if mol is None else mol
@staticmethod
def eprint(*args, **kwargs):
"""Error messages should be printed to STDERR. See
https://stackoverflow.com/questions/5574702/how-to-print-to-stderr-in-python"""
print(*args, file=sys.stderr, **kwargs)
class LoadSMIFile(object):
"""A generator class for loading in the SMILES strings from a file, one at
a time."""
def __init__(self, filename, args):
"""Initializes this class.
:param filename: The filename or file object (i.e., StringIO).
:type filename: str or StringIO
"""
self.args = args
if type(filename) is str:
# It's a filename
self.f = open(filename, "r")
else:
# It's a file object (i.e., StringIO)
self.f = filename
def __iter__(self):
"""Returns this generator object.
:return: This generator object.
:rtype: LoadSMIFile
"""
return self
def __next__(self):
"""Ensure Python3 compatibility.
:return: A dict, where the "smiles" key contains the canonical SMILES
string and the "data" key contains the remaining information
(e.g., the molecule name).
:rtype: dict
"""
return self.next()
def next(self):
"""Get the data associated with the next line.
:raises StopIteration: If there are no more lines left iin the file.
:return: A dict, where the "smiles" key contains the canonical SMILES
string and the "data" key contains the remaining information
(e.g., the molecule name).
:rtype: dict
"""
line = self.f.readline()
if line == "":
# EOF
self.f.close()
raise StopIteration()
return
# Divide line into smi and data
splits = line.split()
if len(splits) != 0:
# Generate mol object
smiles_str = splits[0]
# Convert from SMILES string to RDKIT Mol. This series of tests is
# to make sure the SMILES string is properly formed and to get it
# into a canonical form. Filter if failed.
mol = UtilFuncs.convert_smiles_str_to_mol(smiles_str)
if mol is None:
if "silent" in self.args and not self.args["silent"]:
UtilFuncs.eprint(
"WARNING: Skipping poorly formed SMILES string: " + line
)
return self.next()
# Handle nuetralizing the molecules. Filter if failed.
mol = UtilFuncs.neutralize_mol(mol)
if mol is None:
if "silent" in self.args and not self.args["silent"]:
UtilFuncs.eprint(
"WARNING: Skipping poorly formed SMILES string: " + line
)
return self.next()
# Remove the hydrogens.
try:
mol = Chem.RemoveHs(mol)
except:
if "silent" in self.args and not self.args["silent"]:
UtilFuncs.eprint(
"WARNING: Skipping poorly formed SMILES string: " + line
)
return self.next()
if mol is None:
if "silent" in self.args and not self.args["silent"]:
UtilFuncs.eprint(
"WARNING: Skipping poorly formed SMILES string: " + line
)
return self.next()
# Regenerate the smiles string (to standardize).
new_mol_string = Chem.MolToSmiles(mol, isomericSmiles=True)
return {"smiles": new_mol_string, "data": splits[1:]}
else:
# Blank line? Go to next one.
return self.next()
class Protonate(object):
"""A generator class for protonating SMILES strings, one at a time."""
def __init__(self, args):
"""Initialize the generator.
:param args: A dictionary containing the arguments.
:type args: dict
"""
# Make the args an object variable variable.
self.args = args
# A list to store the protonated SMILES strings associated with a
# single input model.
self.cur_prot_SMI = []
# Clean and normalize the args
self.args = ArgParseFuncs.clean_args(args)
# Make sure functions in ProtSubstructFuncs have access to the args.
ProtSubstructFuncs.args = args
# Load the substructures that can be protonated.
self.subs = ProtSubstructFuncs.load_protonation_substructs_calc_state_for_ph(
self.args["min_ph"], self.args["max_ph"], self.args["pka_precision"]
)
def __iter__(self):
"""Returns this generator object.
:return: This generator object.
:rtype: Protonate
"""
return self
def __next__(self):
"""Ensure Python3 compatibility.
:return: A dict, where the "smiles" key contains the canonical SMILES
string and the "data" key contains the remaining information
(e.g., the molecule name).
:rtype: dict
"""
return self.next()
def next(self):
"""Return the next protonated SMILES string.
:raises StopIteration: If there are no more lines left iin the file.
:return: A dict, where the "smiles" key contains the canonical SMILES
string and the "data" key contains the remaining information
(e.g., the molecule name).
:rtype: dict
"""
# If there are any SMILES strings in self.cur_prot_SMI, just return
# the first one and update the list to include only the remaining.
if len(self.cur_prot_SMI) > 0:
first, self.cur_prot_SMI = self.cur_prot_SMI[0], self.cur_prot_SMI[1:]
return first
# self.cur_prot_SMI is empty, so try to add more to it.
# Get the next SMILES string from the input file.
try:
smile_and_datum = self.args["smiles_and_data"].next()
except StopIteration:
# There are no more input smiles strings...
raise StopIteration()
# Keep track of the original smiles string for reporting, starting the
# protonation process, etc.
orig_smi = smile_and_datum["smiles"]
# Dimorphite-DL may protonate some sites in ways that produce invalid
# SMILES. We need to keep track of all smiles so we can "rewind" to
# the last valid one, should things go south.
properly_formed_smi_found = [orig_smi]
# Everything on SMILES line but the SMILES string itself (e.g., the
# molecule name).
data = smile_and_datum["data"]
# Collect the data associated with this smiles (e.g., the molecule
# name).
tag = " ".join(data)
# sites is a list of (atom index, "PROTONATED|DEPROTONATED|BOTH",
# reaction name, mol). Note that the second entry indicates what state
# the site SHOULD be in (not the one it IS in per the SMILES string).
# It's calculated based on the probablistic distributions obtained
# during training.
(
sites,
mol_used_to_idx_sites,
) = ProtSubstructFuncs.get_prot_sites_and_target_states(orig_smi, self.subs)
new_mols = [mol_used_to_idx_sites]
if len(sites) > 0:
for site in sites:
# Make a new smiles with the correct protonation state. Note that
# new_smis is a growing list. This is how multiple protonation
# sites are handled.
new_mols = ProtSubstructFuncs.protonate_site(new_mols, site)
if len(new_mols) > self.args["max_variants"]:
new_mols = new_mols[: self.args["max_variants"]]
if "silent" in self.args and not self.args["silent"]:
UtilFuncs.eprint(
"WARNING: Limited number of variants to "
+ str(self.args["max_variants"])
+ ": "
+ orig_smi
)
# Go through each of these new molecules and add them to the
# properly_formed_smi_found, in case you generate a poorly
# formed SMILES in the future and have to "rewind."
properly_formed_smi_found += [Chem.MolToSmiles(m) for m in new_mols]
else:
# Deprotonate the mols (because protonate_site never called to do
# it).
mol_used_to_idx_sites = Chem.RemoveHs(mol_used_to_idx_sites)
new_mols = [mol_used_to_idx_sites]
# Go through each of these new molecules and add them to the
# properly_formed_smi_found, in case you generate a poorly formed
# SMILES in the future and have to "rewind."
properly_formed_smi_found.append(Chem.MolToSmiles(mol_used_to_idx_sites))
# In some cases, the script might generate redundant molecules.
# Phosphonates, when the pH is between the two pKa values and the
# stdev value is big enough, for example, will generate two identical
# BOTH states. Let's remove this redundancy.
new_smis = list(
set(
[
Chem.MolToSmiles(m, isomericSmiles=True, canonical=True)
for m in new_mols
]
)
)
# Sometimes Dimorphite-DL generates molecules that aren't actually
# possible. Simply convert these to mol objects to eliminate the bad
# ones (that are None).
new_smis = [
s for s in new_smis if UtilFuncs.convert_smiles_str_to_mol(s) is not None
]
# If there are no smi left, return the input one at the very least.
# All generated forms have apparently been judged
# inappropriate/malformed.
if len(new_smis) == 0:
properly_formed_smi_found.reverse()
for smi in properly_formed_smi_found:
if UtilFuncs.convert_smiles_str_to_mol(smi) is not None:
new_smis = [smi]
break
# If the user wants to see the target states, add those to the ends of
# each line.
if self.args["label_states"]:
states = "\t".join([x[1] for x in sites])
new_lines = [x + "\t" + tag + "\t" + states for x in new_smis]
else:
new_lines = [x + "\t" + tag for x in new_smis]
self.cur_prot_SMI = new_lines
return self.next()
class ProtSubstructFuncs:
"""A namespace to store functions for loading the substructures that can
be protonated. To keep things organized."""
args = {}
@staticmethod
def load_substructre_smarts_file():
"""Loads the substructure smarts file. Similar to just using readlines,
except it filters out comments (lines that start with "#").
:return: A list of the lines in the site_substructures.smarts file,
except blank lines and lines that start with "#"
"""
pwd = os.path.dirname(os.path.realpath(__file__))
site_structures_file = "{}/{}".format(pwd, "site_substructures.smarts")
lines = [
l
for l in open(site_structures_file, "r")
if l.strip() != "" and not l.startswith("#")
]
return lines
@staticmethod
def load_protonation_substructs_calc_state_for_ph(
min_ph=6.4, max_ph=8.4, pka_std_range=1
):
"""A pre-calculated list of R-groups with protonation sites, with their
likely pKa bins.
:param float min_ph: The lower bound on the pH range, defaults to 6.4.
:param float max_ph: The upper bound on the pH range, defaults to 8.4.
:param pka_std_range: Basically the precision (stdev from predicted pKa to
consider), defaults to 1.
:return: A dict of the protonation substructions for the specified pH
range.
"""
subs = []
for line in ProtSubstructFuncs.load_substructre_smarts_file():
line = line.strip()
sub = {}
if line is not "":
splits = line.split()
sub["name"] = splits[0]
sub["smart"] = splits[1]
sub["mol"] = Chem.MolFromSmarts(sub["smart"])
pka_ranges = [splits[i : i + 3] for i in range(2, len(splits) - 1, 3)]
prot = []
for pka_range in pka_ranges:
site = pka_range[0]
std = float(pka_range[2]) * pka_std_range
mean = float(pka_range[1])
protonation_state = ProtSubstructFuncs.define_protonation_state(
mean, std, min_ph, max_ph
)
prot.append([site, protonation_state])
sub["prot_states_for_pH"] = prot
subs.append(sub)
return subs
@staticmethod
def define_protonation_state(mean, std, min_ph, max_ph):
"""Updates the substructure definitions to include the protonation state
based on the user-given pH range. The size of the pKa range is also based
on the number of standard deviations to be considered by the user param.
:param float mean: The mean pKa.
:param float std: The precision (stdev).
:param float min_ph: The min pH of the range.
:param float max_ph: The max pH of the range.
:return: A string describing the protonation state.
"""
min_pka = mean - std
max_pka = mean + std
# This needs to be reassigned, and 'ERROR' should never make it past
# the next set of checks.
if min_pka <= max_ph and min_ph <= max_pka:
protonation_state = "BOTH"
elif mean > max_ph:
protonation_state = "PROTONATED"
else:
protonation_state = "DEPROTONATED"
return protonation_state
@staticmethod
def get_prot_sites_and_target_states(smi, subs):
"""For a single molecule, find all possible matches in the protonation
R-group list, subs. Items that are higher on the list will be matched
first, to the exclusion of later items.
:param string smi: A SMILES string.
:param list subs: Substructure information.
:return: A list of protonation sites (atom index), pKa bin.
('PROTONATED', 'BOTH', or 'DEPROTONATED'), and reaction name.
Also, the mol object that was used to generate the atom index.
"""
# Convert the Smiles string (smi) to an RDKit Mol Obj
mol_used_to_idx_sites = UtilFuncs.convert_smiles_str_to_mol(smi)
# Check Conversion worked
if mol_used_to_idx_sites is None:
UtilFuncs.eprint("ERROR: ", smi)
return []
# Try to Add hydrogens. if failed return []
try:
mol_used_to_idx_sites = Chem.AddHs(mol_used_to_idx_sites)
except:
UtilFuncs.eprint("ERROR: ", smi)
return []
# Check adding Hs worked
if mol_used_to_idx_sites is None:
UtilFuncs.eprint("ERROR: ", smi)
return []
ProtectUnprotectFuncs.unprotect_molecule(mol_used_to_idx_sites)
protonation_sites = []
for item in subs:
smart = item["mol"]
if mol_used_to_idx_sites.HasSubstructMatch(smart):
matches = ProtectUnprotectFuncs.get_unprotected_matches(
mol_used_to_idx_sites, smart
)
prot = item["prot_states_for_pH"]
for match in matches:
# We want to move the site from being relative to the
# substructure, to the index on the main molecule.
for site in prot:
proton = int(site[0])
category = site[1]
new_site = (match[proton], category, item["name"])
if not new_site in protonation_sites:
# Because sites must be unique.
protonation_sites.append(new_site)
ProtectUnprotectFuncs.protect_molecule(mol_used_to_idx_sites, match)
return protonation_sites, mol_used_to_idx_sites
@staticmethod
def protonate_site(mols, site):
"""Given a list of molecule objects, we protonate the site.
:param list mols: The list of molecule objects.
:param tuple site: Information about the protonation site.
(idx, target_prot_state, prot_site_name)
:return: A list of the appropriately protonated molecule objects.
"""
# Decouple the atom index and its target protonation state from the
# site tuple
idx, target_prot_state, prot_site_name = site
state_to_charge = {"DEPROTONATED": [-1], "PROTONATED": [0], "BOTH": [-1, 0]}
charges = state_to_charge[target_prot_state]
# Now make the actual smiles match the target protonation state.
output_mols = ProtSubstructFuncs.set_protonation_charge(
mols, idx, charges, prot_site_name
)
return output_mols
@staticmethod
def set_protonation_charge(mols, idx, charges, prot_site_name):
"""Sets the atomic charge on a particular site for a set of SMILES.
:param list mols: A list of the input molecule
objects.
:param int idx: The index of the atom to consider.
:param list charges: A list of the charges (ints) to
assign at this site.
:param string prot_site_name: The name of the protonation site.
:return: A list of the processed (protonated/deprotonated) molecule
objects.
"""
# Sets up the output list and the Nitrogen charge
output = []
for charge in charges:
# The charge for Nitrogens is 1 higher than others (i.e.,
# protonated state is positively charged).
nitrogen_charge = charge + 1
# But there are a few nitrogen moieties where the acidic group is
# the neutral one. Amides are a good example. I gave some thought
# re. how to best flag these. I decided that those
# nitrogen-containing moieties where the acidic group is neutral
# (rather than positively charged) will have "*" in the name.
if "*" in prot_site_name:
nitrogen_charge = nitrogen_charge - 1 # Undo what was done previously.
for mol in mols:
# Make a copy of the molecule.
mol_copy = copy.deepcopy(mol)
# Remove hydrogen atoms.
try:
mol_copy = Chem.RemoveHs(mol_copy)
except:
if "silent" in ProtSubstructFuncs.args and not ProtSubstructFuncs.args["silent"]:
UtilFuncs.eprint(
"WARNING: Skipping poorly formed SMILES string: "
+ Chem.MolToSmiles(mol_copy)
)
continue
atom = mol_copy.GetAtomWithIdx(idx)
explicit_bond_order_total = sum(
[b.GetBondTypeAsDouble() for b in atom.GetBonds()]
)
# Assign the protonation charge, with special care for
# nitrogens
element = atom.GetAtomicNum()
if element == 7:
atom.SetFormalCharge(nitrogen_charge)
# Need to figure out how many hydrogens to add.
if nitrogen_charge == 1 and explicit_bond_order_total == 1:
atom.SetNumExplicitHs(3)
elif nitrogen_charge == 1 and explicit_bond_order_total == 2:
atom.SetNumExplicitHs(2)
elif nitrogen_charge == 1 and explicit_bond_order_total == 3:
atom.SetNumExplicitHs(1)
elif nitrogen_charge == 0 and explicit_bond_order_total == 1:
atom.SetNumExplicitHs(2)
elif nitrogen_charge == 0 and explicit_bond_order_total == 2:
atom.SetNumExplicitHs(1)
elif nitrogen_charge == -1 and explicit_bond_order_total == 2:
atom.SetNumExplicitHs(0)
elif nitrogen_charge == -1 and explicit_bond_order_total == 1:
atom.SetNumExplicitHs(1)
#### JDD
else:
atom.SetFormalCharge(charge)
if element == 8 or element == 16: # O and S
if charge == 0 and explicit_bond_order_total == 1:
atom.SetNumExplicitHs(1)
elif charge == -1 and explicit_bond_order_total == 1:
atom.SetNumExplicitHs(0)
# Deprotonating protonated aromatic nitrogen gives [nH-]. Change this
# to [n-].
if "[nH-]" in Chem.MolToSmiles(mol_copy):
atom.SetNumExplicitHs(0)
mol_copy.UpdatePropertyCache(strict=False)
# prod.UpdatePropertyCache(strict=False)
output.append(mol_copy)
return output
class ProtectUnprotectFuncs:
"""A namespace for storing functions that are useful for protecting and
unprotecting molecules. To keep things organized. We need to identify and
mark groups that have been matched with a substructure."""
@staticmethod
def unprotect_molecule(mol):
"""Sets the protected property on all atoms to 0. This also creates the
property for new molecules.
:param rdkit.Chem.rdchem.Mol mol: The rdkit Mol object.
:type mol: The rdkit Mol object with atoms unprotected.
"""
for atom in mol.GetAtoms():
atom.SetProp("_protected", "0")
@staticmethod
def protect_molecule(mol, match):
"""Given a 'match', a list of molecules idx's, we set the protected status
of each atom to 1. This will prevent any matches using that atom in the
future.
:param rdkit.Chem.rdchem.Mol mol: The rdkit Mol object to protect.
:param list match: A list of molecule idx's.
"""
for idx in match:
atom = mol.GetAtomWithIdx(idx)
atom.SetProp("_protected", "1")
@staticmethod
def get_unprotected_matches(mol, substruct):
"""Finds substructure matches with atoms that have not been protected.
Returns list of matches, each match a list of atom idxs.
:param rdkit.Chem.rdchem.Mol mol: The Mol object to consider.
:param string substruct: The SMARTS string of the substructure ot match.
:return: A list of the matches. Each match is itself a list of atom idxs.
"""
matches = mol.GetSubstructMatches(substruct)
unprotected_matches = []
for match in matches:
if ProtectUnprotectFuncs.is_match_unprotected(mol, match):
unprotected_matches.append(match)
return unprotected_matches
@staticmethod
def is_match_unprotected(mol, match):
"""Checks a molecule to see if the substructure match contains any
protected atoms.
:param rdkit.Chem.rdchem.Mol mol: The Mol object to check.
:param list match: The match to check.
:return: A boolean, whether the match is present or not.
"""
for idx in match:
atom = mol.GetAtomWithIdx(idx)
protected = atom.GetProp("_protected")
if protected == "1":
return False
return True
class TestFuncs:
"""A namespace for storing functions that perform tests on the code. To
keep things organized."""
@staticmethod
def test():
"""Tests all the 38 groups."""
# fmt: off
smis = [
# input smiles, protonated, deprotonated, category
["C#CCO", "C#CCO", "C#CC[O-]", "Alcohol"],
["C(=O)N", "NC=O", "[NH-]C=O", "Amide"],
["CC(=O)NOC(C)=O", "CC(=O)NOC(C)=O", "CC(=O)[N-]OC(C)=O", "Amide_electronegative"],
["COC(=N)N", "COC(N)=[NH2+]", "COC(=N)N", "AmidineGuanidine2"],
["Brc1ccc(C2NCCS2)cc1", "Brc1ccc(C2[NH2+]CCS2)cc1", "Brc1ccc(C2NCCS2)cc1", "Amines_primary_secondary_tertiary"],
["CC(=O)[n+]1ccc(N)cc1", "CC(=O)[n+]1ccc([NH3+])cc1", "CC(=O)[n+]1ccc(N)cc1", "Anilines_primary"],
["CCNc1ccccc1", "CC[NH2+]c1ccccc1", "CCNc1ccccc1", "Anilines_secondary"],
["Cc1ccccc1N(C)C", "Cc1ccccc1[NH+](C)C", "Cc1ccccc1N(C)C", "Anilines_tertiary"],
["BrC1=CC2=C(C=C1)NC=C2", "Brc1ccc2[nH]ccc2c1", "Brc1ccc2[n-]ccc2c1", "Indole_pyrrole"],
["O=c1cc[nH]cc1", "O=c1cc[nH]cc1", "O=c1cc[n-]cc1", "Aromatic_nitrogen_protonated"],
["C-N=[N+]=[N@H]", "CN=[N+]=N", "CN=[N+]=[N-]", "Azide"],
["BrC(C(O)=O)CBr", "O=C(O)C(Br)CBr", "O=C([O-])C(Br)CBr", "Carboxyl"],
["NC(NN=O)=N", "NC(=[NH2+])NN=O", "N=C(N)NN=O", "AmidineGuanidine1"],
["C(F)(F)(F)C(=O)NC(=O)C", "CC(=O)NC(=O)C(F)(F)F", "CC(=O)[N-]C(=O)C(F)(F)F", "Imide"],
["O=C(C)NC(C)=O", "CC(=O)NC(C)=O", "CC(=O)[N-]C(C)=O", "Imide2"],
["CC(C)(C)C(N(C)O)=O", "CN(O)C(=O)C(C)(C)C", "CN([O-])C(=O)C(C)(C)C", "N-hydroxyamide"],
["C[N+](O)=O", "C[N+](=O)O", "C[N+](=O)[O-]", "Nitro"],
["O=C1C=C(O)CC1", "O=C1C=C(O)CC1", "O=C1C=C([O-])CC1", "O=C-C=C-OH"],
["C1CC1OO", "OOC1CC1", "[O-]OC1CC1", "Peroxide2"],
["C(=O)OO", "O=COO", "O=CO[O-]", "Peroxide1"],
["Brc1cc(O)cc(Br)c1", "Oc1cc(Br)cc(Br)c1", "[O-]c1cc(Br)cc(Br)c1", "Phenol"],
["CC(=O)c1ccc(S)cc1", "CC(=O)c1ccc(S)cc1", "CC(=O)c1ccc([S-])cc1", "Phenyl_Thiol"],
["C=CCOc1ccc(C(=O)O)cc1", "C=CCOc1ccc(C(=O)O)cc1", "C=CCOc1ccc(C(=O)[O-])cc1", "Phenyl_carboxyl"],
["COP(=O)(O)OC", "COP(=O)(O)OC", "COP(=O)([O-])OC", "Phosphate_diester"],
["CP(C)(=O)O", "CP(C)(=O)O", "CP(C)(=O)[O-]", "Phosphinic_acid"],
["CC(C)OP(C)(=O)O", "CC(C)OP(C)(=O)O", "CC(C)OP(C)(=O)[O-]", "Phosphonate_ester"],
["CC1(C)OC(=O)NC1=O", "CC1(C)OC(=O)NC1=O", "CC1(C)OC(=O)[N-]C1=O", "Ringed_imide1"],
["O=C(N1)C=CC1=O", "O=C1C=CC(=O)N1", "O=C1C=CC(=O)[N-]1", "Ringed_imide2"],
["O=S(OC)(O)=O", "COS(=O)(=O)O", "COS(=O)(=O)[O-]", "Sulfate"],
["COc1ccc(S(=O)O)cc1", "COc1ccc(S(=O)O)cc1", "COc1ccc(S(=O)[O-])cc1", "Sulfinic_acid"],
["CS(N)(=O)=O", "CS(N)(=O)=O", "CS([NH-])(=O)=O", "Sulfonamide"],
["CC(=O)CSCCS(O)(=O)=O", "CC(=O)CSCCS(=O)(=O)O", "CC(=O)CSCCS(=O)(=O)[O-]", "Sulfonate"],
["CC(=O)S", "CC(=O)S", "CC(=O)[S-]", "Thioic_acid"],
["C(C)(C)(C)(S)", "CC(C)(C)S", "CC(C)(C)[S-]", "Thiol"],
["Brc1cc[nH+]cc1", "Brc1cc[nH+]cc1", "Brc1ccncc1", "Aromatic_nitrogen_unprotonated"],
["C=C(O)c1c(C)cc(C)cc1C", "C=C(O)c1c(C)cc(C)cc1C", "C=C([O-])c1c(C)cc(C)cc1C", "Vinyl_alcohol"],
["CC(=O)ON", "CC(=O)O[NH3+]", "CC(=O)ON", "Primary_hydroxyl_amine"],
# Note testing Internal_phosphate_polyphos_chain and
# Initial_phosphate_like_in_ATP_ADP here because no way to
# generate monoprotic compounds to test them. See Other tests
# people...
]
smis_phos = [
# [input smiles, protonated, deprotonated1, deprotonated2, category]
["O=P(O)(O)OCCCC", "CCCCOP(=O)(O)O", "CCCCOP(=O)([O-])O", "CCCCOP(=O)([O-])[O-]", "Phosphate"],
["CC(P(O)(O)=O)C", "CC(C)P(=O)(O)O", "CC(C)P(=O)([O-])O", "CC(C)P(=O)([O-])[O-]", "Phosphonate"],
]
# fmt: on
cats_with_two_prot_sites = [inf[4] for inf in smis_phos]
# Load the average pKa values.
average_pkas = {
l.split()[0].replace("*", ""): float(l.split()[3])
for l in ProtSubstructFuncs.load_substructre_smarts_file()
if l.split()[0] not in cats_with_two_prot_sites
}
average_pkas_phos = {
l.split()[0].replace("*", ""): [float(l.split()[3]), float(l.split()[6])]
for l in ProtSubstructFuncs.load_substructre_smarts_file()
if l.split()[0] in cats_with_two_prot_sites
}
print("Running Tests")
print("=============")
print("")
print("Very Acidic (pH -10000000)")
print("--------------------------")
print("")
args = {
"min_ph": -10000000,
"max_ph": -10000000,
"pka_precision": 0.5,
"smiles": "",
"label_states": True,
"silent": True
}
for smi, protonated, deprotonated, category in smis:
args["smiles"] = smi
TestFuncs.test_check(args, [protonated], ["PROTONATED"])
# Test phosphates separately
for smi, protonated, mix, deprotonated, category in smis_phos:
args["smiles"] = smi
TestFuncs.test_check(args, [protonated], ["PROTONATED"])
args["min_ph"] = 10000000
args["max_ph"] = 10000000
print("")
print("Very Basic (pH 10000000)")
print("------------------------")
print("")
for smi, protonated, deprotonated, category in smis:
args["smiles"] = smi
TestFuncs.test_check(args, [deprotonated], ["DEPROTONATED"])
for smi, protonated, mix, deprotonated, category in smis_phos:
args["smiles"] = smi
TestFuncs.test_check(args, [deprotonated], ["DEPROTONATED"])
print("")
print("pH is Category pKa")
print("------------------")
print("")
for smi, protonated, deprotonated, category in smis:
avg_pka = average_pkas[category]
args["smiles"] = smi
args["min_ph"] = avg_pka
args["max_ph"] = avg_pka
TestFuncs.test_check(args, [protonated, deprotonated], ["BOTH"])
for smi, protonated, mix, deprotonated, category in smis_phos:
args["smiles"] = smi
avg_pka = average_pkas_phos[category][0]
args["min_ph"] = avg_pka
args["max_ph"] = avg_pka
TestFuncs.test_check(args, [mix, protonated], ["BOTH"])
avg_pka = average_pkas_phos[category][1]
args["min_ph"] = avg_pka
args["max_ph"] = avg_pka
TestFuncs.test_check(
args, [mix, deprotonated], ["DEPROTONATED", "DEPROTONATED"]
)
avg_pka = 0.5 * (
average_pkas_phos[category][0] + average_pkas_phos[category][1]
)
args["min_ph"] = avg_pka
args["max_ph"] = avg_pka
args["pka_precision"] = 5 # Should give all three
TestFuncs.test_check(
args, [mix, deprotonated, protonated], ["BOTH", "BOTH"]
)
print("")
print("Other Tests")
print("-----------")
print("")
# Make sure no carbanion (old bug).
smi = "Cc1nc2cc(-c3[nH]c4cc5ccccc5c5c4c3CCN(C(=O)O)[C@@H]5O)cc3c(=O)[nH][nH]c(n1)c23"
output = list(Protonate({"smiles": smi, "test": False, "silent": True}))
if "[C-]" in "".join(output).upper():
msg = "Processing " + smi + " produced a molecule with a carbanion!"
raise Exception(msg)
else:
print("(CORRECT) No carbanion: " + smi)
# Make sure max number of variants is limited (old bug).
smi = "CCCC[C@@H](C(=O)N)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](C(C)C)NC(=O)[C@@H](NC(=O)[C@H](Cc1c[nH]c2c1cccc2)NC(=O)[C@@H](NC(=O)[C@@H](Cc1ccc(cc1)O)N)CCC(=O)N)C)C)Cc1nc[nH]c1)Cc1ccccc1"
output = list(Protonate({"smiles": smi, "test": False, "silent": True}))
if len(output) != 128:
msg = "Processing " + smi + " produced more than 128 variants!"
raise Exception(msg)
else:
print("(CORRECT) Produced 128 variants: " + smi)
# Make sure ATP and NAD work at different pHs (because can't test
# Internal_phosphate_polyphos_chain and
# Initial_phosphate_like_in_ATP_ADP with monoprotic examples.
specific_examples = [
[
"O=P(O)(OP(O)(OP(O)(OCC1OC(C(C1O)O)N2C=NC3=C2N=CN=C3N)=O)=O)O", # input, ATP
(
0.5,
"[NH3+]c1[nH+]c[nH+]c2c1[nH+]cn2C1OC(COP(=O)(O)OP(=O)(O)OP(=O)(O)O)C(O)C1O",
),
(
1.0,
"[NH3+]c1[nH+]c[nH+]c2c1[nH+]cn2C1OC(COP(=O)(O)OP(=O)([O-])OP(=O)(O)O)C(O)C1O",
),
(
2.6,
"[NH3+]c1[nH+]c[nH+]c2c1[nH+]cn2C1OC(COP(=O)([O-])OP(=O)([O-])OP(=O)([O-])O)C(O)C1O",
),
(
7.0,
"Nc1ncnc2c1ncn2C1OC(COP(=O)([O-])OP(=O)([O-])OP(=O)([O-])[O-])C(O)C1O",
),
],
[
"O=P(O)(OP(O)(OCC1C(O)C(O)C(N2C=NC3=C(N)N=CN=C32)O1)=O)OCC(O4)C(O)C(O)C4[N+]5=CC=CC(C(N)=O)=C5", # input, NAD
(
0.5,
"NC(=O)c1ccc[n+](C2OC(COP(=O)(O)OP(=O)(O)OCC3OC(n4cnc5c([NH3+])ncnc54)C(O)C3O)C(O)C2O)c1",
),
(
2.5,
"NC(=O)c1ccc[n+](C2OC(COP(=O)([O-])OP(=O)([O-])OCC3OC(n4cnc5c([NH3+])ncnc54)C(O)C3O)C(O)C2O)c1",
),
(
7.4,
"NC(=O)c1ccc[n+](C2OC(COP(=O)([O-])OP(=O)([O-])OCC3OC(n4cnc5c(N)ncnc54)C(O)C3O)C(O)C2O)c1",
),
],
]
for example in specific_examples:
smi = example[0]
for ph, expected_output in example[1:]:
output = list(
Protonate(
{
"smiles": smi,
"test": False,
"min_ph": ph,
"max_ph": ph,
"pka_precision": 0,
"silent": True
}
)
)
if output[0].strip() == expected_output:
print(
"(CORRECT) "
+ smi
+ " at pH "
+ str(ph)
+ " is "
+ output[0].strip()
)
else:
msg = (
smi
+ " at pH "
+ str(ph)
+ " should be "
+ expected_output
+ ", but it is "
+ output[0].strip()
)
raise Exception(msg)
@staticmethod
def test_check(args, expected_output, labels):
"""Tests most ionizable groups. The ones that can only loose or gain a single proton.
:param args: The arguments to pass to protonate()
:param expected_output: A list of the expected SMILES-strings output.
:param labels: The labels. A list containing combo of BOTH, PROTONATED,
DEPROTONATED.
:raises Exception: Wrong number of states produced.
:raises Exception: Unexpected output SMILES.
:raises Exception: Wrong labels.
"""
output = list(Protonate(args))
output = [o.split() for o in output]
num_states = len(expected_output)
if len(output) != num_states:
msg = (
args["smiles"]
+ " should have "
+ str(num_states)
+ " states at at pH "
+ str(args["min_ph"])
+ ": "
+ str(output)
)
UtilFuncs.eprint(msg)
raise Exception(msg)
if len(set([l[0] for l in output]) - set(expected_output)) != 0:
msg = (
args["smiles"]
+ " is not "
+ " AND ".join(expected_output)
+ " at pH "
+ str(args["min_ph"])
+ " - "
+ str(args["max_ph"])
+ "; it is "
+ " AND ".join([l[0] for l in output])
)
UtilFuncs.eprint(msg)
raise Exception(msg)
if len(set([l[1] for l in output]) - set(labels)) != 0:
msg = (
args["smiles"]
+ " not labeled as "
+ " AND ".join(labels)
+ "; it is "
+ " AND ".join([l[1] for l in output])
)
UtilFuncs.eprint(msg)
raise Exception(msg)
ph_range = sorted(list(set([args["min_ph"], args["max_ph"]])))
ph_range_str = "(" + " - ".join("{0:.2f}".format(n) for n in ph_range) + ")"
print(
"(CORRECT) "
+ ph_range_str.ljust(10)
+ " "
+ args["smiles"]
+ " => "
+ " AND ".join([l[0] for l in output])
)
def run(**kwargs):
"""A helpful, importable function for those who want to call Dimorphite-DL
from another Python script rather than the command line. Note that this
function accepts keyword arguments that match the command-line parameters
exactly. If you want to pass and return a list of RDKit Mol objects, import
run_with_mol_list() instead.
:param **kwargs: For a complete description, run dimorphite_dl.py from the
command line with the -h option.
:type kwargs: dict
"""
# Run the main function with the specified arguments.
main(kwargs)
def run_with_mol_list(mol_lst, **kwargs):
"""A helpful, importable function for those who want to call Dimorphite-DL
from another Python script rather than the command line. Note that this
function is for passing Dimorphite-DL a list of RDKit Mol objects, together
with command-line parameters. If you want to use only the same parameters
that you would use from the command line, import run() instead.
:param mol_lst: A list of rdkit.Chem.rdchem.Mol objects.
:type mol_lst: list
:raises Exception: If the **kwargs includes "smiles", "smiles_file",
"output_file", or "test" parameters.
:return: A list of properly protonated rdkit.Chem.rdchem.Mol objects.
:rtype: list
"""
# Do a quick check to make sure the user input makes sense.
for bad_arg in ["smiles", "smiles_file", "output_file", "test"]:
if bad_arg in kwargs:
msg = (
"You're using Dimorphite-DL's run_with_mol_list(mol_lst, "
+ '**kwargs) function, but you also passed the "'
+ bad_arg
+ '" argument. Did you mean to use the '
+ "run(**kwargs) function instead?"
)
UtilFuncs.eprint(msg)
raise Exception(msg)
# Set the return_as_list flag so main() will return the protonated smiles
# as a list.
kwargs["return_as_list"] = True
# Having reviewed the code, it will be very difficult to rewrite it so
# that a list of Mol objects can be used directly. Intead, convert this
# list of mols to smiles and pass that. Not efficient, but it will work.
protonated_smiles_and_props = []
for m in mol_lst:
props = m.GetPropsAsDict()
kwargs["smiles"] = Chem.MolToSmiles(m, isomericSmiles=True)
protonated_smiles_and_props.extend(
[(s.split("\t")[0], props) for s in main(kwargs)]
)
# Now convert the list of protonated smiles strings back to RDKit Mol
# objects. Also, add back in the properties from the original mol objects.
mols = []
for s, props in protonated_smiles_and_props:
m = Chem.MolFromSmiles(s)
if m:
for prop, val in props.items():
if type(val) is int:
m.SetIntProp(prop, val)
elif type(val) is float:
m.SetDoubleProp(prop, val)
elif type(val) is bool:
m.SetBoolProp(prop, val)
else:
m.SetProp(prop, str(val))
mols.append(m)
else:
UtilFuncs.eprint(
"WARNING: Could not process molecule with SMILES string "
+ s
+ " and properties "
+ str(props)
)
return mols
if __name__ == "__main__":
main()
| 56,465 | 37.861666 | 209 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/SMILES/dimorphite_dl/__init__.py | # This file included so this directory can be loaded as a Python module.
| 73 | 36 | 72 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/IO/Web2DOutput.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Saves the output to an HTML file (2D images only). This is mostly for
debugging.
"""
# import webbrowser
import os
import gypsum_dl.Utils as Utils
import gypsum_dl.ChemUtils as ChemUtils
try:
from rdkit.Chem import rdDepictor
from rdkit.Chem.Draw import rdMolDraw2D
from rdkit.Chem.Draw import PrepareMolForDrawing
from rdkit import Chem
except:
Utils.exception("You need to install rdkit and its dependencies.")
def web_2d_output(contnrs, output_folder):
"""Saves pictures of the models to an HTML file on disk. It can be viewed in
a browser. Then opens a browser automatically to view them. This is mostly
for debugging."""
Utils.log("Saving html image of molecules associated with...")
# Let's not parallelize it for now. This will rarely be used.
html_file = output_folder + os.sep + "gypsum_dl_success.html"
f = open(html_file, "w")
for contnr in contnrs:
Utils.log("\t" + contnr.orig_smi)
for mol in contnr.mols:
# See
# http://rdkit.org/docs/source/rdkit.Chem.rdmolops.html#rdkit.Chem.rdmolops.RemoveHs
# I think in older versions of rdkit (e.g., 2016.09.2), RemoveHs
# would remove hydrogens, even if that make double bonds
# ambiguous. Not so in newer versions (e.g., 2018.03.4). So if
# your double-bonded nitrogen doesn't have its hydrogen attached,
# and you're using an older version of rdkit, don't worry about
# it. The cis/trans info is still there.
mol2 = Chem.RemoveHs(mol.rdkit_mol)
# mol2 = mol.rdkit_mol
mol2 = PrepareMolForDrawing(mol2, addChiralHs=True, wedgeBonds=True)
rdDepictor.Compute2DCoords(mol2)
drawer = rdMolDraw2D.MolDraw2DSVG(200, 200)
drawer.DrawMolecule(mol2)
drawer.FinishDrawing()
svg = drawer.GetDrawingText()
f.write(
'<div style="float: left; width:200px; height: 220px;" title="'
+ mol.name
+ '">'
+ '<div style="width: 200px; height: 200px;">'
+ svg.replace("svg:", "")
+ "</div>"
+ '<div style="width: 200px; height: 20px;">'
+ "<small><center>"
+ mol.smiles(True)
+ "</center></small>"
+ "</div>"
+ "</div>"
)
f.close()
# Open the browser to show the file.
# webbrowser.open("file://" + os.path.abspath(html_file))
| 3,142 | 37.329268 | 96 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/IO/SaveToPDB.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Contains the function for saving the output to PDB files.
"""
import __future__
import glob
import sys
import os
from os.path import basename
from gypsum_dl import Utils
import rdkit
import rdkit.Chem as Chem
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
sys.path.append(os.path.join(os.path.abspath(os.path.dirname(__file__)), "gypsum_dl"))
def convert_sdfs_to_PDBs(contnrs, output_folder):
"""This will convert every conformer into a PDB file, which is saved in
the output_folder.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param output_folder: The name of the output folder.
:type output_folder: str
"""
# Go through each container.
for contnr in contnrs:
contnr.add_container_properties()
# Get the molecule name and associated variants.
name = contnr.name
mols = contnr.mols
# Got through the variants.
for i, m in enumerate(mols):
pdb_file = "{}{}__input{}__variant{}.pdb".format(
output_folder + os.sep,
Utils.slug(name),
contnr.contnr_idx_orig + 1,
i + 1,
)
# Get the conformers into the rdkit_mol object.
m.load_conformers_into_rdkit_mol()
mol = m.rdkit_mol
if mol == None:
continue
else:
# Write conformers to a PDB file.
Chem.MolToPDBFile(mol, pdb_file, flavor=32)
# Add header to PDB file with original SMILES and final SMILES
printout = "REMARK Original SMILES string: {}\nREMARK Final SMILES string: {}\n".format(
m.orig_smi, m.standardize_smiles()
)
with open(pdb_file) as f:
printout = printout + f.read()
with open(pdb_file, "w") as f:
f.write(printout)
printout = ""
| 2,598 | 31.08642 | 104 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/IO/ProcessOutput.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
The proccess_output definition determines which formats are saved to the
disk (output).
"""
import __future__
from gypsum_dl.Steps.IO.SaveToSDF import save_to_sdf
from gypsum_dl.Steps.IO.SaveToPDB import convert_sdfs_to_PDBs
from gypsum_dl.Steps.IO.Web2DOutput import web_2d_output
from gypsum_dl import Utils
def proccess_output(contnrs, params):
"""Proccess the molecular models in preparation for writing them to the
disk."""
# Unpack some variables.
separate_output_files = params["separate_output_files"]
output_folder = params["output_folder"]
if params["add_html_output"] == True:
# Write to an HTML file.
web_2d_output(contnrs, output_folder)
# Write to an SDF file.
save_to_sdf(contnrs, params, separate_output_files, output_folder)
# Also write to PDB files, if requested.
if params["add_pdb_output"] == True:
Utils.log("\nMaking PDB output files\n")
convert_sdfs_to_PDBs(contnrs, output_folder)
| 1,574 | 32.510638 | 75 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/IO/LoadFiles.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A module for loading in files.
"""
import __future__
from gypsum_dl import Utils
try:
from rdkit import Chem
except:
Utils.exception("You need to install rdkit and its dependencies.")
def load_smiles_file(filename):
"""Loads a smiles file.
:param filename: The filename.
:type filename: str
:return: A list of tuples, (SMILES, Name).
:rtype: list
"""
# A smiles file contains one molecule on each line. Each line is a string,
# separated by white space, followed by the molecule name.
data = []
duplicate_names = {}
line_counter = 0
name_list = []
for line in open(filename):
# You've got the line.
line = line.strip()
if line != "":
# From that line, get the smiles string and name.
chunks = line.split()
smiles = chunks[0]
name = " ".join(chunks[1:])
# Handle unnamed ligands.
if name == "":
name = "untitled_line_{}".format(line_counter + 1)
Utils.log(
(
"\tUntitled ligand on line {}. Naming that ligand "
+ "{}. All associated files will be referred to with "
+ "this name."
).format(line_counter + 1, name)
)
# Handle duplicate ligands in same list.
if name in name_list:
# If multiple names...
if name in list(duplicate_names.keys()):
duplicate_names[name] = duplicate_names[name] + 1
new_name = "{}_copy_{}".format(name, duplicate_names[name])
Utils.log(
"\nMultiple entries with the ligand name: {}".format(name)
)
Utils.log(
"\tThe version of the ligand on line {} will be retitled {}".format(
line_counter, new_name
)
)
Utils.log(
"\tAll associated files will be referred to with this name"
)
name = new_name
else:
duplicate_names[name] = 2
new_name = "{}_copy_{}".format(name, duplicate_names[name])
Utils.log(
"\nMultiple entries with the ligand name: {}".format(name)
)
Utils.log(
"\tThe version of the ligand on line {} will be retitled {}".format(
line_counter, new_name
)
)
Utils.log(
"\tAll associated files will be referred to with this name"
)
name = new_name
# Save the data for this line and advance.
name_list.append(name)
line_counter += 1
data.append((smiles, name, {}))
# Return the data.
return data
def load_sdf_file(filename):
"""Loads an sdf file.
:param filename: The filename.
:type filename: str
:return: A list of tuples, (SMILES, Name).
:rtype: list
"""
suppl = Chem.SDMolSupplier(filename)
data = []
duplicate_names = {}
missing_name_counter = 0
mol_obj_counter = 0
name_list = []
for mol in suppl:
# Convert mols to smiles. That's what the rest of the program is
# designed to deal with.
smiles = Chem.MolToSmiles(mol, isomericSmiles=True, canonical=True)
try:
name = mol.GetProp("_Name")
except:
name = ""
# Handle unnamed ligands
if name == "":
Utils.log(
"\tUntitled ligand for the {} molecule in the input SDF".format(
mol_obj_counter
)
)
name = "untitled_{}_molnum_{}".format(missing_name_counter, mol_obj_counter)
Utils.log("\tNaming that ligand {}".format(name))
Utils.log("\tAll associated files will be referred to with this name")
missing_name_counter += 1
# Handle duplicate ligands in same list.
if name in name_list:
# If multiple names.
if name in list(duplicate_names.keys()):
duplicate_names[name] = duplicate_names[name] + 1
new_name = "{}_copy_{}".format(name, duplicate_names[name])
Utils.log(
"\nMultiple entries with the ligand name: {}".format(name)
)
Utils.log(
"\tThe version of the ligand for the {} molecule in the SDF file will be retitled {}".format(
mol_obj_counter, new_name
)
)
Utils.log(
"\tAll associated files will be referred to with this name"
)
name = new_name
else:
duplicate_names[name] = 2
new_name = "{}_copy_{}".format(name, duplicate_names[name])
Utils.log(
"\nMultiple entries with the ligand name: {}".format(name)
)
Utils.log(
"\tThe version of the ligand for the {} molecule in the SDF file will be retitled {}".format(
mol_obj_counter, new_name
)
)
Utils.log(
"\tAll associated files will be referred to with this name"
)
name = new_name
mol_obj_counter += 1
name_list.append(name)
# SDF files may also contain properties. Get those as well.
try:
properties = mol.GetPropsAsDict()
except:
properties = {}
if smiles != "":
data.append((smiles, name, properties))
return data
| 6,751 | 34.166667 | 117 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/IO/__init__.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# gypsum_dl/gypsum_dl/Steps/IO/
# Including the below allows other programs to import functions from
# gypsum-DL.
import sys
import os
current_dir = os.path.dirname(os.path.realpath(__file__))
Steps = os.path.dirname(current_dir)
gypsum_gypsum_dir = os.path.dirname(Steps)
gypsum_top_dir = os.path.dirname(gypsum_gypsum_dir)
sys.path.extend([current_dir, Steps, gypsum_gypsum_dir, gypsum_top_dir])
from gypsum_dl.Steps.IO.ProcessOutput import proccess_output
from gypsum_dl.Steps.IO.LoadFiles import load_smiles_file
from gypsum_dl.Steps.IO.LoadFiles import load_sdf_file
| 1,157 | 35.1875 | 74 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Steps/IO/SaveToSDF.py | # Copyright 2018 Jacob D. Durrant
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Saves output files to SDF.
"""
import __future__
import os
import gypsum_dl.Utils as Utils
try:
from rdkit import Chem
except:
Utils.exception("You need to install rdkit and its dependencies.")
def save_to_sdf(contnrs, params, separate_output_files, output_folder):
"""Saves the 3D models to the disk as an SDF file.
:param contnrs: A list of containers (MolContainer.MolContainer).
:type contnrs: list
:param params: The parameters.
:type params: dict
:param separate_output_files: Whether save each molecule to a different
file.
:type separate_output_files: bool
:param output_folder: The output folder.
:type output_folder: str
"""
# Save an empty molecule with the parameters.
if separate_output_files == False:
w = Chem.SDWriter(output_folder + os.sep + "gypsum_dl_success.sdf")
else:
w = Chem.SDWriter(output_folder + os.sep + "gypsum_dl_params.sdf")
m = Chem.Mol()
m.SetProp("_Name", "EMPTY MOLECULE DESCRIBING GYPSUM-DL PARAMETERS")
for param in params:
m.SetProp(param, str(params[param]))
w.write(m)
if separate_output_files == True:
w.flush()
w.close()
# Also save the file or files containing the output molecules.
Utils.log("Saving molecules associated with...")
for i, contnr in enumerate(contnrs):
# Add the container properties to the rdkit_mol object so they get
# written to the SDF file.
contnr.add_container_properties()
# Let the user know which molecule you're on.
Utils.log("\t" + contnr.orig_smi)
# Save the file(s).
if separate_output_files == True:
# sdf_file = "{}{}__{}.pdb".format(output_folder + os.sep, slug(name), conformer_counter)
sdf_file = "{}{}__input{}.sdf".format(
output_folder + os.sep,
Utils.slug(contnr.name),
contnr.contnr_idx_orig + 1,
)
w = Chem.SDWriter(sdf_file)
# w = Chem.SDWriter(output_folder + os.sep + "output." + str(i + 1) + ".sdf")
for m in contnr.mols:
m.load_conformers_into_rdkit_mol()
w.write(m.rdkit_mol)
if separate_output_files == True:
w.flush()
w.close()
if separate_output_files == False:
w.flush()
w.close()
| 2,963 | 31.217391 | 101 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/resonance.py | # -*- coding: utf-8 -*-
"""
molvs.resonance
~~~~~~~~~~~~~~~
Resonance (mesomeric) transformations.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
from rdkit import Chem
log = logging.getLogger(__name__)
MAX_STRUCTURES = 1000
class ResonanceEnumerator(object):
"""Simple wrapper around RDKit ResonanceMolSupplier.
"""
def __init__(self, kekule_all=False, allow_incomplete_octets=False, unconstrained_cations=False,
unconstrained_anions=False, allow_charge_separation=False, max_structures=MAX_STRUCTURES):
"""
:param bool allow_incomplete_octets: include resonance structures whose octets are less complete than the the most octet-complete structure.
:param bool allow_charge_separation: include resonance structures featuring charge separation also when uncharged resonance structures exist.
:param bool kekule_all: enumerate all possible degenerate Kekule resonance structures (the default is to include just one).
:param bool unconstrained_cations: if False positively charged atoms left and right of N with an incomplete octet are acceptable only if the conjugated group has a positive total formal charge.
:param bool unconstrained_anions: if False, negatively charged atoms left of N are acceptable only if the conjugated group has a negative total formal charge.
:param int max_structures: Maximum number of resonance forms.
"""
self.kekule_all = kekule_all
self.allow_incomplete_octets = allow_incomplete_octets
self.unconstrained_cations = unconstrained_cations
self.unconstrained_anions = unconstrained_anions
self.allow_charge_separation = allow_charge_separation
self.max_structures = max_structures
def __call__(self, mol):
"""Calling a ResonanceEnumerator instance like a function is the same as calling its enumerate(mol) method."""
return self.enumerate(mol)
def enumerate(self, mol):
"""Enumerate all possible resonance forms and return them as a list.
:param mol: The input molecule.
:type mol: rdkit.Chem.rdchem.Mol
:return: A list of all possible resonance forms of the molecule.
:rtype: list of rdkit.Chem.rdchem.Mol
"""
flags = 0
if self.kekule_all:
flags = flags | Chem.KEKULE_ALL
if self.allow_incomplete_octets:
flags = flags | Chem.ALLOW_INCOMPLETE_OCTETS
if self.allow_charge_separation:
flags = flags | Chem.ALLOW_CHARGE_SEPARATION
if self.unconstrained_anions:
flags = flags | Chem.UNCONSTRAINED_ANIONS
if self.unconstrained_cations:
flags = flags | Chem.UNCONSTRAINED_CATIONS
results = []
for result in Chem.ResonanceMolSupplier(mol, flags=flags, maxStructs=self.max_structures):
# This seems necessary? ResonanceMolSupplier only does a partial sanitization
Chem.SanitizeMol(result)
results.append(result)
return results
# Potentially interesting: getNumConjGrps(), getBondConjGrpIdx() and getAtomConjGrpIdx()
def enumerate_resonance_smiles(smiles):
"""Return a set of resonance forms as SMILES strings, given a SMILES string.
:param smiles: A SMILES string.
:returns: A set containing SMILES strings for every possible resonance form.
:rtype: set of strings.
"""
mol = Chem.MolFromSmiles(smiles)
#Chem.SanitizeMol(mol) # MolFromSmiles does Sanitize by default
mesomers = ResonanceEnumerator().enumerate(mol)
return {Chem.MolToSmiles(m, isomericSmiles=True) for m in mesomers}
| 3,773 | 40.021739 | 201 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/charge.py | # -*- coding: utf-8 -*-
"""
molvs.charge
~~~~~~~~~~~~
This module implements tools for manipulating charges on molecules. In particular, :class:`~molvs.charge.Reionizer`,
which competitively reionizes acids such that the strongest acids ionize first, and :class:`~molvs.charge.Uncharger`,
which attempts to neutralize ionized acids and bases on a molecule.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import copy
import logging
from rdkit import Chem
from .utils import memoized_property
log = logging.getLogger(__name__)
class AcidBasePair(object):
"""An acid and its conjugate base, defined by SMARTS.
A strength-ordered list of AcidBasePairs can be used to ensure the strongest acids in a molecule ionize first.
"""
def __init__(self, name, acid, base):
"""Initialize an AcidBasePair with the following parameters:
:param string name: A name for this AcidBasePair.
:param string acid: SMARTS pattern for the protonated acid.
:param string base: SMARTS pattern for the conjugate ionized base.
"""
log.debug('Initializing AcidBasePair: %s', name)
self.name = name
self.acid_str = acid
self.base_str = base
@memoized_property
def acid(self):
log.debug('Loading AcidBasePair acid: %s', self.name)
return Chem.MolFromSmarts(self.acid_str)
@memoized_property
def base(self):
log.debug('Loading AcidBasePair base: %s', self.name)
return Chem.MolFromSmarts(self.base_str)
def __repr__(self):
return 'AcidBasePair({!r}, {!r}, {!r})'.format(self.name, self.acid_str, self.base_str)
def __str__(self):
return self.name
#: The default list of AcidBasePairs, sorted from strongest to weakest. This list is derived from the Food and Drug
#: Administration Substance Registration System Standard Operating Procedure guide.
ACID_BASE_PAIRS = (
AcidBasePair('-OSO3H', 'OS(=O)(=O)[OH]', 'OS(=O)(=O)[O-]'),
AcidBasePair('–SO3H', '[!O]S(=O)(=O)[OH]', '[!O]S(=O)(=O)[O-]'),
AcidBasePair('-OSO2H', 'O[SD3](=O)[OH]', 'O[SD3](=O)[O-]'),
AcidBasePair('-SO2H', '[!O][SD3](=O)[OH]', '[!O][SD3](=O)[O-]'),
AcidBasePair('-OPO3H2', 'OP(=O)([OH])[OH]', 'OP(=O)([OH])[O-]'),
AcidBasePair('-PO3H2', '[!O]P(=O)([OH])[OH]', '[!O]P(=O)([OH])[O-]'),
AcidBasePair('-CO2H', 'C(=O)[OH]', 'C(=O)[O-]'),
AcidBasePair('thiophenol', 'c[SH]', 'c[S-]'),
AcidBasePair('(-OPO3H)-', 'OP(=O)([O-])[OH]', 'OP(=O)([O-])[O-]'),
AcidBasePair('(-PO3H)-', '[!O]P(=O)([O-])[OH]', '[!O]P(=O)([O-])[O-]'),
AcidBasePair('phthalimide', 'O=C2c1ccccc1C(=O)[NH]2', 'O=C2c1ccccc1C(=O)[N-]2'),
AcidBasePair('CO3H (peracetyl)', 'C(=O)O[OH]', 'C(=O)O[O-]'),
AcidBasePair('alpha-carbon-hydrogen-nitro group', 'O=N(O)[CH]', 'O=N(O)[C-]'),
AcidBasePair('-SO2NH2', 'S(=O)(=O)[NH2]', 'S(=O)(=O)[NH-]'),
AcidBasePair('-OBO2H2', 'OB([OH])[OH]', 'OB([OH])[O-]'),
AcidBasePair('-BO2H2', '[!O]B([OH])[OH]', '[!O]B([OH])[O-]'),
AcidBasePair('phenol', 'c[OH]', 'c[O-]'),
AcidBasePair('SH (aliphatic)', 'C[SH]', 'C[S-]'),
AcidBasePair('(-OBO2H)-', 'OB([O-])[OH]', 'OB([O-])[O-]'),
AcidBasePair('(-BO2H)-', '[!O]B([O-])[OH]', '[!O]B([O-])[O-]'),
AcidBasePair('cyclopentadiene', 'C1=CC=C[CH2]1', 'c1ccc[cH-]1'),
AcidBasePair('-CONH2', 'C(=O)[NH2]', 'C(=O)[NH-]'),
AcidBasePair('imidazole', 'c1cnc[nH]1', 'c1cnc[n-]1'),
AcidBasePair('-OH (aliphatic alcohol)', '[CX4][OH]', '[CX4][O-]'),
AcidBasePair('alpha-carbon-hydrogen-keto group', 'O=C([!O])[C!H0+0]', 'O=C([!O])[C-]'),
AcidBasePair('alpha-carbon-hydrogen-acetyl ester group', 'OC(=O)[C!H0+0]', 'OC(=O)[C-]'),
AcidBasePair('sp carbon hydrogen', 'C#[CH]', 'C#[C-]'),
AcidBasePair('alpha-carbon-hydrogen-sulfone group', 'CS(=O)(=O)[C!H0+0]', 'CS(=O)(=O)[C-]'),
AcidBasePair('alpha-carbon-hydrogen-sulfoxide group', 'C[SD3](=O)[C!H0+0]', 'C[SD3](=O)[C-]'),
AcidBasePair('-NH2', '[CX4][NH2]', '[CX4][NH-]'),
AcidBasePair('benzyl hydrogen', 'c[CX4H2]', 'c[CX3H-]'),
AcidBasePair('sp2-carbon hydrogen', '[CX3]=[CX3!H0+0]', '[CX3]=[CX2-]'),
AcidBasePair('sp3-carbon hydrogen', '[CX4!H0+0]', '[CX3-]'),
)
class ChargeCorrection(object):
"""An atom that should have a certain charge applied, defined by a SMARTS pattern."""
def __init__(self, name, smarts, charge):
"""Initialize a ChargeCorrection with the following parameters:
:param string name: A name for this ForcedAtomCharge.
:param string smarts: SMARTS pattern to match. Charge is applied to the first atom.
:param int charge: The charge to apply.
"""
log.debug('Initializing ChargeCorrection: %s', name)
self.name = name
self.smarts_str = smarts
self.charge = charge
@memoized_property
def smarts(self):
log.debug('Loading ChargeCorrection smarts: %s', self.name)
return Chem.MolFromSmarts(self.smarts_str)
def __repr__(self):
return 'ChargeCorrection({!r}, {!r}, {!r})'.format(self.name, self.smarts_str, self.charge)
def __str__(self):
return self.name
#: The default list of ChargeCorrections.
CHARGE_CORRECTIONS = (
ChargeCorrection('[Li,Na,K]', '[Li,Na,K;X0+0]', 1),
ChargeCorrection('[Mg,Ca]', '[Mg,Ca;X0+0]', 2),
ChargeCorrection('[Cl]', '[Cl;X0+0]', -1),
# TODO: Extend to other incorrectly charged atoms
)
class Reionizer(object):
"""A class to fix charges and reionize a molecule such that the strongest acids ionize first."""
def __init__(self, acid_base_pairs=ACID_BASE_PAIRS, charge_corrections=CHARGE_CORRECTIONS):
"""Initialize a Reionizer with the following parameter:
:param acid_base_pairs: A list of :class:`AcidBasePairs <molvs.charge.AcidBasePair>` to reionize, sorted from
strongest to weakest.
:param charge_corrections: A list of :class:`ChargeCorrections <molvs.charge.ChargeCorrection>`.
"""
log.debug('Initializing Reionizer')
self.acid_base_pairs = acid_base_pairs
self.charge_corrections = charge_corrections
def __call__(self, mol):
"""Calling a Reionizer instance like a function is the same as calling its reionize(mol) method."""
return self.reionize(mol)
def reionize(self, mol):
"""Enforce charges on certain atoms, then perform competitive reionization.
First, charge corrections are applied to ensure, for example, that free metals are correctly ionized. Then, if
a molecule with multiple acid groups is partially ionized, ensure the strongest acids ionize first.
The algorithm works as follows:
- Use SMARTS to find the strongest protonated acid and the weakest ionized acid.
- If the ionized acid is weaker than the protonated acid, swap proton and repeat.
:param mol: The molecule to reionize.
:type mol: rdkit.Chem.rdchem.Mol
:return: The reionized molecule.
:rtype: rdkit.Chem.rdchem.Mol
"""
log.debug('Running Reionizer')
start_charge = Chem.GetFormalCharge(mol)
# Apply forced charge corrections
for cc in self.charge_corrections:
for match in mol.GetSubstructMatches(cc.smarts):
atom = mol.GetAtomWithIdx(match[0])
log.info('Applying charge correction %s (%s %+d)', cc.name, atom.GetSymbol(), cc.charge)
atom.SetFormalCharge(cc.charge)
current_charge = Chem.GetFormalCharge(mol)
charge_diff = Chem.GetFormalCharge(mol) - start_charge
# If molecule is now neutral, assume everything is now fixed
# But otherwise, if charge has become more positive, look for additional protonated acid groups to ionize
if not current_charge == 0:
while charge_diff > 0:
ppos, poccur = self._strongest_protonated(mol)
if ppos is None:
break
log.info('Ionizing %s to balance previous charge corrections', self.acid_base_pairs[ppos].name)
patom = mol.GetAtomWithIdx(poccur[-1])
patom.SetFormalCharge(patom.GetFormalCharge() - 1)
if patom.GetNumExplicitHs() > 0:
patom.SetNumExplicitHs(patom.GetNumExplicitHs() - 1)
# else:
patom.UpdatePropertyCache()
charge_diff -= 1
already_moved = set()
while True:
ppos, poccur = self._strongest_protonated(mol)
ipos, ioccur = self._weakest_ionized(mol)
if ioccur and poccur and ppos < ipos:
if poccur[-1] == ioccur[-1]:
# Bad! H wouldn't be moved, resulting in infinite loop.
log.warning('Aborted reionization due to unexpected situation')
break
key = tuple(sorted([poccur[-1], ioccur[-1]]))
if key in already_moved:
log.warning('Aborting reionization to avoid infinite loop due to it being ambiguous where to put a Hydrogen')
break
already_moved.add(key)
log.info('Moved proton from %s to %s', self.acid_base_pairs[ppos].name, self.acid_base_pairs[ipos].name)
# Remove hydrogen from strongest protonated
patom = mol.GetAtomWithIdx(poccur[-1])
patom.SetFormalCharge(patom.GetFormalCharge() - 1)
# If no implicit Hs to autoremove, and at least 1 explicit H to remove, reduce explicit count by 1
if patom.GetNumImplicitHs() == 0 and patom.GetNumExplicitHs() > 0:
patom.SetNumExplicitHs(patom.GetNumExplicitHs() - 1)
# TODO: Remove any chiral label on patom?
patom.UpdatePropertyCache()
# Add hydrogen to weakest ionized
iatom = mol.GetAtomWithIdx(ioccur[-1])
iatom.SetFormalCharge(iatom.GetFormalCharge() + 1)
# Increase explicit H count if no implicit, or aromatic N or P, or non default valence state
if (iatom.GetNoImplicit() or
((patom.GetAtomicNum() == 7 or patom.GetAtomicNum() == 15) and patom.GetIsAromatic()) or
iatom.GetTotalValence() not in list(Chem.GetPeriodicTable().GetValenceList(iatom.GetAtomicNum()))):
iatom.SetNumExplicitHs(iatom.GetNumExplicitHs() + 1)
iatom.UpdatePropertyCache()
else:
break
# TODO: Canonical ionization position if multiple equivalent positions?
Chem.SanitizeMol(mol)
return mol
def _strongest_protonated(self, mol):
for position, pair in enumerate(self.acid_base_pairs):
for occurrence in mol.GetSubstructMatches(pair.acid):
return position, occurrence
return None, None
def _weakest_ionized(self, mol):
for position, pair in enumerate(reversed(self.acid_base_pairs)):
for occurrence in mol.GetSubstructMatches(pair.base):
return len(self.acid_base_pairs) - position - 1, occurrence
return None, None
class Uncharger(object):
"""Class for neutralizing charges in a molecule.
This class uncharges molecules by adding and/or removing hydrogens. In cases where there is a positive charge that
is not neutralizable, any corresponding negative charge is also preserved.
"""
def __init__(self, acid_base_pairs=ACID_BASE_PAIRS):
log.debug('Initializing Uncharger')
self.acid_base_pairs = acid_base_pairs
self.nitro = Chem.MolFromSmarts('[!#8][NX3+](=O)[O-]')
def __call__(self, mol):
"""Calling an Uncharger instance like a function is the same as calling its uncharge(mol) method."""
return self.uncharge(mol)
def uncharge(self, mol):
"""Neutralize molecule by adding/removing hydrogens.
:param mol: The molecule to uncharge.
:type mol: rdkit.Chem.rdchem.Mol
:return: The uncharged molecule.
:rtype: rdkit.Chem.rdchem.Mol
"""
log.debug('Running Uncharger')
mol = copy.deepcopy(mol)
# Neutralize positive charges
pos_remainder = 0
neg_count = 0
for atom in mol.GetAtoms():
# Remove hydrogen from positive atoms and reduce formal change until neutral or no more hydrogens
while atom.GetFormalCharge() > 0 and atom.GetNumExplicitHs() > 0:
atom.SetNumExplicitHs(atom.GetNumExplicitHs() - 1)
atom.SetFormalCharge(atom.GetFormalCharge() - 1)
log.info('Removed positive charge')
chg = atom.GetFormalCharge()
if chg > 0:
# Record number of non-neutralizable positive charges
pos_remainder += chg
elif chg < 0:
# Record total number of negative charges
neg_count += -chg
# Choose negative charges to leave in order to balance non-neutralizable positive charges
neg_skip = self._get_neg_skip(mol, pos_remainder)
# Neutralize remaining negative charges
for atom in mol.GetAtoms():
log.info(atom.GetIdx())
if atom.GetIdx() in neg_skip:
continue
# Make sure to stop when neg_count <= pos_remainder, as it is possible that neg_skip is not large enough
while atom.GetFormalCharge() < 0 and neg_count > pos_remainder:
atom.SetNumExplicitHs(atom.GetNumExplicitHs() + 1)
atom.SetFormalCharge(atom.GetFormalCharge() + 1)
neg_count -= 1
log.info('Removed negative charge')
return mol
def _get_neg_skip(self, mol, pos_count):
"""Get negatively charged atoms to skip (up to pos_count)."""
neg_skip = set()
if pos_count:
# Get negative oxygens in charge-separated nitro groups TODO: Any other special cases to skip?
for occurrence in mol.GetSubstructMatches(self.nitro):
neg_skip.add(occurrence[-1])
if len(neg_skip) >= pos_count:
return neg_skip
# Get strongest ionized acids
for position, pair in enumerate(self.acid_base_pairs):
for occurrence in mol.GetSubstructMatches(pair.base):
neg_skip.add(occurrence[-1])
if len(neg_skip) >= pos_count:
return neg_skip
return neg_skip
| 14,678 | 43.213855 | 129 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/tautomer.py | # -*- coding: utf-8 -*-
"""
molvs.tautomer
~~~~~~~~~~~~~~
This module contains tools for enumerating tautomers and determining a canonical tautomer.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import copy
import logging
from rdkit import Chem
from rdkit.Chem.rdchem import BondDir, BondStereo, BondType
from .utils import memoized_property, pairwise
log = logging.getLogger(__name__)
class TautomerTransform(object):
"""Rules to transform one tautomer to another.
Each TautomerTransform is defined by a SMARTS pattern where the transform involves moving a hydrogen from the first
atom in the pattern to the last atom in the pattern. By default, alternating single and double bonds along the
pattern are swapped accordingly to account for the hydrogen movement. If necessary, the transform can instead define
custom resulting bond orders and also resulting atom charges.
"""
BONDMAP = {'-': BondType.SINGLE, '=': BondType.DOUBLE, '#': BondType.TRIPLE, ':': BondType.AROMATIC}
CHARGEMAP = {'+': 1, '0': 0, '-': -1}
def __init__(self, name, smarts, bonds=(), charges=(), radicals=()):
"""Initialize a TautomerTransform with a name, SMARTS pattern and optional bonds and charges.
The SMARTS pattern match is applied to a Kekule form of the molecule, so use explicit single and double bonds
rather than aromatic.
Specify custom bonds as a string of ``-``, ``=``, ``#``, ``:`` for single, double, triple and aromatic bonds
respectively. Specify custom charges as ``+``, ``0``, ``-`` for +1, 0 and -1 charges respectively.
:param string name: A name for this TautomerTransform.
:param string smarts: SMARTS pattern to match for the transform.
:param string bonds: Optional specification for the resulting bonds.
:param string charges: Optional specification for the resulting charges on the atoms.
"""
self.name = name
self.tautomer_str = smarts
self.bonds = [self.BONDMAP[b] for b in bonds]
self.charges = [self.CHARGEMAP[b] for b in charges]
# TODO: Raise error (ValueError?) if bonds and charges lists are not the correct length
@memoized_property
def tautomer(self):
return Chem.MolFromSmarts(self.tautomer_str)
def __repr__(self):
return 'TautomerTransform({!r}, {!r}, {!r}, {!r})'.format(self.name, self.tautomer_str, self.bonds, self.charges)
def __str__(self):
return self.name
class TautomerScore(object):
"""A substructure defined by SMARTS and its score contribution to determine the canonical tautomer."""
def __init__(self, name, smarts, score):
"""Initialize a TautomerScore with a name, SMARTS pattern and score.
:param name: A name for this TautomerScore.
:param smarts: SMARTS pattern to match a substructure.
:param score: The score to assign for this substructure.
"""
self.name = name
self.smarts_str = smarts
self.score = score
@memoized_property
def smarts(self):
return Chem.MolFromSmarts(self.smarts_str)
def __repr__(self):
return 'TautomerScore({!r}, {!r}, {!r})'.format(self.name, self.smarts_str, self.score)
def __str__(self):
return self.name
#: The default list of TautomerTransforms.
TAUTOMER_TRANSFORMS = (
TautomerTransform('1,3 (thio)keto/enol f', '[CX4!H0]-[C]=[O,S,Se,Te;X1]'),
TautomerTransform('1,3 (thio)keto/enol r', '[O,S,Se,Te;X2!H0]-[C]=[C]'),
TautomerTransform('1,5 (thio)keto/enol f', '[CX4,NX3;!H0]-[C]=[C][CH0]=[O,S,Se,Te;X1]'),
TautomerTransform('1,5 (thio)keto/enol r', '[O,S,Se,Te;X2!H0]-[CH0]=[C]-[C]=[C,N]'),
TautomerTransform('aliphatic imine f', '[CX4!H0]-[C]=[NX2]'),
TautomerTransform('aliphatic imine r', '[NX3!H0]-[C]=[CX3]'),
TautomerTransform('special imine f', '[N!H0]-[C]=[CX3R0]'),
TautomerTransform('special imine r', '[CX4!H0]-[c]=[n]'),
TautomerTransform('1,3 aromatic heteroatom H shift f', '[#7!H0]-[#6R1]=[O,#7X2]'),
TautomerTransform('1,3 aromatic heteroatom H shift r', '[O,#7;!H0]-[#6R1]=[#7X2]'),
TautomerTransform('1,3 heteroatom H shift', '[#7,S,O,Se,Te;!H0]-[#7X2,#6,#15]=[#7,#16,#8,Se,Te]'),
TautomerTransform('1,5 aromatic heteroatom H shift', '[#7,#16,#8;!H0]-[#6,#7]=[#6]-[#6,#7]=[#7,#16,#8;H0]'),
TautomerTransform('1,5 aromatic heteroatom H shift f', '[#7,#16,#8,Se,Te;!H0]-[#6,nX2]=[#6,nX2]-[#6,#7X2]=[#7X2,S,O,Se,Te]'),
TautomerTransform('1,5 aromatic heteroatom H shift r', '[#7,S,O,Se,Te;!H0]-[#6,#7X2]=[#6,nX2]-[#6,nX2]=[#7,#16,#8,Se,Te]'),
TautomerTransform('1,7 aromatic heteroatom H shift f', '[#7,#8,#16,Se,Te;!H0]-[#6,#7X2]=[#6,#7X2]-[#6,#7X2]=[#6]-[#6,#7X2]=[#7X2,S,O,Se,Te,CX3]'),
TautomerTransform('1,7 aromatic heteroatom H shift r', '[#7,S,O,Se,Te,CX4;!H0]-[#6,#7X2]=[#6]-[#6,#7X2]=[#6,#7X2]-[#6,#7X2]=[NX2,S,O,Se,Te]'),
TautomerTransform('1,9 aromatic heteroatom H shift f', '[#7,O;!H0]-[#6,#7X2]=[#6,#7X2]-[#6,#7X2]=[#6,#7X2]-[#6,#7X2]=[#6,#7X2]-[#6,#7X2]=[#7,O]'),
TautomerTransform('1,11 aromatic heteroatom H shift f', '[#7,O;!H0]-[#6,nX2]=[#6,nX2]-[#6,nX2]=[#6,nX2]-[#6,nX2]=[#6,nX2]-[#6,nX2]=[#6,nX2]-[#6,nX2]=[#7X2,O]'),
TautomerTransform('furanone f', '[O,S,N;!H0]-[#6r5]=[#6X3r5;$([#6]([#6r5])=[#6r5])]'),
TautomerTransform('furanone r', '[#6r5!H0;$([#6]([#6r5])[#6r5])]-[#6r5]=[O,S,N]'),
TautomerTransform('keten/ynol f', '[C!H0]=[C]=[O,S,Se,Te;X1]', bonds='#-'),
TautomerTransform('keten/ynol r', '[O,S,Se,Te;!H0X2]-[C]#[C]', bonds='=='),
TautomerTransform('ionic nitro/aci-nitro f', '[C!H0]-[N+;$([N][O-])]=[O]'),
TautomerTransform('ionic nitro/aci-nitro r', '[O!H0]-[N+;$([N][O-])]=[C]'),
TautomerTransform('oxim/nitroso f', '[O!H0]-[N]=[C]'),
TautomerTransform('oxim/nitroso r', '[C!H0]-[N]=[O]'),
TautomerTransform('oxim/nitroso via phenol f', '[O!H0]-[N]=[C]-[C]=[C]-[C]=[OH0]'),
TautomerTransform('oxim/nitroso via phenol r', '[O!H0]-[c]=[c]-[c]=[c]-[N]=[OH0]'),
TautomerTransform('cyano/iso-cyanic acid f', '[O!H0]-[C]#[N]', bonds='=='),
TautomerTransform('cyano/iso-cyanic acid r', '[N!H0]=[C]=[O]', bonds='#-'),
# TautomerTransform('formamidinesulfinic acid f', '[O,N;!H0]-[C]=[S,Se,Te]=[O]', bonds='=--'), # TODO: WAT!?
# TautomerTransform('formamidinesulfinic acid r', '[O!H0]-[S,Se,Te]-[C]=[O,N]', bonds='=--'),
TautomerTransform('isocyanide f', '[C-0!H0]#[N+0]', bonds='#', charges='-+'),
TautomerTransform('isocyanide r', '[N+!H0]#[C-]', bonds='#', charges='-+'),
TautomerTransform('phosphonic acid f', '[OH]-[PH0]', bonds='='),
TautomerTransform('phosphonic acid r', '[PH]=[O]', bonds='-'),
)
#: The default list of TautomerScores.
TAUTOMER_SCORES = (
TautomerScore('benzoquinone', '[#6]1([#6]=[#6][#6]([#6]=[#6]1)=,:[N,S,O])=,:[N,S,O]', 25),
TautomerScore('oxim', '[#6]=[N][OH]', 4),
TautomerScore('C=O', '[#6]=,:[#8]', 2),
TautomerScore('N=O', '[#7]=,:[#8]', 2),
TautomerScore('P=O', '[#15]=,:[#8]', 2),
TautomerScore('C=hetero', '[#6]=[!#1;!#6]', 1),
TautomerScore('methyl', '[CX4H3]', 1),
TautomerScore('guanidine terminal=N', '[#7][#6](=[NR0])[#7H0]', 1),
TautomerScore('guanidine endocyclic=N', '[#7;R][#6;R]([N])=[#7;R]', 2),
TautomerScore('aci-nitro', '[#6]=[N+]([O-])[OH]', -4),
)
#: The default value for the maximum number of tautomers to enumerate, a limit to prevent combinatorial explosion.
MAX_TAUTOMERS = 1000
class TautomerCanonicalizer(object):
"""
"""
def __init__(self, transforms=TAUTOMER_TRANSFORMS, scores=TAUTOMER_SCORES, max_tautomers=MAX_TAUTOMERS):
"""
:param transforms: A list of TautomerTransforms to use to enumerate tautomers.
:param scores: A list of TautomerScores to use to choose the canonical tautomer.
:param max_tautomers: The maximum number of tautomers to enumerate, a limit to prevent combinatorial explosion.
"""
self.transforms = transforms
self.scores = scores
self.max_tautomers = max_tautomers
def __call__(self, mol):
"""Calling a TautomerCanonicalizer instance like a function is the same as calling its canonicalize(mol) method."""
return self.canonicalize(mol)
def canonicalize(self, mol):
"""Return a canonical tautomer by enumerating and scoring all possible tautomers.
:param mol: The input molecule.
:type mol: rdkit.Chem.rdchem.Mol
:return: The canonical tautomer.
:rtype: rdkit.Chem.rdchem.Mol
"""
# TODO: Overload the mol parameter to pass a list of pre-enumerated tautomers
tautomers = self._enumerate_tautomers(mol)
if len(tautomers) == 1:
return tautomers[0]
# Calculate score for each tautomer
highest = None
for t in tautomers:
smiles = Chem.MolToSmiles(t, isomericSmiles=True)
log.debug('Tautomer: %s', smiles)
score = 0
# Add aromatic ring scores
ssr = Chem.GetSymmSSSR(t)
for ring in ssr:
btypes = {t.GetBondBetweenAtoms(*pair).GetBondType() for pair in pairwise(ring)}
elements = {t.GetAtomWithIdx(idx).GetAtomicNum() for idx in ring}
if btypes == {BondType.AROMATIC}:
log.debug('Score +100 (aromatic ring)')
score += 100
if elements == {6}:
log.debug('Score +150 (carbocyclic aromatic ring)')
score += 150
# Add SMARTS scores
for tscore in self.scores:
for match in t.GetSubstructMatches(tscore.smarts):
log.debug('Score %+d (%s)', tscore.score, tscore.name)
score += tscore.score
# Add (P,S,Se,Te)-H scores
for atom in t.GetAtoms():
if atom.GetAtomicNum() in {15, 16, 34, 52}:
hs = atom.GetTotalNumHs()
if hs:
log.debug('Score %+d (%s-H bonds)', -hs, atom.GetSymbol())
score -= hs
# Set as highest if score higher or if score equal and smiles comes first alphabetically
if not highest or highest['score'] < score or (highest['score'] == score and smiles < highest['smiles']):
log.debug('New highest tautomer: %s (%s)', smiles, score)
highest = {'smiles': smiles, 'tautomer': t, 'score': score}
return highest['tautomer']
@memoized_property
def _enumerate_tautomers(self):
return TautomerEnumerator(self.transforms, self.max_tautomers)
class TautomerEnumerator(object):
"""
"""
def __init__(self, transforms=TAUTOMER_TRANSFORMS, max_tautomers=MAX_TAUTOMERS):
"""
:param transforms: A list of TautomerTransforms to use to enumerate tautomers.
:param max_tautomers: The maximum number of tautomers to enumerate (limit to prevent combinatorial explosion).
"""
self.transforms = transforms
self.max_tautomers = max_tautomers
def __call__(self, mol):
"""Calling a TautomerEnumerator instance like a function is the same as calling its enumerate(mol) method."""
return self.enumerate(mol)
def enumerate(self, mol):
"""Enumerate all possible tautomers and return them as a list.
:param mol: The input molecule.
:type mol: rdkit.Chem.rdchem.Mol
:return: A list of all possible tautomers of the molecule.
:rtype: list of rdkit.Chem.rdchem.Mol
"""
smiles = Chem.MolToSmiles(mol, isomericSmiles=True)
tautomers = {smiles: copy.deepcopy(mol)}
# Create a kekulized form of the molecule to match the SMARTS against
kekulized = copy.deepcopy(mol)
Chem.Kekulize(kekulized)
kekulized = {smiles: kekulized}
done = set()
while len(tautomers) < self.max_tautomers:
for tsmiles in sorted(tautomers):
if tsmiles in done:
continue
for transform in self.transforms:
for match in kekulized[tsmiles].GetSubstructMatches(transform.tautomer):
# log.debug('Matched rule: %s to %s for %s', transform.name, tsmiles, match)
# Create a copy of in the input molecule so we can modify it
# Use kekule form so bonds are explicitly single/double instead of aromatic
product = copy.deepcopy(kekulized[tsmiles])
# Remove a hydrogen from the first matched atom and add one to the last
first = product.GetAtomWithIdx(match[0])
last = product.GetAtomWithIdx(match[-1])
# log.debug('%s: H%s -> H%s' % (first.GetSymbol(), first.GetTotalNumHs(), first.GetTotalNumHs() - 1))
# log.debug('%s: H%s -> H%s' % (last.GetSymbol(), last.GetTotalNumHs(), last.GetTotalNumHs() + 1))
first.SetNumExplicitHs(max(0, first.GetTotalNumHs() - 1))
last.SetNumExplicitHs(last.GetTotalNumHs() + 1)
# Remove any implicit hydrogens from the first and last atoms now we have set the count explicitly
first.SetNoImplicit(True)
last.SetNoImplicit(True)
# Adjust bond orders
for bi, pair in enumerate(pairwise(match)):
if transform.bonds:
# Set the resulting bond types as manually specified in the transform
# log.debug('%s-%s: %s -> %s' % (product.GetAtomWithIdx(pair[0]).GetSymbol(), product.GetAtomWithIdx(pair[1]).GetSymbol(), product.GetBondBetweenAtoms(*pair).GetBondType(), transform.bonds[bi]))
product.GetBondBetweenAtoms(*pair).SetBondType(transform.bonds[bi])
else:
# If no manually specified bond types, just swap single and double bonds
current_bond_type = product.GetBondBetweenAtoms(*pair).GetBondType()
product.GetBondBetweenAtoms(*pair).SetBondType(BondType.DOUBLE if current_bond_type == BondType.SINGLE else BondType.SINGLE)
# log.debug('%s-%s: %s -> %s' % (product.GetAtomWithIdx(pair[0]).GetSymbol(), product.GetAtomWithIdx(pair[1]).GetSymbol(), current_bond_type, product.GetBondBetweenAtoms(*pair).GetBondType()))
# Adjust charges
if transform.charges:
for ci, idx in enumerate(match):
atom = product.GetAtomWithIdx(idx)
# log.debug('%s: C%s -> C%s' % (atom.GetSymbol(), atom.GetFormalCharge(), atom.GetFormalCharge() + transform.charges[ci]))
atom.SetFormalCharge(atom.GetFormalCharge() + transform.charges[ci])
try:
Chem.SanitizeMol(product)
smiles = Chem.MolToSmiles(product, isomericSmiles=True)
log.debug('Applied rule: %s to %s', transform.name, tsmiles)
if smiles not in tautomers:
log.debug('New tautomer produced: %s' % smiles)
kekulized_product = copy.deepcopy(product)
Chem.Kekulize(kekulized_product)
tautomers[smiles] = product
kekulized[smiles] = kekulized_product
else:
log.debug('Previous tautomer produced again: %s' % smiles)
except ValueError:
log.debug('ValueError Applying rule: %s', transform.name)
done.add(tsmiles)
if len(tautomers) == len(done):
break
else:
log.warning('Tautomer enumeration stopped at maximum %s', self.max_tautomers)
# Clean up stereochemistry
for tautomer in tautomers.values():
Chem.AssignStereochemistry(tautomer, force=True, cleanIt=True)
for bond in tautomer.GetBonds():
if bond.GetBondType() == BondType.DOUBLE and bond.GetStereo() > BondStereo.STEREOANY:
begin = bond.GetBeginAtomIdx()
end = bond.GetEndAtomIdx()
for othertautomer in tautomers.values():
if not othertautomer.GetBondBetweenAtoms(begin, end).GetBondType() == BondType.DOUBLE:
neighbours = tautomer.GetAtomWithIdx(begin).GetBonds() + tautomer.GetAtomWithIdx(end).GetBonds()
for otherbond in neighbours:
if otherbond.GetBondDir() in {BondDir.ENDUPRIGHT, BondDir.ENDDOWNRIGHT}:
otherbond.SetBondDir(BondDir.NONE)
Chem.AssignStereochemistry(tautomer, force=True, cleanIt=True)
log.debug('Removed stereochemistry from unfixed double bond')
break
return list(tautomers.values()) | 17,504 | 52.53211 | 226 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/errors.py | # -*- coding: utf-8 -*-
"""
molvs.errors
~~~~~~~~~~~~
This module contains exceptions that are raised by MolVS.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
class MolVSError(Exception):
pass
class StandardizeError(MolVSError):
pass
class ValidateError(MolVSError):
pass
class StopValidateError(ValidateError):
"""Called by Validations to stop any further validations from being performed."""
pass
| 497 | 15.6 | 85 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/standardize.py | # -*- coding: utf-8 -*-
"""
molvs.standardize
~~~~~~~~~~~~~~~~~
This module contains the main :class:`~molvs.standardize.Standardizer` class that can be used to perform all
standardization tasks, as well as convenience functions like :func:`~molvs.standardize.standardize_smiles` for common
standardization tasks.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import copy
import logging
from rdkit import Chem
from .metal import MetalDisconnector
from .fragment import PREFER_ORGANIC, LargestFragmentChooser, FragmentRemover
from .normalize import NORMALIZATIONS, MAX_RESTARTS, Normalizer
from .tautomer import TAUTOMER_TRANSFORMS, TAUTOMER_SCORES, MAX_TAUTOMERS, TautomerCanonicalizer, TautomerEnumerator
from .charge import ACID_BASE_PAIRS, CHARGE_CORRECTIONS, Reionizer, Uncharger
from .utils import memoized_property
log = logging.getLogger(__name__)
class Standardizer(object):
"""The main class for performing standardization of molecules and deriving parent molecules.
The primary usage is via the :meth:`~molvs.standardize.Standardizer.standardize` method::
s = Standardizer()
mol1 = Chem.MolFromSmiles('C1=CC=CC=C1')
mol2 = s.standardize(mol1)
There are separate methods to derive fragment, charge, tautomer, isotope and stereo parent molecules.
"""
def __init__(self, normalizations=NORMALIZATIONS, acid_base_pairs=ACID_BASE_PAIRS,
charge_corrections=CHARGE_CORRECTIONS, tautomer_transforms=TAUTOMER_TRANSFORMS,
tautomer_scores=TAUTOMER_SCORES, max_restarts=MAX_RESTARTS, max_tautomers=MAX_TAUTOMERS,
prefer_organic=PREFER_ORGANIC):
"""Initialize a Standardizer with optional custom parameters.
:param normalizations: A list of Normalizations to apply (default: :data:`~molvs.normalize.NORMALIZATIONS`).
:param acid_base_pairs: A list of AcidBasePairs for competitive reionization (default:
:data:`~molvs.charge.ACID_BASE_PAIRS`).
:param charge_corrections: A list of ChargeCorrections to apply (default:
:data:`~molvs.charge.CHARGE_CORRECTIONS`).
:param tautomer_transforms: A list of TautomerTransforms to apply (default:
:data:`~molvs.tautomer.TAUTOMER_TRANSFORMS`).
:param tautomer_scores: A list of TautomerScores used to determine canonical tautomer (default:
:data:`~molvs.tautomer.TAUTOMER_SCORES`).
:param max_restarts: The maximum number of times to attempt to apply the series of normalizations (default 200).
:param max_tautomers: The maximum number of tautomers to enumerate (default 1000).
:param prefer_organic: Whether to prioritize organic fragments when choosing fragment parent (default False).
"""
log.debug('Initializing Standardizer')
self.normalizations = normalizations
self.acid_base_pairs = acid_base_pairs
self.charge_corrections = charge_corrections
self.tautomer_transforms = tautomer_transforms
self.tautomer_scores = tautomer_scores
self.max_restarts = max_restarts
self.max_tautomers = max_tautomers
self.prefer_organic = prefer_organic
def __call__(self, mol):
"""Calling a Standardizer instance like a function is the same as calling its
:meth:`~molvs.standardize.Standardizer.standardize` method."""
return self.standardize(mol)
def standardize(self, mol):
"""Return a standardized version the given molecule.
The standardization process consists of the following stages: RDKit
:py:func:`~rdkit.Chem.rdmolops.RemoveHs`, RDKit :py:func:`~rdkit.Chem.rdmolops.SanitizeMol`,
:class:`~molvs.metal.MetalDisconnector`, :class:`~molvs.normalize.Normalizer`,
:class:`~molvs.charge.Reionizer`, RDKit :py:func:`~rdkit.Chem.rdmolops.AssignStereochemistry`.
:param mol: The molecule to standardize.
:type mol: rdkit.Chem.rdchem.Mol
:returns: The standardized molecule.
:rtype: rdkit.Chem.rdchem.Mol
"""
mol = copy.deepcopy(mol)
Chem.SanitizeMol(mol)
mol = Chem.RemoveHs(mol)
mol = self.disconnect_metals(mol)
mol = self.normalize(mol)
mol = self.reionize(mol)
Chem.AssignStereochemistry(mol, force=True, cleanIt=True)
# TODO: Check this removes symmetric stereocenters
return mol
def tautomer_parent(self, mol, skip_standardize=False):
"""Return the tautomer parent of a given molecule.
:param mol: The input molecule.
:type mol: rdkit.Chem.rdchem.Mol
:param bool skip_standardize: Set to True if mol has already been standardized.
:returns: The tautomer parent molecule.
:rtype: rdkit.Chem.rdchem.Mol
"""
if not skip_standardize:
mol = self.standardize(mol)
tautomer = self.canonicalize_tautomer(mol)
tautomer = self.standardize(tautomer)
return tautomer
def fragment_parent(self, mol, skip_standardize=False):
"""Return the fragment parent of a given molecule.
The fragment parent is the largest organic covalent unit in the molecule.
:param mol: The input molecule.
:type mol: rdkit.Chem.rdchem.Mol
:param bool skip_standardize: Set to True if mol has already been standardized.
:returns: The fragment parent molecule.
:rtype: rdkit.Chem.rdchem.Mol
"""
if not skip_standardize:
mol = self.standardize(mol)
# TODO: Consider applying FragmentRemover first to remove salts, solvents?
fragment = self.largest_fragment(mol)
return fragment
def stereo_parent(self, mol, skip_standardize=False):
"""Return the stereo parent of a given molecule.
The stereo parent has all stereochemistry information removed from tetrahedral centers and double bonds.
:param mol: The input molecule.
:type mol: rdkit.Chem.rdchem.Mol
:param bool skip_standardize: Set to True if mol has already been standardized.
:returns: The stereo parent molecule.
:rtype: rdkit.Chem.rdchem.Mol
"""
if not skip_standardize:
mol = self.standardize(mol)
else:
mol = copy.deepcopy(mol)
Chem.RemoveStereochemistry(mol)
return mol
def isotope_parent(self, mol, skip_standardize=False):
"""Return the isotope parent of a given molecule.
The isotope parent has all atoms replaced with the most abundant isotope for that element.
:param mol: The input molecule.
:type mol: rdkit.Chem.rdchem.Mol
:param bool skip_standardize: Set to True if mol has already been standardized.
:returns: The isotope parent molecule.
:rtype: rdkit.Chem.rdchem.Mol
"""
if not skip_standardize:
mol = self.standardize(mol)
else:
mol = copy.deepcopy(mol)
# Replace isotopes with common weight
for atom in mol.GetAtoms():
atom.SetIsotope(0)
return mol
def charge_parent(self, mol, skip_standardize=False):
"""Return the charge parent of a given molecule.
The charge parent is the uncharged version of the fragment parent.
:param mol: The input molecule.
:type mol: rdkit.Chem.rdchem.Mol
:param bool skip_standardize: Set to True if mol has already been standardized.
:returns: The charge parent molecule.
:rtype: rdkit.Chem.rdchem.Mol
"""
# TODO: All ionized acids and bases should be neutralised.
if not skip_standardize:
mol = self.standardize(mol)
fragment = self.fragment_parent(mol, skip_standardize=True)
if fragment:
uncharged = self.uncharge(fragment)
# During final standardization, the Reionizer ensures any remaining charges are in the right places
uncharged = self.standardize(uncharged)
return uncharged
def super_parent(self, mol, skip_standardize=False):
"""Return the super parent of a given molecule.
THe super parent is fragment, charge, isotope, stereochemistry and tautomer insensitive. From the input
molecule, the largest fragment is taken. This is uncharged and then isotope and stereochemistry information is
discarded. Finally, the canonical tautomer is determined and returned.
:param mol: The input molecule.
:type mol: rdkit.Chem.rdchem.Mol
:param bool skip_standardize: Set to True if mol has already been standardized.
:returns: The super parent molecule.
:rtype: rdkit.Chem.rdchem.Mol
"""
if not skip_standardize:
mol = self.standardize(mol)
# We don't need to get fragment parent, because the charge parent is the largest fragment
mol = self.charge_parent(mol, skip_standardize=True)
mol = self.isotope_parent(mol, skip_standardize=True)
mol = self.stereo_parent(mol, skip_standardize=True)
mol = self.tautomer_parent(mol, skip_standardize=True)
mol = self.standardize(mol)
return mol
def standardize_with_parents(self, mol):
""""""
standardized = self.standardize(mol)
tautomer = self.tautomer_parent(standardized, skip_standardize=True)
super = self.super_parent(standardized, skip_standardize=True)
# TODO: Add other parents - have optional argument to specify which are wanted
mols = {
'standardized': standardized,
'tautomer_parent': tautomer,
'super_parent': super
}
return mols
# TODO: All unique tautomers
# TODO: All unique fragments (each has to be standardized again?)
@memoized_property
def disconnect_metals(self):
"""
:returns: A callable :class:`~molvs.metal.MetalDisconnector` instance.
"""
return MetalDisconnector()
@memoized_property
def normalize(self):
"""
:returns: A callable :class:`~molvs.normalize.Normalizer` instance.
"""
return Normalizer(normalizations=self.normalizations, max_restarts=self.max_restarts)
@memoized_property
def reionize(self):
"""
:returns: A callable :class:`~molvs.charge.Reionizer` instance.
"""
return Reionizer(acid_base_pairs=self.acid_base_pairs, charge_corrections=self.charge_corrections)
@memoized_property
def uncharge(self):
"""
:returns: A callable :class:`~molvs.charge.Uncharger` instance.
"""
return Uncharger(acid_base_pairs=self.acid_base_pairs)
@memoized_property
def remove_fragments(self):
"""
:returns: A callable :class:`~molvs.fragment.FragmentRemover` instance.
"""
return FragmentRemover()
@memoized_property
def largest_fragment(self):
"""
:returns: A callable :class:`~molvs.fragment.LargestFragmentChooser` instance.
"""
return LargestFragmentChooser(prefer_organic=self.prefer_organic)
@memoized_property
def enumerate_tautomers(self):
"""
:returns: A callable :class:`~molvs.tautomer.TautomerEnumerator` instance.
"""
return TautomerEnumerator(transforms=self.tautomer_transforms, max_tautomers=self.max_tautomers)
@memoized_property
def canonicalize_tautomer(self):
"""
:returns: A callable :class:`~molvs.tautomer.TautomerCanonicalizer` instance.
"""
return TautomerCanonicalizer(transforms=self.tautomer_transforms, scores=self.tautomer_scores,
max_tautomers=self.max_tautomers)
def standardize_smiles(smiles):
"""Return a standardized canonical SMILES string given a SMILES string.
Note: This is a convenience function for quickly standardizing a single SMILES string. It is more efficient to use
the :class:`~molvs.standardize.Standardizer` class directly when working with many molecules or when custom options
are needed.
:param string smiles: The SMILES for the molecule.
:returns: The SMILES for the standardized molecule.
:rtype: string.
"""
# Skip sanitize as standardize does this anyway
mol = Chem.MolFromSmiles(smiles, sanitize=False)
mol = Standardizer().standardize(mol)
return Chem.MolToSmiles(mol, isomericSmiles=True)
def enumerate_tautomers_smiles(smiles):
"""Return a set of tautomers as SMILES strings, given a SMILES string.
:param smiles: A SMILES string.
:returns: A set containing SMILES strings for every possible tautomer.
:rtype: set of strings.
"""
# Skip sanitize as standardize does this anyway
mol = Chem.MolFromSmiles(smiles, sanitize=False)
mol = Standardizer().standardize(mol)
tautomers = TautomerEnumerator().enumerate(mol)
return {Chem.MolToSmiles(m, isomericSmiles=True) for m in tautomers}
def canonicalize_tautomer_smiles(smiles):
"""Return a standardized canonical tautomer SMILES string given a SMILES string.
Note: This is a convenience function for quickly standardizing and finding the canonical tautomer for a single
SMILES string. It is more efficient to use the :class:`~molvs.standardize.Standardizer` class directly when working
with many molecules or when custom options are needed.
:param string smiles: The SMILES for the molecule.
:returns: The SMILES for the standardize canonical tautomer.
:rtype: string.
"""
# Skip sanitize as standardize does this anyway
mol = Chem.MolFromSmiles(smiles, sanitize=False)
mol = Standardizer().standardize(mol)
tautomer = TautomerCanonicalizer().canonicalize(mol)
return Chem.MolToSmiles(tautomer, isomericSmiles=True)
| 13,987 | 40.630952 | 120 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/validate.py | # -*- coding: utf-8 -*-
"""
molvs.validate
~~~~~~~~~~~~~~
This module contains the main :class:`~molvs.validate.Validator` class that can be used to perform all
:class:`Validations <molvs.validations.Validation>`, as well as the :func:`~molvs.validate.validate_smiles()`
convenience function.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import logging
import sys
from rdkit import Chem
from .errors import StopValidateError
from .validations import VALIDATIONS
#: The default format for log messages.
SIMPLE_FORMAT = '%(levelname)s: [%(validation)s] %(message)s'
#: A more detailed format for log messages. Specify when initializing a Validator.
LONG_FORMAT = '%(asctime)s - %(levelname)s - %(validation)s - %(message)s'
class LogHandler(logging.Handler):
"""A simple logging Handler that just stores logs in an array until flushed."""
def __init__(self):
logging.Handler.__init__(self)
self.logs = []
@property
def logmessages(self):
return [self.format(record) for record in self.logs]
def emit(self, record):
"""Append the record."""
self.logs.append(record)
def flush(self):
"""Clear the log records."""
self.acquire()
try:
self.logs = []
finally:
self.release()
def close(self):
"""Close the handler."""
self.flush()
logging.Handler.close(self)
class Validator(object):
"""The main class for running :class:`Validations <molvs.validations.Validation>` on molecules."""
def __init__(self, validations=VALIDATIONS, log_format=SIMPLE_FORMAT, level=logging.INFO, stdout=False, raw=False):
"""Initialize a Validator with the following parameters:
:param validations: A list of Validations to apply (default: :data:`~molvs.validations.VALIDATIONS`).
:param string log_format: A string format (default: :data:`~molvs.validate.SIMPLE_FORMAT`).
:param level: The minimum logging level to output.
:param bool stdout: Whether to send log messages to standard output.
:param bool raw: Whether to return raw :class:`~logging.LogRecord` objects instead of formatted log strings.
"""
self.raw = raw
# Set up logger and add default LogHandler
self.log = logging.getLogger(type(self).__name__)
self.log.setLevel(level)
self.handler = LogHandler()
self.handler.setFormatter(logging.Formatter(log_format))
self.log.addHandler(self.handler)
# Add stdout StreamHandler if specified in parameters
if stdout:
strhdlr = logging.StreamHandler(sys.stdout)
strhdlr.setFormatter(logging.Formatter(log_format))
self.log.addHandler(strhdlr)
# Instantiate the validations
self.validations = [validation(self.log) for validation in validations]
def __call__(self, mol):
"""Calling a Validator instance like a function is the same as calling its
:meth:`~molvs.validate.Validator.validate` method."""
return self.validate(mol)
def validate(self, mol):
""""""
# Clear any log messages from previous runs
self.handler.flush()
# Run every validation, stopping if StopValidateError is raised
for validation in self.validations:
try:
validation(mol)
except StopValidateError:
break
return self.handler.logs if self.raw else self.handler.logmessages
def validate_smiles(smiles):
"""Return log messages for a given SMILES string using the default validations.
Note: This is a convenience function for quickly validating a single SMILES string. It is more efficient to use
the :class:`~molvs.validate.Validator` class directly when working with many molecules or when custom options
are needed.
:param string smiles: The SMILES for the molecule.
:returns: A list of log messages.
:rtype: list of strings.
"""
# Skip sanitize as standardize does this anyway
mol = Chem.MolFromSmiles(smiles)
logs = Validator().validate(mol)
return logs
| 4,213 | 34.116667 | 119 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/utils.py | # -*- coding: utf-8 -*-
"""
molvs.utils
~~~~~~~~~~~
This module contains miscellaneous utility functions.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import functools
from itertools import tee
import six
def memoized_property(fget):
"""Decorator to create memoized properties."""
attr_name = '_{}'.format(fget.__name__)
@functools.wraps(fget)
def fget_memoized(self):
if not hasattr(self, attr_name):
setattr(self, attr_name, fget(self))
return getattr(self, attr_name)
return property(fget_memoized)
def pairwise(iterable):
"""Utility function to iterate in a pairwise fashion."""
a, b = tee(iterable)
next(b, None)
return six.moves.zip(a, b)
| 787 | 20.888889 | 60 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/cli.py | # -*- coding: utf-8 -*-
"""
molvs.cli
~~~~~~~~~
This module contains a command line interface for standardization.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import argparse
import logging
from rdkit import Chem
import sys
from molvs import Standardizer, Validator
log = logging.getLogger(__name__)
FILETYPES = ['smi', 'mol', 'sdf']
class MolvsParser(argparse.ArgumentParser):
def error(self, message):
sys.stderr.write('Error: %s\n\n'.encode() % message)
self.print_help()
sys.exit(2)
def main():
"""Main function for molvs command line interface."""
# Root options
parser = MolvsParser(epilog='use "molvs <command> -h" to show help for a specific command')
subparsers = parser.add_subparsers(title='Available commands')
# Options common to all commands
common_parser = MolvsParser(add_help=False)
common_parser.add_argument('infile', nargs='?', help='input filename', type=argparse.FileType('r'), default=sys.stdin)
common_parser.add_argument('-i', '--intype', help='input filetype', choices=FILETYPES)
common_parser.add_argument('-:', '--smiles', help='input SMILES instead of file', metavar='<smiles>')
common_parser.add_argument('-O', '--outfile', help='output filename', type=argparse.FileType('w'), default=sys.stdout, metavar='<outfile>')
# Standardize options
standardize_parser = subparsers.add_parser('standardize', help='standardize a molecule', parents=[common_parser])
standardize_parser.add_argument('-o', '--outtype', help='output filetype', choices=FILETYPES)
standardize_parser.set_defaults(func=standardize_main)
# Validate options
validate_parser = subparsers.add_parser('validate', help='validate a molecule', parents=[common_parser])
validate_parser.set_defaults(func=validate_main)
args = parser.parse_args()
try:
args.func(args)
except Exception as e:
sys.stderr.write('Error: %s\n\n'.encode() % e.message)
parser.print_help()
sys.exit(2)
def _read_mol(args):
if args.smiles:
return Chem.MolFromSmiles(args.smiles)
elif args.intype in {'smi', 'smiles'} or args.infile.name.endswith('smi') or args.infile.name.endswith('smiles'):
return Chem.MolFromSmiles(args.infile.read())
elif args.intype in {'mol', 'sdf'} or args.infile.name.endswith('mol') or args.infile.name.endswith('sdf'):
return Chem.MolFromMolBlock(args.infile.read())
else:
return Chem.MolFromSmiles(args.infile.read())
def _write_mol(mol, args):
if args.outtype in {'smi', 'smiles'} or args.outfile.name.endswith('smi') or args.outfile.name.endswith('smiles'):
args.outfile.write(Chem.MolToSmiles(mol))
args.outfile.write('\n')
elif args.outtype in {'mol', 'sdf'} or args.outfile.name.endswith('mol') or args.outfile.name.endswith('sdf'):
args.outfile.write(Chem.MolToMolBlock(mol))
else:
args.outfile.write(Chem.MolToSmiles(mol))
args.outfile.write('\n')
def standardize_main(args):
mol = _read_mol(args)
s = Standardizer()
mol = s.standardize(mol)
_write_mol(mol, args)
def validate_main(args):
mol = _read_mol(args)
v = Validator()
logs = v.validate(mol)
for log in logs:
args.outfile.write(log)
args.outfile.write('\n')
| 3,399 | 31.380952 | 143 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/metal.py | # -*- coding: utf-8 -*-
"""
molvs.metal
~~~~~~~~~~~
This module contains tools for disconnecting metal atoms that are defined as covalently bonded to non-metals.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import logging
from rdkit import Chem
log = logging.getLogger(__name__)
# TODO: This won't disconnect e.g. covalent [Na]Cl...
class MetalDisconnector(object):
"""Class for breaking covalent bonds between metals and organic atoms under certain conditions."""
def __init__(self):
log.debug('Initializing MetalDisconnector')
# Initialize SMARTS to identify relevant substructures
# TODO: Use atomic numbers instead of element symbols in SMARTS to allow for isotopes?
self._metal_nof = Chem.MolFromSmarts('[Li,Na,K,Rb,Cs,Fr,Be,Mg,Ca,Sr,Ba,Ra,Sc,Ti,V,Cr,Mn,Fe,Co,Ni,Cu,Zn,Al,Ga,Y,Zr,Nb,Mo,Tc,Ru,Rh,Pd,Ag,Cd,In,Sn,Hf,Ta,W,Re,Os,Ir,Pt,Au,Hg,Tl,Pb,Bi]~[N,O,F]')
self._metal_non = Chem.MolFromSmarts('[Al,Sc,Ti,V,Cr,Mn,Fe,Co,Ni,Cu,Zn,Y,Zr,Nb,Mo,Tc,Ru,Rh,Pd,Ag,Cd,Hf,Ta,W,Re,Os,Ir,Pt,Au]~[B,C,Si,P,As,Sb,S,Se,Te,Cl,Br,I,At]')
def __call__(self, mol):
"""Calling a MetalDisconnector instance like a function is the same as calling its disconnect(mol) method."""
return self.disconnect(mol)
def disconnect(self, mol):
"""Break covalent bonds between metals and organic atoms under certain conditions.
The algorithm works as follows:
- Disconnect N, O, F from any metal.
- Disconnect other non-metals from transition metals + Al (but not Hg, Ga, Ge, In, Sn, As, Tl, Pb, Bi, Po).
- For every bond broken, adjust the charges of the begin and end atoms accordingly.
:param mol: The input molecule.
:type mol: rdkit.Chem.rdchem.Mol
:return: The molecule with metals disconnected.
:rtype: rdkit.Chem.rdchem.Mol
"""
log.debug('Running MetalDisconnector')
# Remove bonds that match SMARTS
for smarts in [self._metal_nof, self._metal_non]:
pairs = mol.GetSubstructMatches(smarts)
rwmol = Chem.RWMol(mol)
orders = []
for i, j in pairs:
# TODO: Could get the valence contributions of the bond instead of GetBondTypeAsDouble?
orders.append(int(mol.GetBondBetweenAtoms(i, j).GetBondTypeAsDouble()))
rwmol.RemoveBond(i, j)
# Adjust neighbouring charges accordingly
mol = rwmol.GetMol()
for n, (i, j) in enumerate(pairs):
chg = orders[n]
atom1 = mol.GetAtomWithIdx(i)
atom1.SetFormalCharge(atom1.GetFormalCharge() + chg)
atom2 = mol.GetAtomWithIdx(j)
atom2.SetFormalCharge(atom2.GetFormalCharge() - chg)
log.info('Removed covalent bond between %s and %s', atom1.GetSymbol(), atom2.GetSymbol())
Chem.SanitizeMol(mol)
return mol
| 3,012 | 40.273973 | 197 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/__init__.py | # -*- coding: utf-8 -*-
"""
MolVS - Molecule Validation and Standardization
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
MolVS is a python tool built on top of RDKit that performs validation and standardization of chemical structures.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import logging
from .standardize import Standardizer, standardize_smiles, enumerate_tautomers_smiles, canonicalize_tautomer_smiles
from .validate import Validator, validate_smiles
from .errors import MolVSError, StandardizeError, ValidateError
__title__ = 'MolVS'
__version__ = '0.1.1'
__author__ = 'Matt Swain'
__email__ = 'm.swain@me.com'
__license__ = 'MIT'
__copyright__ = 'Copyright 2019 Matt Swain'
log = logging.getLogger(__name__)
log.addHandler(logging.NullHandler())
| 837 | 26.933333 | 115 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/normalize.py | # -*- coding: utf-8 -*-
"""
molvs.normalize
~~~~~~~~~~~~~~~
This module contains tools for normalizing molecules using reaction SMARTS patterns.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import logging
from rdkit import Chem
from rdkit.Chem import AllChem
import six
from .utils import memoized_property
log = logging.getLogger(__name__)
class Normalization(object):
"""A normalization transform defined by reaction SMARTS."""
def __init__(self, name, transform):
"""
:param string name: A name for this Normalization
:param string transform: Reaction SMARTS to define the transformation.
"""
log.debug('Initializing Normalization: %s', name)
self.name = name
self.transform_str = transform
@memoized_property
def transform(self):
log.debug('Loading Normalization transform: %s', self.name)
return AllChem.ReactionFromSmarts(str(self.transform_str))
def __repr__(self):
return 'Normalization({!r}, {!r})'.format(self.name, self.transform_str)
def __str__(self):
return self.name
#: The default list of Normalization transforms.
NORMALIZATIONS = (
# Opposite of #2.1 in InChI technical manual? Covered by RDKit Sanitization.
Normalization('Nitro to N+(O-)=O', '[N,P,As,Sb;X3:1](=[O,S,Se,Te:2])=[O,S,Se,Te:3]>>[*+1:1]([*-1:2])=[*:3]'),
Normalization('Sulfone to S(=O)(=O)', '[S+2:1]([O-:2])([O-:3])>>[S+0:1](=[O-0:2])(=[O-0:3])'),
Normalization('Pyridine oxide to n+O-', '[n:1]=[O:2]>>[n+:1][O-:2]'),
Normalization('Azide to N=N+=N-', '[*,H:1][N:2]=[N:3]#[N:4]>>[*,H:1][N:2]=[N+:3]=[N-:4]'),
Normalization('Diazo/azo to =N+=N-', '[*:1]=[N:2]#[N:3]>>[*:1]=[N+:2]=[N-:3]'),
Normalization('Sulfoxide to -S+(O-)-', '[!O:1][S+0;X3:2](=[O:3])[!O:4]>>[*:1][S+1:2]([O-:3])[*:4]'),
# Equivalent to #1.5 in InChI technical manual
Normalization('Phosphate to P(O-)=O', '[O,S,Se,Te;-1:1][P+;D4:2][O,S,Se,Te;-1:3]>>[*+0:1]=[P+0;D5:2][*-1:3]'),
# Equivalent to #1.8 in InChI technical manual
Normalization('C/S+N to C/S=N+', '[C,S;X3+1:1]([NX3:2])[NX3!H0:3]>>[*+0:1]([N:2])=[N+:3]'),
# Equivalent to #1.8 in InChI technical manual
Normalization('P+N to P=N+', '[P;X4+1:1]([NX3:2])[NX3!H0:3]>>[*+0:1]([N:2])=[N+:3]'),
Normalization('Normalize hydrazine-diazonium', '[CX4:1][NX3H:2]-[NX3H:3][CX4:4][NX2+:5]#[NX1:6]>>[CX4:1][NH0:2]=[NH+:3][C:4][N+0:5]=[NH:6]'),
# Equivalent to #1.3 in InChI technical manual
Normalization('Recombine 1,3-separated charges', '[N,P,As,Sb,O,S,Se,Te;-1:1]-[A+0:2]=[N,P,As,Sb,O,S,Se,Te;+1:3]>>[*-0:1]=[*:2]-[*+0:3]'),
Normalization('Recombine 1,3-separated charges', '[n,o,p,s;-1:1]:[a:2]=[N,O,P,S;+1:3]>>[*-0:1]:[*:2]-[*+0:3]'),
Normalization('Recombine 1,3-separated charges', '[N,O,P,S;-1:1]-[a:2]:[n,o,p,s;+1:3]>>[*-0:1]=[*:2]:[*+0:3]'),
Normalization('Recombine 1,5-separated charges', '[N,P,As,Sb,O,S,Se,Te;-1:1]-[A+0:2]=[A:3]-[A:4]=[N,P,As,Sb,O,S,Se,Te;+1:5]>>[*-0:1]=[*:2]-[*:3]=[*:4]-[*+0:5]'),
Normalization('Recombine 1,5-separated charges', '[n,o,p,s;-1:1]:[a:2]:[a:3]:[c:4]=[N,O,P,S;+1:5]>>[*-0:1]:[*:2]:[*:3]:[c:4]-[*+0:5]'),
Normalization('Recombine 1,5-separated charges', '[N,O,P,S;-1:1]-[c:2]:[a:3]:[a:4]:[n,o,p,s;+1:5]>>[*-0:1]=[c:2]:[*:3]:[*:4]:[*+0:5]'),
# Conjugated cation rules taken from Francis Atkinson's standardiser. Those that can reduce aromaticity aren't included
Normalization('Normalize 1,3 conjugated cation', '[N,O;+0!H0:1]-[A:2]=[N!$(*[O-]),O;+1H0:3]>>[*+1:1]=[*:2]-[*+0:3]'),
Normalization('Normalize 1,3 conjugated cation', '[n;+0!H0:1]:[c:2]=[N!$(*[O-]),O;+1H0:3]>>[*+1:1]:[*:2]-[*+0:3]'),
#Normalization('Normalize 1,3 conjugated cation', '[N,O;+0!H0:1]-[c:2]:[n!$(*[O-]),o;+1H0:3]>>[*+1:1]=[*:2]:[*+0:3]'),
Normalization('Normalize 1,5 conjugated cation', '[N,O;+0!H0:1]-[A:2]=[A:3]-[A:4]=[N!$(*[O-]),O;+1H0:5]>>[*+1:1]=[*:2]-[*:3]=[*:4]-[*+0:5]'),
Normalization('Normalize 1,5 conjugated cation', '[n;+0!H0:1]:[a:2]:[a:3]:[c:4]=[N!$(*[O-]),O;+1H0:5]>>[n+1:1]:[*:2]:[*:3]:[*:4]-[*+0:5]'),
# Normalization('Normalize 1,5 conjugated cation', '[N,O;+0!H0:1]-[c:2]:[a:3]:[a:4]:[n!$(*[O-]),o;+1H0:5]>>[*+1:1]=[c:2]:[*:3]:[*:4]:[*+0:5]'),
# Normalization('Normalize 1,5 conjugated cation', '[n;+0!H0:1]1:[a:2]:[a:3]:[a:4]:[n!$(*[O-]);+1H0:5]1>>[n+1:1]1:[*:2]:[*:3]:[*:4]:[n+0:5]1'),
# Normalization('Normalize 1,5 conjugated cation', '[n;+0!H0:1]:[a:2]:[a:3]:[a:4]:[n!$(*[O-]);+1H0:5]>>[n+1:1]:[*:2]:[*:3]:[*:4]:[n+0:5]'),
# Equivalent to #1.6 in InChI technical manual. RDKit Sanitization handles this for perchlorate.
Normalization('Charge normalization', '[F,Cl,Br,I,At;-1:1]=[O:2]>>[*-0:1][O-:2]'),
Normalization('Charge recombination', '[N,P,As,Sb;-1:1]=[C+;v3:2]>>[*+0:1]#[C+0:2]'),
)
# InChI technical manual has many additional rules that cover situations that are disallowed by RDKit
#: The default value for the maximum number of times to attempt to apply the series of normalizations.
MAX_RESTARTS = 200
class Normalizer(object):
"""A class for applying Normalization transforms.
This class is typically used to apply a series of Normalization transforms to correct functional groups and
recombine charges. Each transform is repeatedly applied until no further changes occur.
"""
def __init__(self, normalizations=NORMALIZATIONS, max_restarts=MAX_RESTARTS):
"""Initialize a Normalizer with an optional custom list of :class:`~molvs.normalize.Normalization` transforms.
:param normalizations: A list of :class:`~molvs.normalize.Normalization` transforms to apply.
:param int max_restarts: The maximum number of times to attempt to apply the series of normalizations (default
200).
"""
log.debug('Initializing Normalizer')
self.normalizations = normalizations
self.max_restarts = max_restarts
def __call__(self, mol):
"""Calling a Normalizer instance like a function is the same as calling its normalize(mol) method."""
return self.normalize(mol)
def normalize(self, mol):
"""Apply a series of Normalization transforms to correct functional groups and recombine charges.
A series of transforms are applied to the molecule. For each Normalization, the transform is applied repeatedly
until no further changes occur. If any changes occurred, we go back and start from the first Normalization
again, in case the changes mean an earlier transform is now applicable. The molecule is returned once the entire
series of Normalizations cause no further changes or if max_restarts (default 200) is reached.
:param mol: The molecule to normalize.
:type mol: rdkit.Chem.rdchem.Mol
:return: The normalized fragment.
:rtype: rdkit.Chem.rdchem.Mol
"""
log.debug('Running Normalizer')
# Normalize each fragment separately to get around quirky RunReactants behaviour
fragments = []
for fragment in Chem.GetMolFrags(mol, asMols=True):
fragments.append(self._normalize_fragment(fragment))
# Join normalized fragments into a single molecule again
outmol = fragments.pop()
for fragment in fragments:
outmol = Chem.CombineMols(outmol, fragment)
Chem.SanitizeMol(outmol)
return outmol
def _normalize_fragment(self, mol):
for n in six.moves.range(self.max_restarts):
# Iterate through Normalization transforms and apply each in order
for normalization in self.normalizations:
product = self._apply_transform(mol, normalization.transform)
if product:
# If transform changed mol, go back to first rule and apply each again
log.info('Rule applied: %s', normalization.name)
mol = product
break
else:
# For loop finishes normally, all applicable transforms have been applied
return mol
# If we're still going after max_restarts (default 200), stop and warn, but still return the mol
log.warning('Gave up normalization after %s restarts', self.max_restarts)
return mol
def _apply_transform(self, mol, rule):
"""Repeatedly apply normalization transform to molecule until no changes occur.
It is possible for multiple products to be produced when a rule is applied. The rule is applied repeatedly to
each of the products, until no further changes occur or after 20 attempts. If there are multiple unique products
after the final application, the first product (sorted alphabetically by SMILES) is chosen.
"""
mols = [mol]
for n in six.moves.range(20):
products = {}
for mol in mols:
for product in [x[0] for x in rule.RunReactants((mol,))]:
if Chem.SanitizeMol(product, catchErrors=True) == 0:
products[Chem.MolToSmiles(product, isomericSmiles=True)] = product
if products:
mols = [products[s] for s in sorted(products)]
else:
# If n == 0, the rule was not applicable and we return None
return mols[0] if n > 0 else None
| 9,432 | 52.902857 | 165 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/fragment.py | # -*- coding: utf-8 -*-
"""
molvs.fragment
~~~~~~~~~~~~~~
This module contains tools for dealing with molecules with more than one covalently bonded unit. The main classes are
:class:`~molvs.fragment.LargestFragmentChooser`, which returns the largest covalent unit in a molecule, and
:class:`~molvs.fragment.FragmentRemover`, which filters out fragments from a molecule using SMARTS patterns.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import logging
from rdkit import Chem
from rdkit.Chem import rdMolDescriptors
from .utils import memoized_property
log = logging.getLogger(__name__)
class FragmentPattern(object):
"""A fragment defined by a SMARTS pattern."""
def __init__(self, name, smarts):
"""Initialize a FragmentPattern with a name and a SMARTS pattern.
:param name: A name for this FragmentPattern.
:param smarts: A SMARTS pattern.
"""
self.name = name
self.smarts_str = smarts
@memoized_property
def smarts(self):
return Chem.MolFromSmarts(self.smarts_str)
def __repr__(self):
return 'FragmentPattern({!r}, {!r})'.format(self.name, self.smarts_str)
def __str__(self):
return self.name
#: The default list of :class:`FragmentPatterns <molvs.fragment.FragmentPattern>` to be used by
#: :class:`~molvs.fragment.FragmentRemover`.
REMOVE_FRAGMENTS = (
FragmentPattern('hydrogen', '[H]'),
FragmentPattern('fluorine', '[F]'),
FragmentPattern('chlorine', '[Cl]'),
FragmentPattern('bromine', '[Br]'),
FragmentPattern('iodine', '[I]'),
FragmentPattern('lithium', '[Li]'),
FragmentPattern('sodium', '[Na]'),
FragmentPattern('potassium', '[K]'),
FragmentPattern('calcium', '[Ca]'),
FragmentPattern('magnesium', '[Mg]'),
FragmentPattern('aluminium', '[Al]'),
FragmentPattern('barium', '[Ba]'),
FragmentPattern('bismuth', '[Bi]'),
FragmentPattern('silver', '[Ag]'),
FragmentPattern('strontium', '[Sr]'),
FragmentPattern('zinc', '[Zn]'),
FragmentPattern('ammonia/ammonium', '[#7]'),
FragmentPattern('water/hydroxide', '[#8]'),
FragmentPattern('methyl amine', '[#6]-[#7]'),
FragmentPattern('sulfide', 'S'),
FragmentPattern('nitrate', '[#7](=[#8])(-[#8])-[#8]'),
FragmentPattern('phosphate', '[P](=[#8])(-[#8])(-[#8])-[#8]'),
FragmentPattern('hexafluorophosphate', '[P](-[#9])(-[#9])(-[#9])(-[#9])(-[#9])-[#9]'),
FragmentPattern('sulfate', '[S](=[#8])(=[#8])(-[#8])-[#8]'),
FragmentPattern('methyl sulfonate', '[#6]-[S](=[#8])(=[#8])(-[#8])'),
FragmentPattern('trifluoromethanesulfonic acid', '[#8]-[S](=[#8])(=[#8])-[#6](-[#9])(-[#9])-[#9]'),
FragmentPattern('trifluoroacetic acid', '[#9]-[#6](-[#9])(-[#9])-[#6](=[#8])-[#8]'),
FragmentPattern('1,2-dichloroethane', '[Cl]-[#6]-[#6]-[Cl]'),
FragmentPattern('1,2-dimethoxyethane', '[#6]-[#8]-[#6]-[#6]-[#8]-[#6]'),
FragmentPattern('1,4-dioxane', '[#6]-1-[#6]-[#8]-[#6]-[#6]-[#8]-1'),
FragmentPattern('1-methyl-2-pyrrolidinone', '[#6]-[#7]-1-[#6]-[#6]-[#6]-[#6]-1=[#8]'),
FragmentPattern('2-butanone', '[#6]-[#6]-[#6](-[#6])=[#8]'),
FragmentPattern('acetate/acetic acid', '[#8]-[#6](-[#6])=[#8]'),
FragmentPattern('acetone', '[#6]-[#6](-[#6])=[#8]'),
FragmentPattern('acetonitrile', '[#6]-[#6]#[N]'),
FragmentPattern('benzene', '[#6]1[#6][#6][#6][#6][#6]1'),
FragmentPattern('butanol', '[#8]-[#6]-[#6]-[#6]-[#6]'),
FragmentPattern('t-butanol', '[#8]-[#6](-[#6])(-[#6])-[#6]'),
FragmentPattern('chloroform', '[Cl]-[#6](-[Cl])-[Cl]'),
FragmentPattern('cycloheptane', '[#6]-1-[#6]-[#6]-[#6]-[#6]-[#6]-[#6]-1'),
FragmentPattern('cyclohexane', '[#6]-1-[#6]-[#6]-[#6]-[#6]-[#6]-1'),
FragmentPattern('dichloromethane', '[Cl]-[#6]-[Cl]'),
FragmentPattern('diethyl ether', '[#6]-[#6]-[#8]-[#6]-[#6]'),
FragmentPattern('diisopropyl ether', '[#6]-[#6](-[#6])-[#8]-[#6](-[#6])-[#6]'),
FragmentPattern('dimethyl formamide', '[#6]-[#7](-[#6])-[#6]=[#8]'),
FragmentPattern('dimethyl sulfoxide', '[#6]-[S](-[#6])=[#8]'),
FragmentPattern('ethanol', '[#8]-[#6]-[#6]'),
FragmentPattern('ethyl acetate', '[#6]-[#6]-[#8]-[#6](-[#6])=[#8]'),
FragmentPattern('formic acid', '[#8]-[#6]=[#8]'),
FragmentPattern('heptane', '[#6]-[#6]-[#6]-[#6]-[#6]-[#6]-[#6]'),
FragmentPattern('hexane', '[#6]-[#6]-[#6]-[#6]-[#6]-[#6]'),
FragmentPattern('isopropanol', '[#8]-[#6](-[#6])-[#6]'),
FragmentPattern('methanol', '[#8]-[#6]'),
FragmentPattern('N,N-dimethylacetamide', '[#6]-[#7](-[#6])-[#6](-[#6])=[#8]'),
FragmentPattern('pentane', '[#6]-[#6]-[#6]-[#6]-[#6]'),
FragmentPattern('propanol', '[#8]-[#6]-[#6]-[#6]'),
FragmentPattern('pyridine', '[#6]-1=[#6]-[#6]=[#7]-[#6]=[#6]-1'),
FragmentPattern('t-butyl methyl ether', '[#6]-[#8]-[#6](-[#6])(-[#6])-[#6]'),
FragmentPattern('tetrahydrofurane', '[#6]-1-[#6]-[#6]-[#8]-[#6]-1'),
FragmentPattern('toluene', '[#6]-[#6]~1~[#6]~[#6]~[#6]~[#6]~[#6]~1'),
FragmentPattern('xylene', '[#6]-[#6]~1~[#6](-[#6])~[#6]~[#6]~[#6]~[#6]~1')
)
#: The default value for whether to ensure at least one fragment is left after FragmentRemover is applied.
LEAVE_LAST = True
#: The default value for whether LargestFragmentChooser sees organic fragments as "larger" than inorganic fragments.
PREFER_ORGANIC = False
def is_organic(fragment):
"""Return true if fragment contains at least one carbon atom.
:param fragment: The fragment as an RDKit Mol object.
"""
# TODO: Consider a different definition?
# Could allow only H, C, N, O, S, P, F, Cl, Br, I
for a in fragment.GetAtoms():
if a.GetAtomicNum() == 6:
return True
return False
class FragmentRemover(object):
"""A class for filtering out fragments using SMARTS patterns."""
def __init__(self, fragments=REMOVE_FRAGMENTS, leave_last=LEAVE_LAST):
"""Initialize a FragmentRemover with an optional custom list of :class:`~molvs.fragment.FragmentPattern`.
Setting leave_last to True will ensure at least one fragment is left in the molecule, even if it is matched by a
:class:`~molvs.fragment.FragmentPattern`. Fragments are removed in the order specified in the list, so place
those you would prefer to be left towards the end of the list. If all the remaining fragments match the same
:class:`~molvs.fragment.FragmentPattern`, they will all be left.
:param fragments: A list of :class:`~molvs.fragment.FragmentPattern` to remove.
:param bool leave_last: Whether to ensure at least one fragment is left.
"""
log.debug('Initializing FragmentRemover')
self.fragments = fragments
self.leave_last = leave_last
def __call__(self, mol):
"""Calling a FragmentRemover instance like a function is the same as calling its remove(mol) method."""
return self.remove(mol)
def remove(self, mol):
"""Return the molecule with specified fragments removed.
:param mol: The molecule to remove fragments from.
:type mol: rdkit.Chem.rdchem.Mol
:return: The molecule with fragments removed.
:rtype: rdkit.Chem.rdchem.Mol
"""
log.debug('Running FragmentRemover')
# Iterate FragmentPatterns and remove matching fragments
for frag in self.fragments:
# If nothing is left or leave_last and only one fragment, end here
if mol.GetNumAtoms() == 0 or (self.leave_last and len(Chem.GetMolFrags(mol)) <= 1):
break
# Apply removal for this FragmentPattern
removed = Chem.DeleteSubstructs(mol, frag.smarts, onlyFrags=True)
if not mol.GetNumAtoms() == removed.GetNumAtoms():
log.info('Removed fragment: %s', frag.name)
if self.leave_last and removed.GetNumAtoms() == 0:
# All the remaining fragments match this pattern - leave them all
break
mol = removed
return mol
class LargestFragmentChooser(object):
"""A class for selecting the largest covalent unit in a molecule with multiple fragments."""
def __init__(self, prefer_organic=PREFER_ORGANIC):
"""
If prefer_organic is set to True, any organic fragment will be considered larger than any inorganic fragment. A
fragment is considered organic if it contains a carbon atom.
:param bool prefer_organic: Whether to prioritize organic fragments above all others.
"""
log.debug('Initializing LargestFragmentChooser')
self.prefer_organic = prefer_organic
def __call__(self, mol):
"""Calling a LargestFragmentChooser instance like a function is the same as calling its choose(mol) method."""
return self.choose(mol)
def choose(self, mol):
"""Return the largest covalent unit.
The largest fragment is determined by number of atoms (including hydrogens). Ties are broken by taking the
fragment with the higher molecular weight, and then by taking the first alphabetically by SMILES if needed.
:param mol: The molecule to choose the largest fragment from.
:type mol: rdkit.Chem.rdchem.Mol
:return: The largest fragment.
:rtype: rdkit.Chem.rdchem.Mol
"""
log.debug('Running LargestFragmentChooser')
# TODO: Alternatively allow a list of fragments to be passed as the mol parameter
fragments = Chem.GetMolFrags(mol, asMols=True)
largest = None
for f in fragments:
smiles = Chem.MolToSmiles(f, isomericSmiles=True)
log.debug('Fragment: %s', smiles)
organic = is_organic(f)
if self.prefer_organic:
# Skip this fragment if not organic and we already have an organic fragment as the largest so far
if largest and largest['organic'] and not organic:
continue
# Reset largest if it wasn't organic and this fragment is organic
if largest and organic and not largest['organic']:
largest = None
# Count atoms
atoms = 0
for a in f.GetAtoms():
atoms += 1 + a.GetTotalNumHs()
# Skip this fragment if fewer atoms than the largest
if largest and atoms < largest['atoms']:
continue
# Skip this fragment if equal number of atoms but weight is lower
weight = rdMolDescriptors.CalcExactMolWt(f)
if largest and atoms == largest['atoms'] and weight < largest['weight']:
continue
# Skip this fragment if equal atoms and equal weight but smiles comes last alphabetically
if largest and atoms == largest['atoms'] and weight == largest['weight'] and smiles > largest['smiles']:
continue
# Otherwise this is the largest so far
log.debug('New largest fragment: %s (%s)', smiles, atoms)
largest = {'smiles': smiles, 'fragment': f, 'atoms': atoms, 'weight': weight, 'organic': organic}
return largest['fragment']
| 11,192 | 44.872951 | 120 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/convert_files/gypsum_dl/gypsum_dl/molvs/validations.py | # -*- coding: utf-8 -*-
"""
molvs.validations
~~~~~~~~~~~~~~~~~
This module contains all the built-in :class:`Validations <molvs.validations.Validation>`.
"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import logging
from rdkit import Chem
from .errors import StopValidateError
from .fragment import REMOVE_FRAGMENTS
class Validation(object):
"""The base class that all :class:`~molvs.validations.Validation` subclasses must inherit from."""
def __init__(self, log):
self.log = logging.LoggerAdapter(log, {'validation': type(self).__name__})
def __call__(self, mol):
try:
self.log.debug('Running %s', type(self).__name__)
self.run(mol)
except Exception as e:
if isinstance(e, StopValidateError):
raise e
else:
self.log.debug('Validation failed: %s', e)
def run(self, mol):
""""""
raise NotImplementedError("Validation subclasses must implement the run method")
class SmartsValidation(Validation):
"""Abstract superclass for :class:`Validations <molvs.validations.Validation>` that log a message if a SMARTS
pattern matches the molecule.
Subclasses can override the following attributes:
"""
#: The logging level of the message.
level = logging.INFO
#: The message to log if the SMARTS pattern matches the molecule.
message = 'Molecule matched %(smarts)s'
#: Whether the SMARTS pattern should match an entire covalent unit.
entire_fragment = False
def __init__(self, log):
super(SmartsValidation, self).__init__(log)
self._smarts = Chem.MolFromSmarts(self.smarts)
@property
def smarts(self):
"""The SMARTS pattern as a string. Subclasses must implement this."""
raise NotImplementedError('SmartsValidation subclasses must have a smarts attribute')
def _check_matches(self, mol):
if mol.HasSubstructMatch(self._smarts):
self.log.log(self.level, self.message, {'smarts': self.smarts})
def _check_matches_fragment(self, mol):
matches = frozenset(frozenset(match) for match in mol.GetSubstructMatches(self._smarts))
fragments = frozenset(frozenset(frag) for frag in Chem.GetMolFrags(mol))
if matches & fragments:
self.log.log(self.level, self.message, {'smarts': self.smarts})
def run(self, mol):
if self.entire_fragment:
self._check_matches_fragment(mol)
else:
self._check_matches(mol)
class IsNoneValidation(Validation):
"""Logs an error if ``None`` is passed to the Validator.
This can happen if RDKit failed to parse an input format. If the molecule is ``None``, no subsequent validations
will run.
"""
def run(self, mol):
if mol is None:
self.log.error('Molecule is None')
raise StopValidateError()
class NoAtomValidation(Validation):
"""Logs an error if the molecule has zero atoms.
If the molecule has no atoms, no subsequent validations will run.
"""
def run(self, mol):
if mol.GetNumAtoms() == 0:
self.log.error('No atoms are present')
raise StopValidateError()
class DichloroethaneValidation(SmartsValidation):
"""Logs if 1,2-dichloroethane is present.
This is provided as an example of how to subclass :class:`~molvs.validations.SmartsValidation` to check for the
presence of a substructure.
"""
level = logging.INFO
smarts = '[Cl]-[#6]-[#6]-[Cl]'
entire_fragment = True
message = '1,2-Dichloroethane is present'
class FragmentValidation(Validation):
"""Logs if certain fragments are present.
Subclass and override the ``fragments`` class attribute to customize the list of
:class:`FragmentPatterns <molvs.fragment.FragmentPattern>`.
"""
#: A list of :class:`FragmentPatterns <molvs.fragment.FragmentPattern>` to check for.
fragments = REMOVE_FRAGMENTS
def run(self, mol):
for fp in self.fragments:
matches = frozenset(frozenset(match) for match in mol.GetSubstructMatches(fp.smarts))
fragments = frozenset(frozenset(frag) for frag in Chem.GetMolFrags(mol))
if matches & fragments:
self.log.info('%s is present', fp.name)
class NeutralValidation(Validation):
"""Logs if not an overall neutral system."""
def run(self, mol):
charge = Chem.GetFormalCharge(mol)
if not charge == 0:
chargestring = '+%s' % charge if charge > 0 else '%s' % charge
self.log.info('Not an overall neutral system (%s)', chargestring)
class IsotopeValidation(Validation):
"""Logs if molecule contains isotopes."""
def run(self, mol):
isotopes = set()
for atom in mol.GetAtoms():
isotope = atom.GetIsotope()
if not isotope == 0:
isotopes.add('%s%s' % (isotope, atom.GetSymbol()))
for isotope in isotopes:
self.log.info('Molecule contains isotope %s', isotope)
#: The default list of :class:`Validations <molvs.validations.Validation>` used by :class:`~molvs.validate.Validator`.
VALIDATIONS = (
IsNoneValidation,
NoAtomValidation,
#DichloroethaneValidation,
FragmentValidation,
NeutralValidation,
IsotopeValidation,
)
# - WARN/ERROR: Are all atoms defined/real - no query atoms or invalid elements, r-group things
# - INFO: Contains unknown stereo (Perform stereochemistry perception first?)
# - INFO: Nonstandard tautomer (log SMILES of tautomer parent, or the name of the tautomer transform?)
# - WARN: InChI generation failed
# - WARN: Contains covalent bond to metal (that would be broken by MetalDisconnector)
# - WARN: Contains solvent molecules (in addition other fragment)
# - WARN: More than 99 rings causes problems with SMILES
# - INFO: Cis azo dye is unusual
# - WARN: Adjacent atoms with like charges (i.e. both positive or both negative)
# - INFO: Has more than one radical centre
# - INFO: ethane, methane molecules present
# - INFO: Boron, Sulfur atoms with no explicit bonds
# - INFO: Solvent molecules present (only if also other fragments)
# - INFO: One unknown stereocentre and no defined stereocentres (probably racemate, so info not warn)
# - WARN: More than one undefined stereocentre and no defined stereocentres
# - INFO: One undefined stereocentre and at least one defined stereocentre (epimer or mixture of anomers, so info not warn)
# - WARN: More than one undefined stereocentre and at least one defined stereocentre
# - INFO: Unknown double bond stereochemistry
# - WARN: Ring containing stereobonds?
# - INFO: Not canonical tautomer
# Coordinates?
# Info - Lack of coordinates? Uneven bond lengths?
# Web services (needs to be optional)
# Info - Could not match to ChemSpider ID, PubChem CID
# UniChem from EBI could be useful here, otherwise use each API directly
# Allow definition of MolSchema to set custom validations on e.g.
# People can define a filterer
# This has a series of validations, and the required output - e.g. no error or no warns?
| 7,188 | 33.07109 | 123 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/crossover/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/crossover/execute_crossover.py | """Pre-SmileMerge_mcs_Filter
This script should take an input of a randomly selected file containing a list
of smiles.
A random number function will be used to pick 2 non-identical numbers for 0 to
the len(smile_list) Then those numbers are used to grab 2 non-identical
molecules from smile_list Those 2 smiles are tested using the MCS function to
find the most common structure (MCS). If MCS returns None (ie. no shared
structures) then mol2 is reassigned using the random function generator. This
iterates until a shared structure is returned.
"""
import __future__
import random
import copy
import rdkit
from rdkit import Chem
from rdkit.Chem import rdFMCS
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
import autogrow.operators.filter.execute_filters as Filter
import autogrow.operators.crossover.smiles_merge.smiles_merge as smiles_merge
import autogrow.operators.convert_files.gypsum_dl.gypsum_dl.MolObjectHandling as MOH
## __all__ = ['make_crossovers']
def test_for_mcs(vars, mol_1, mol_2):
"""
Takes a ligand and a random selected molecule and looks for the Most
common structure. rdFMCS.FindMCS(mols) flags an error if there are no
common structures being compared. Try/except statement used to prevent
program crash when 2 unlike molecules are compared. mols is a list of the
molecules to be compare using rdFMCS.FindMCS
This can function with mol_1 and mol_2 having H's but we recommend using
this function with molecules with H's removed. If implicit H's are added
they will be recoginized as part of MCS. This means 1 atom in common with
3H's in common would pass a atom similarity count of 4 atoms shared, but
really its only 1 non-H atom...
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param rdkit.Chem.rdchem.Mol mol_1: the 1st rdkit molecule
:param rdkit.Chem.rdchem.Mol mol_2: the 2nd rdkit molecule
Returns:
:returns: <class 'rdkit.Chem.rdFMCS.MCSResult'> results: an MCSResults
object returns None if it fails to find MCS sufficient with the User
defined parameters.
"""
mols = [mol_1, mol_2]
time_timeout = vars["max_time_mcs_prescreen"]
min_number_atoms_matched = vars["min_atom_match_mcs"]
try:
result = rdFMCS.FindMCS(
mols,
matchValences=False,
ringMatchesRingOnly=True,
completeRingsOnly=False,
timeout=time_timeout,
)
except:
return None
# could be used for a theoretical timeout prefilter was to be implement
# (but this isn't implemented) (ie. if it times out the prefilter dont use
# in thorough MCS ligmerge) canceled: if True, the MCS calculation did not
# finish
# filter by UserDefined minimum number of atoms found. The higher the
# number the more similar 2 ligands are but the more restrictive for
# finding mergable ligands number of atoms in common found
if result.numAtoms < min_number_atoms_matched:
return None
if result.canceled is True:
return None
return result
def find_random_lig2(vars, ligands_list, ligand1_pair):
"""
Pick a random molecule from the list and check that it can be converted
into a rdkit mol object and then test for a satistifactory Most common
substructure (MCS) which satisifies the User specified minimum shared
substructure
NECESSARY INCASE THE SMILE CANNOT BE USED (ie. valence issue)
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param list ligands_list: list of all the lignads to chose from
:param list ligand1_pair: information for the Ligand 1. This info includes
the name and SMILES string
Returns:
:returns: list mol2_pair: a set of information for a 2nd ligand (Lig2)
This includes the name and SMILES string this mol is from the ligands_list
:returns: bool bool: returns False if no satistifactory matches were found
it returns False
"""
count = 0
shuffled_num_list = list(range(0, len(ligands_list) - 1))
random.shuffle(shuffled_num_list)
# Convert lig_1 into an RDkit mol
lig_1_string = ligand1_pair[0]
lig1_mol = convert_mol_from_smiles(lig_1_string)
while count < len(ligands_list) - 1:
rand_num = shuffled_num_list[count]
mol2_pair = ligands_list[rand_num]
if mol2_pair[0] == lig_1_string:
count = count + 1
continue
# Convert lig_1 into an RDkit mol
lig_2_string = mol2_pair[0]
lig2_mol = convert_mol_from_smiles(lig_2_string)
if lig2_mol is None:
count = count + 1
continue
# it converts and it is not Ligand1. now lets test for a common substructure
if test_for_mcs(vars, lig1_mol, lig2_mol) is None:
count = count + 1
continue
# We found a good pair of Ligands
return mol2_pair
return False
def convert_mol_from_smiles(smiles_string):
"""
Test a SMILES string can be converted into an rdkit molecule
(rdkit.Chem.rdchem.Mol) and be sanitize. This also deprotanates them
Inputs:
:param str smiles_string: a single SMILES String
Returns:
:returns: rdkit.Chem.rdchem.Mol mol: an rdkit molecule object if it
properly converts from the SMILE and None
"""
try:
mol = Chem.MolFromSmiles(smiles_string, sanitize=False)
except:
return None
mol = MOH.check_sanitization(mol)
if mol is None:
return None
mol = MOH.try_deprotanation(mol)
if mol is None:
return False
return mol
#########################
#### RUN MAIN PARTS #####
#########################
# __all__ = ['make_crossovers']
def make_crossovers(vars, generation_num, number_of_processors,
num_crossovers_to_make, list_previous_gen_smiles, new_crossover_smiles_list):
"""
Make crossover compounds in a list to be returned.
This runs SmileClick and returns a list of new molecules.
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param int generation_num: the generation number indexed by 0
:param int number_of_processors: number of processors to multithread with
:param int num_crossovers_to_make: number of crossovers to make User
specified
:param list list_previous_gen_smiles: a list of molecules to seed the
current generations crossovers
:param list new_crossover_smiles_list: a list of ligands made by crossover
for this generation but in a previous run of crossover, i.e., if filtering
ligands removed some of the ligands generated by crossover, it requires
another loop of crossover to fill out the list so this is used to prevent
creating the same mol multiple times
Returns:
:returns: list new_ligands_list: list of new unique ligands with unique
names/IDS ["CCC" (zinc123+zinc456)Gen_1_Cross_123456"] return None if no
new mol gets generated
"""
if len(new_crossover_smiles_list) == 0:
new_ligands_list = []
else:
new_ligands_list = copy.deepcopy(new_crossover_smiles_list)
# Use a temp vars dict so you don't put mpi multiprocess info through
# itself...
temp_vars = {}
for key in list(vars.keys()):
if key == "parallelizer":
continue
temp_vars[key] = vars[key]
new_ligands_list = []
number_of_processors = int(vars["parallelizer"].return_node())
loop_counter = 0
while loop_counter < 2000 and len(new_ligands_list) < num_crossovers_to_make:
react_list = copy.deepcopy(list_previous_gen_smiles)
while len(new_ligands_list) < num_crossovers_to_make and len(react_list) > 0:
num_to_grab = num_crossovers_to_make - len(new_ligands_list)
num_to_make = num_to_grab
# to minimize a big loop of running a single crossover at a time
# we will make 1 new lig/processor. This will help to prevent
# wasting reasources and time.
if num_to_make < number_of_processors:
num_to_make = number_of_processors
smile_pairs = [react_list.pop() for x in range(num_to_make) if len(react_list) > 0]
# smile_inputs = [x[0] for x in smile_pairs]
# smile_names = [x[1] for x in smile_pairs]
# make a list of tuples for multi-processing Crossover
job_input = []
for i in smile_pairs:
temp = tuple([temp_vars, i, list_previous_gen_smiles])
job_input.append(temp)
job_input = tuple(job_input)
# Example information:
# result is a list of lists
# result = [[ligand_new_smiles, lig1_smile_pair,lig_2_pair],...]
# ligand_new_smiles is the smiles string of a new ligand from crossover
# lig1_smile_pair = ["NCCCCCC","zinc123"]
# Lig2_smile_pair = ["NCCCO","zinc456"]
# Lig1 and lig 2 were used to generate the ligand_new_smiles
#######################################
######### main crossover ############
#######################################
results = vars["parallelizer"].run(job_input, do_crossovers_smiles_merge) #################### main crossover ################
results = [x for x in results if x is not None]
for index, i in enumerate(results):
if i is None:
continue
# Get the new molecule's (aka the Child lig) Smile string
child_lig_smile = i[0]
# get the ID for the parent of a child mol
parent_lig_1_id = i[1][1]
parent_lig_2_id = i[2][1]
# get the unique ID (last few diget ID of the parent mol)
parent_lig_1_id = parent_lig_1_id.split(")")[-1]
parent_lig_2_id = parent_lig_2_id.split(")")[-1]
# Make a list of all smiles and smile_id's of all previously
# made smiles in this generation
list_of_already_made_smiles = []
list_of_already_made_id = []
# fill lists of all smiles and smile_id's of all previously
# made smiles in this generation
for x in new_ligands_list:
list_of_already_made_smiles.append(x[0])
list_of_already_made_id.append(x[1])
if child_lig_smile not in list_of_already_made_smiles:
# if the smiles string is unique to the list of previous
# smile strings in this round of reactions then we append
# it to the list of newly created ligands we append it
# with a unique ID, which also tracks the progress of the
# reactant
is_name_unique = False
while is_name_unique is False:
# make unique ID with the 1st number being the
# ligand_id_Name for the derived mol. second being the
# lig2 number. Followed by Cross. folowed by the
# generation number. followed by a unique.
random_id_num = random.randint(100, 1000000)
new_lig_id = "({}+{})Gen_{}_Cross_{}".format(
parent_lig_1_id,
parent_lig_2_id,
generation_num,
random_id_num,
)
# check name is unique
if new_lig_id not in list_of_already_made_id:
is_name_unique = True
# make a temporary list containing the smiles string of
# the new product and the unique ID
ligand_info = [child_lig_smile, new_lig_id]
# append the new ligand smile and ID to the list of all
# newly made ligands
new_ligands_list.append(ligand_info)
loop_counter = loop_counter + 1
if len(new_ligands_list) < num_crossovers_to_make:
return None
# once the number of mutants we need is generated return the list
return new_ligands_list
def run_smiles_merge_prescreen(vars, ligands_list, ligand1_pair):
"""
This function runs a series of functions to find two molecules with a
sufficient amount of shared common structure (most common structure = MCS)
These two parent molecules will be derived from a list of molecules from
the parent generation.
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param list ligands_list: list of all the lignads to chose from
:param list ligand1_pair: information for the Ligand 1. This info includes
the name and SMILES string
Returns:
:returns: list lig_2_pair: a set of information for a 2nd ligand (Lig2)
This includes the name and SMILES string this mol is from the
ligands_list. returns None if a ligand with a sufficient MCS can not be
found return None.
"""
ligand_1_string = ligand1_pair[0]
# check if ligand_1 can be converted to an rdkit mol
lig_1 = convert_mol_from_smiles(ligand_1_string)
if lig_1 is False:
# Ligand1_string failed to be converted to rdkit mol format
return None
# GET TWO UNIQUE LIGANDS TO WITH A SHARED SUBSTRUCTURE
lig_2_pair = find_random_lig2(vars, ligands_list, ligand1_pair)
if lig_2_pair is False:
# ligand_1 has no matches
return None
return lig_2_pair
def do_crossovers_smiles_merge(vars, lig1_smile_pair, ligands_list):
"""
This function will take the list of ligands to work on and the number in
that list for the Ligand 1.
It will then prescreen the Ligand 1 using the run_smiles_merge_prescreen
which should return either a None if no matches are found, or the Smile
string of a second ligand (ligand 2) which has some share common
substructure with Ligand 1.
This pair of ligands will be passed off to the
SmilesMerge.run_main_smiles_merge function which will execute a crossover
and return a new molecule
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param list lig1_smile_pair: a list with the SMILES string and info for
lig1
:param list ligands_list: a list of all the seed ligands from the previous
generation
Returns:
:returns: str ligand_new_smiles: a new mol's SMILES string
:returns: list lig1_smile_pair: a list of parent ligand 1's information
:returns: list lig_2_pair: a list of the parent ligand 2's information
:returns: bool None: returns three Nones if there are no sufficient
matches
"""
# Run the run_smiles_merge_prescreen of the ligand. This gets a new a lig2
# which passed the prescreen.
lig_2_pair = run_smiles_merge_prescreen(vars, ligands_list, lig1_smile_pair)
if lig_2_pair is None:
return None
ligand_1_string = lig1_smile_pair[0]
ligand_2_string = lig_2_pair[0]
counter = 0
while counter < 3:
# run SmilesMerge
ligand_new_smiles = smiles_merge.run_main_smiles_merge(vars, ligand_1_string, ligand_2_string)
if ligand_new_smiles is None:
counter = counter + 1
else:
# Filter Here
pass_or_not = Filter.run_filter_on_just_smiles(ligand_new_smiles, vars["filter_object_dict"])
if pass_or_not is False:
counter = counter + 1
else:
return [ligand_new_smiles, lig1_smile_pair, lig_2_pair]
return None
| 15,983 | 35.91455 | 140 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/crossover/smiles_merge/smiles_merge.py | """
Run a single crossover event.
"""
import __future__
import rdkit
from rdkit import Chem
from rdkit.Chem import rdFMCS
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
import autogrow.operators.crossover.smiles_merge.merge_functions.merge_w_core as MWC
import autogrow.operators.crossover.smiles_merge.merge_functions.dict_and_r_groups as DnR
import autogrow.operators.crossover.smiles_merge.merge_functions.alignment_and_breaks as AnB
import autogrow.operators.convert_files.gypsum_dl.gypsum_dl.MolObjectHandling as MOH
def process_ligand_new_mol(ligand_new_mol):
"""
This function processes the ligand_new_mol.
It either returns the SMILES string of ligand_new_mol (ligand_new_smiles)
or None if it failed at any step.
Inputs:
:param str lig_string_1: smile string for lig 1
Returns:
:returns: str ligand_new_smiles: either returns the SMILES
string of ligand_new_mol or None if it failed at any step.
"""
ligand_new_mol = MOH.check_sanitization(ligand_new_mol)
if ligand_new_mol is None:
return None
# REMOVE ALL THE ISOTOPES IN THE NEW MOLECULE
ligand_new_mol_final = MWC.remove_all_isolabels(ligand_new_mol)
# Remove any fragments incase 1 made it through
ligand_new_mol_final = MOH.handle_frag_check(ligand_new_mol_final)
if ligand_new_mol_final is None:
return None
# Make sure there are no unassigned atoms which made it through. These are
# very unlikely but possible
ligand_new_mol_final = MOH.check_for_unassigned_atom(ligand_new_mol_final)
if ligand_new_mol_final is None:
return None
ligand_new_mol = MOH.check_sanitization(ligand_new_mol_final)
if ligand_new_mol is None:
return None
ligand_new_smiles = Chem.MolToSmiles(ligand_new_mol, isomericSmiles=True)
return ligand_new_smiles
def run_main_smiles_merge(vars, lig_string_1, lig_string_2):
"""
This runs the main script for SmileMerge.
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param str lig_string_1: smile string for lig 1
:param str lig_string_2: smile string for lig 2. example: lig_string_1 =
"[N-] = [N+] = NCC(O)COc1cccc2ccccc12"; example: lig_string_2 = "C#
CCOc1ccc2ccccc2c1CO"
Returns:
:returns: str ligand_new_smiles: smile string for the child ligand derived
from lig_1 and lig_2. Returns None if it failed at any point.
"""
# lig_string_1 = "[N-] = [N+] = NCC(O)COc1cccc2ccccc12"
# lig_string_2 = "C# CCOc1ccc2ccccc2c1CO"
# lig_string_1 = "C1 = CC = CC = C1"
# Sanitize
lig_smile_1 = Chem.MolFromSmiles(lig_string_1, sanitize=False)
lig_smile_2 = Chem.MolFromSmiles(lig_string_2, sanitize=False)
# Sanitize, deprotanate, and reprotanate both molecules
mol_1 = MOH.check_sanitization(lig_smile_1)
mol_2 = MOH.check_sanitization(lig_smile_2)
if mol_1 is None or mol_2 is None:
return False
protanate_step = vars["protanate_step"]
mol_1 = MOH.handleHs(lig_smile_1, protanate_step)
mol_2 = MOH.handleHs(lig_smile_2, protanate_step)
# check that handleHs() worked for both molecules if fail move on to next
# pair of molecules
if mol_1 is None or mol_2 is None:
return None
# make a list of the two rdkit.Chem.rdchem.Mol objects
mols = [mol_1, mol_2]
# Use the below mcs_H function for Most Common Substructure searching.
# This will prevent broken rings.
mcs_results = rdFMCS.FindMCS(
mols,
matchValences=False,
ringMatchesRingOnly=True,
completeRingsOnly=False,
timeout=vars["max_time_mcs_thorough"],
)
if mcs_results.canceled is True:
return None
# confirm that this meets the minimum number of matching atoms
if mcs_results.numAtoms < vars["min_atom_match_mcs"]:
return None
### Convert mcs_res from into usable and referable forms
mcs_mol = Chem.MolFromSmarts(mcs_results.smartsString)
# handle_mcs_align_labeling_and_cyclicbreaks
mol_1, mol_2, mcs_mol = AnB.handle_mcs_align_labeling_and_cyclicbreaks(
mol_1, mol_2, mcs_mol
)
# confirm that this meets the minimum number of matching atoms
if mol_1 is None or mol_2 is None or mcs_mol is None:
return None
# This is a very big step. This is where all of the atoms are fragmented
# to determine what are the R-groups options. R-groups are consolidated
# into B-groups for each molecule individually, the 2 dictionaries of
# B-groups are consolidated into a master B-group which is fed into the
# Mapping class which will randomly select a set of non-clashing B-groups,
# such that no 2 chosen B's will have connections to the same anchors
# (anchors are atoms in the common core). It returns a list of
# smilestrings for the chosen R-groups. It returns None if a step failed.
rs_chosen_smiles = DnR.handle_dicts_and_select_b_groups(mol_1, mol_2, mcs_mol)
if rs_chosen_smiles is None:
return None
# MERGE ALL CHOSEN R-GROUPS WITH THE CORE
ligand_new_mol = MWC.merge_smiles_with_core(rs_chosen_smiles, mcs_mol)
if ligand_new_mol is None:
return None
# ligand_new_smiles is either a SMILES string if processing works
# or None if processing fails
ligand_new_smiles = process_ligand_new_mol(ligand_new_mol)
return ligand_new_smiles
| 5,464 | 33.808917 | 92 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/crossover/smiles_merge/__init__.py | 0 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/crossover/smiles_merge/merge_functions/dict_and_r_groups.py | """
Dictionary and Dictionary handling functions
"""
import __future__
import copy
import rdkit
from rdkit import Chem
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
import autogrow.operators.crossover.smiles_merge.merge_functions.mapping_class as mapping_class
def handle_dicts_and_select_b_groups(mol_1, mol_2, mcs_mol):
"""
this takes 3 rdkit.Chem.rdchem.Mol objects 1 for lig_1,lig_2, and the
common core(mcs_mol). It creates all the necessary dictionaries, mapping,
and selects the ligands that will be added to make the final molecule.
Inputs:
:param rdkit.Chem.rdchem.Mol mol_1: rdkit mol for ligand 1
:param rdkit.Chem.rdchem.Mol mol_2: rdkit mol for ligand 2
:param rdkit.Chem.rdchem.Mol mcs_mol: rdkit mol for shared common core
between mol_1 and mol_2
Returns:
:returns: list rs_chosen_smiles: smiles strings for the the R groups which
correspond to the chosen B's returns None if it fails
"""
# Confirm that mcs_mol can be replaced in mol_1 and mol_2 around 0.8% of
# the time this function fails so we will filter this 1st
they_pass = check_replace_mol(mol_1, mol_2, mcs_mol)
if they_pass is False:
return None
r_smiles_dict_1, b_to_r_master_dict_1, b_to_anchor_master_dict_1 = mol_handling_of_fragmenting_labeling_and_indexing(
mol_1, mcs_mol, 1
)
# check that this worked (ie if it failed they will return None)
if r_smiles_dict_1 is None:
return None
r_smiles_dict_2, b_to_r_master_dict_2, b_to_anchor_master_dict_2 = mol_handling_of_fragmenting_labeling_and_indexing(
mol_2, mcs_mol, 2
)
# check that this worked (ie if it failed they will return None)
if r_smiles_dict_2 is None:
return None
# Merge b_to_anchor_master_dict into 1 master dictionary of B_to_anchors.
# the keys will be all be unique so we can add these dictionaries together
# without worry of overrighting an entry. We will invert the dict after to
# get anchors as the keys and the B's as the items. example
# b_to_anchor_master {'1B1':[10008,10007],'1B2':[10000],'1B3':[10006],
# '2B3':[10006,10007],'2B2':[10000],'2B1':[10008]}
b_to_anchor_master = b_to_anchor_master_dict_1
for i in list(b_to_anchor_master_dict_2.keys()):
b_to_anchor_master[i] = b_to_anchor_master_dict_2[i]
# Invert b_dictionary to produce a master I dictionary. example
# anchor_to_b_master = {10008:['1B1','2B1'],10000:['1B2','2B2']}
anchor_to_b_master = invert_dictionary(b_to_anchor_master)
bs_chosen = mapping_class.run_mapping(b_to_anchor_master, anchor_to_b_master)
# Get the R groups which correspond to the chosen B's
# ['1R1', '1R5', '2R2']
rs_chosen = get_rs_chosen_from_bs(
bs_chosen, b_to_r_master_dict_1, b_to_r_master_dict_2
)
# Get the smiles strings for the the R groups which correspond to the
# chosen B's
rs_chosen_smiles = get_rs_chosen_smiles(rs_chosen, r_smiles_dict_1, r_smiles_dict_2)
return rs_chosen_smiles
def mol_handling_of_fragmenting_labeling_and_indexing(mol, mcs_mol, lig_number):
"""
This takes an rdkit mol for a ligand and 1 for the mcs_mol. It fragments
the ligand by replacing the MCS. and it determines which anchors are in
each fragment. These fragments are our R-groups and the assignment of
anchors. is how we determine which R-group goes where relative to the MCS.
lig_number int is the number of the ligand that is mol
ie if mol is mol_1 lig_number = 1
ie if mol is mol_2 lig_number = 2
Inputs:
:param rdkit.Chem.rdchem.Mol mol: an rdkit mol (either mol_1 or mol_2)
:param rdkit.Chem.rdchem.Mol mcs_mol: rdkit mol for shared common core
between mol_1 and mol_2
:param int lig_number: an int either 1 or 2 for (mol_1 or mol_2
respectively)
Returns:
:returns: dict r_smiles_dictionary: a dictionary of the R-groups which
branch off the common core keys are the R-groups; items are the SMILES
strings of that R-groups returns None if it fails. Example: {'1R1':
'[10003*][1007N]=[1013O]', '1R2': '[10000*][1011CH2]=[1008O]'}
:returns: dict b_to_r_master_dict: A dictionary which tracks the R groups
which belong to a B-group keys are the B-groups; items are the R-groups
which belong to the B-group. returns None if it fails. Example: {'1B1':
['1R2'], '1B2': ['1R1']}
:returns: dict b_to_anchor_master_dict: A dictionary which tracks the iso
label of the anchor atoms for B-group. keys are the B-groups; items are
the iso label of the anchor atoms for B-group returns None if it fails.
Example:{'1B1': [10000], '1B2': [10003]}
"""
# Find which MCS Atoms Rs branch from Function to find all neighbors for a
# set of molecules touching an Isolabeled core
mcs_touches = get_atoms_touch_mcs(mol)
# invert dictionary
lig_r_atoms_touch_mcs = invert_dictionary(mcs_touches)
# remove the Core atoms from each ligand this gives us the R-groups
replace_core = r_group_list(mol, mcs_mol)
if replace_core is None:
# replace_core failed to handle fragments"
return None, None, None
replace_core = replace_core_mol_dummy_atoms(mol, mcs_mol, replace_core)
if replace_core is None:
# replace_core failed to handle fragments"
return None, None, None
# A single anchor (isotope label) may now be present multiple times in the
# replace_core_mols as they are fragmented replace_frag_w_anchor_isolabels
# can return a None if failed so lets check for None before we move on
if replace_core is None:
# replace_core failed to handle fragments"
return None, None, None
# MAKE NEW MOL FRAGS FROM LABELED replace_core
mol_frags = Chem.GetMolFrags(replace_core, asMols=True, sanitizeFrags=False)
list_r_groups = []
i = 0
while i < len(mol_frags):
val = Chem.MolToSmiles(mol_frags[i], isomericSmiles=True)
list_r_groups.append(val)
i = i + 1
# Generate all the R-libraries with full R-groups using the index of its
# respective Lig r_chain_dictionary is the master dictionary for R-groups
r_chain_dictionary, r_smiles_dictionary = r_groups_dict(mol_frags, lig_number)
# r_dict is a secondary dictionary for searching I's in R's. this
# dictionary is limited to only the R-group and anchor(I).
r_dict = get_r_dict(r_chain_dictionary, lig_r_atoms_touch_mcs)
# make inversion of r_dict. keys are the Anchor atom iso_labels while the
# items are the R-group numbers which are attached to that anchor atom.
# Example: {10008: ['2R3'], 10000: ['2R2'], 10006: ['2R1'], 10007:
# ['2R1']}
i_dict = invert_dictionary(r_dict)
"""
B-dictionaries:
Ligmerge will randomly select R-groups to append to a shared common core
from 2 separate ligands but so anchor atoms in the common core may have
more than 1 R-group attached to it.
ie. if an anchor carbon has di-methyls attached to it (which are not part
of the shared core) are these di-methyls 2 separate R-groups or is the
contextual chemical environment created by having both methyls
different from having one alone and thus should be treated as a single
R-group which just happen to branch. This author would argue that
context is important here and so this version of Ligmerge treats
anything attached to an anchor atom in the common core as a singular
contextual functional group which shall be referred to as a B-groups.
ie. a B-group consists of 1 or more R-groups which are attached to an
anchor atom in the shared common core. This makes a significant
difference in how we select for which pieces are added to build our
child molecule. Additionally this has significance in the decision
tree use to build a child molecule. An R/B group can be connected to
multiple anchor atoms so once we chose a B group we will need to know
which anchor atoms are affected by that decision. This is something
handled more in the Mapping class, but this is why the nomenclature
change from R-groups to B-groups and why the next several steps are
important.
make_b_dictionaries (B is the name we gave to R-groups sets)
"""
b_to_r_master_dict, b_to_anchor_master_dict = make_b_dic(i_dict, r_dict, lig_number)
return r_smiles_dictionary, b_to_r_master_dict, b_to_anchor_master_dict
def check_replace_mol(mol_1, mol_2, mcs_mol):
"""
Confirm that mcs_mol can be replaced in mol_1 and mol_2 around 0.8% of the
time this function fails so we will filter this 1st
Inputs:
:param rdkit.Chem.rdchem.Mol mol_1: an rdkit mol
:param rdkit.Chem.rdchem.Mol mol_2: an rdkit mol
:param rdkit.Chem.rdchem.Mol mcs_mol: rdkit mol for shared common core
between mol_1 and mol_2
Returns:
:returns: bool True/False: Returns True if it passes for both mol_1 and
mol_2 returns False if either fails.
"""
temp = r_group_list(mol_1, mcs_mol)
if temp is None:
return False
temp = r_group_list(mol_2, mcs_mol)
if temp is None:
return False
return True
###########################################################
###########################################################
# HANDLE THE OBTAINING THE R-Groups for a given mol
def r_group_list(mol, core_mol):
"""
This takes a mol and the common core and finds all the R-groups by
replacing the atoms in the ligand (which make up the common core) with
nothing.
This fragments the ligand and from those fragments we are able to
determine what our R-groups are. for any common core atom which touched
the fragment a * will replace that atom in the fragments.
Inputs:
:param rdkit.Chem.rdchem.Mol mol: an rdkit molecule
:param rdkit.Chem.rdchem.Mol core_mol: an rdkit molecule for the shared
common core
Returns:
:returns: rdkit.Chem.rdchem.Mol replace_core_mol: an rdkit molecule with
the common core removed from a ligand fragments the mol which can be used
to make lists of R-groups
"""
# This returns all the mol frags for a particular compound against the
# core molecule
replace_core_mol = Chem.ReplaceCore(
mol, core_mol, labelByIndex=True, replaceDummies=True, requireDummyMatch=False
)
if len(replace_core_mol.GetAtoms()) == 0:
# This means that the mol either did not contain the core_mol or the
# core_mol is the same mol as the mol. ie) if mol_string
# ="[10000N-]=[10001N+]=[10002N][10003CH]1[10004O][10005CH]([10006CH2][10007OH])[10008CH]([10013OH])[10009CH]([10012OH])[10010CH]1[10011OH]"
# and core_string
# ="[10000NH]=[10001N+]=[10002N][10003CH]1[10004O][10005CH]([10006CH2][10007OH])[10008CH]([10013OH])[10009CH]([10012OH])[10010CH]1[10011OH]"
# the only difference is the H's which means it can be replaced within
# because its the same mol This is rare but does occur.
return None
return replace_core_mol
def replace_core_mol_dummy_atoms(mol, mcs, replace_core_mol):
"""
This function will replace the dummy atoms (*) with the isotope label from
the core atoms. example:
mol = Chem.MolFromSmiles("[10000N-]=[10001N+]=[10002N][10003CH2][2004CH]1[2005NH2+][2006CH2][2007CH]([2008OH])[2009CH]([2010OH])[2011CH]1[2012OH]")
mcs = Chem.MolFromSmiles("[10003CH3][10002N]=[10001N+]=[10000NH]")
replace_core = Chem.MolFromSmiles("[3*][2004CH]1[2005NH2+][2006CH2][2007CH]([2008OH])[2009CH]([2010OH])[2011CH]1[2012OH]")
resulting replace_core = '[10003*][2004CH]1[2005NH2+][2006CH2][2007CH]([2008OH])[2009CH]([2010OH])[2011CH]1[2012OH]'
Inputs:
:param rdkit.Chem.rdchem.Mol mol: an rdkit molecule
:param rdkit.Chem.rdchem.Mol mcs: an rdkit molecule for the shared common
core
:param rdkit.Chem.rdchem.Mol replace_core_mol: the mol with the MCS
anchors labeled with * and an isotope label of the idx of the core anchor
atom
Returns:
:returns: rdkit.Chem.rdchem.Mol replace_core_mol: an rdkit molecule with
the common core removed from a ligand fragments the mol which can be used
to make lists of R-groups. The * atoms will be isotope labeled with the
isotope label from the core.
"""
replace_core_mol_original = copy.deepcopy(replace_core_mol)
anchor_dict = {}
anchor_to_set_dict = {}
for atom in replace_core_mol.GetAtoms():
if atom.GetAtomicNum() == 0:
anchor_iso = atom.GetIsotope() + 10000
neighbors = atom.GetNeighbors()
tmp = []
for n_atom in neighbors:
tmp.append(n_atom.GetIsotope())
anchor_dict[anchor_iso] = tmp
anchor_to_set_dict[atom.GetIdx()] = anchor_iso
for idx in list(anchor_to_set_dict.keys()):
atom = replace_core_mol.GetAtomWithIdx(idx)
anchor_iso = anchor_to_set_dict[idx]
atom.SetIsotope(anchor_iso)
return replace_core_mol
def r_groups_dict(mol_frags, lig_number_for_multiplier):
"""
given a set of mol_frags and the ligand_number (ie. 1 for mol_1 and 2 for
mol_2) this will make dictionaries of all the Rgroup and all the smiles
for each Rgroup
Input
:param rdkit.Chem.rdchem.Mol mol_frags: a rdkit mol containing fragments
:param int lig_number_for_multiplier: an int either 1 for mol_1 or 2 for
mol_2, used to make labels which are traceable to the ligand being used
Returns:
:returns: dict r_chain_dictionary: a dictionary with the R-groups and the
anchor atoms they connect to ie) {'1R1':[13,14],'1R2':[21,22],'1R3':[25]}
:returns: dict r_smiles_dictionary: a dictionary with the R-groups and the
SMILES strings of those groups ie
{'1R1':'[1*]:[1013c]([1020H])[1014c]([1019H])[1015c]([1018H])[1016c](:[2*])[1017H]',
'1R2':'[3*][1024C]([1026H])([1027H])[1023N] = [1022N+] = [1021N-]',
'1R3':'[4*][1025O][1029H]'}
"""
num_frags = len(mol_frags)
i = 0
r_chain_dictionary = {}
r_smiles_dictionary = {}
k = int(lig_number_for_multiplier)
while i < num_frags:
frag = mol_frags[i]
r_list_temp = []
r_list_smiles = Chem.MolToSmiles(frag, isomericSmiles=True)
for atoms in frag.GetAtoms():
iso = atoms.GetIsotope()
if 3000 > iso > 100:
r_list_temp.append(iso - (1000 * k))
atoms.SetIsotope(0)
if iso > 3000:
name = "I{}".format(iso - 10000)
r_list_temp.append(iso)
lig_num_r_r_num = "{}R{}".format(k, i + 1)
r_chain_dictionary[lig_num_r_r_num] = r_list_temp
r_smiles_dictionary[lig_num_r_r_num] = r_list_smiles
i = i + 1
return r_chain_dictionary, r_smiles_dictionary
def get_r_dict(r_chain_dict, lig_r_atom_touch_mcs):
"""
This will take the r_chain_dict and the dict of all the atoms which touch
the core and return a dict of Rs groups as keys and their nodes as values
Inputs:
:param dict r_chain_dict: dict of all the atom isolabels for in an
R-group. keys are R-groups; items are iso-labels of atoms in the R-group.
ie) {'1R1': [3, 4, 5, 6, 7, 8, 9, 10, 11, 10000]}
:param dict lig_r_atom_touch_mcs: dict of all the atoms which directly
touch the core and what anchor they touch. keys are atom isolabels of
atoms touching an anchor; items are iso-labels of anchor atoms. ie) {3:
[10000]}
Returns:
:returns: dict r_s_dict: dictionary of R-groups and anchor atoms they are
connected to. keys are R-groups. items are isolabel of anchor atoms. ie)
{'1R1': [10000]}
"""
r_s_dict = {}
for key in list(r_chain_dict.keys()):
temp_r_list = r_chain_dict[key]
node_list = []
for atom in r_chain_dict[key]:
for key_id in list(lig_r_atom_touch_mcs.keys()):
if atom == key_id:
for x in lig_r_atom_touch_mcs[key_id]:
node_list.append(x)
r_s_dict[key] = node_list
return r_s_dict
##########
# Mapping functions and finding neighbors
#########
def get_idx_using_unique_iso(mol, iso_val):
"""
This function takes a value for an isotope label and finds the atom in a
mol which has that isotope label. This assumes there is only 1 atom in a
mol with the same isotope value
Inputs:
:param rdkit.Chem.rdchem.Mol mol: a molecule whose atom's have unique
isotope labels
:param int iso_val: the isotope value to search by
Returns:
:returns: int idx: the Idx index number of the atom whose isotope label
is the same as iso_val. Returns None if iso_val not in mol.
"""
for atom in mol.GetAtoms():
if atom.GetIsotope() == iso_val:
idx = atom.GetIdx()
return idx
return None
def make_b_dic(i_dictionary, r_dict_num, lig_number):
"""
This generates the dictionaries for the B-groups. one is to track the
R-groups which a B-group represents (this is the b_to_r_master_dict). one
is to track the anchor atoms a B-group branches from (this is the
b_to_anchor_master_dict).
Inputs:
:param dict i_dictionary:dictionary for R groups bound to nodes (aka I's).
ie) {'10008':[1R1,1R2],'10009':[1R2,1R3]}
:param dict r_dict_num: dictionary for anchors which are attached to an R
group. ie) {'1R1':[10008],'1R2':[10008,10009],'1R3':[10009]}
:param int lig_number: an int either 1 or 2 for (mol_1 or mol_2
respectively)
Returns:
:returns: dict b_to_r_master_dict: key is unique B-name and the R-groups
it represents. example {'1B1':['1R1'],'1B2':['1R2','1R3','1R4'],'1B3':
['1R5']}
:returns: dict b_to_anchor_master_dict: key is unique B-name and items are
anchors that B connects to. example
{'1B1':[10008,10007],'1B2':[10000],'1B3':[10006]}
"""
k = lig_number
b_to_r_master_dict = {}
b_to_anchor_master_dict = {}
counter = 1
anchor_list = list(i_dictionary.keys())
# anchor_list = [10008, 10000, 10006, 10007]
while len(anchor_list) > 0:
anchor = anchor_list[0]
B_key = "{}B{}".format(k, counter)
temp_r_list = []
temp_anchor_list = []
for Rs in i_dictionary[anchor]:
# example Rs in i_dictionary[anchor]: '1R1')
temp_r_list.append(Rs)
r_dict_i = r_dict_num[Rs]
for I in r_dict_i:
# example Rs in i_dictionary[anchor]: '1R1')
temp_anchor_list.append(I)
# remove any redundancies in the list by list(set(list_of_things))
temp_anchor_list = list(set(temp_anchor_list))
temp_r_list = list(set(temp_r_list))
# make new B-group entry in the dictionaries
b_to_r_master_dict[B_key] = temp_r_list # This B-represents these R-groups
b_to_anchor_master_dict[
B_key
] = temp_anchor_list # This B connects to these anchor atoms
counter = counter + 1
# make a list of atoms to remove in the next iteration if they are in
# both the temp_anchor_list and anchor_list
for i in temp_anchor_list:
if i in anchor_list:
anchor_list.remove(i)
# example
# b_to_r_master_dict:{'1B1':['1R1'],'1B2':['1R2','1R3','1R4'],'1B3':
# ['1R5']}. example
# b_to_anchor_master_dict:{'1B1':[10008,10007],'1B2':[10000],'1B3':[10006]}.
return b_to_r_master_dict, b_to_anchor_master_dict
def invert_dictionary(old_dic):
"""
This will invert any dictionary so that the keys are the values and the
values are the keys.
Inputs:
:param dict old_dic: a dictionary to invert
Returns:
:returns: dict inverted_dic: old_dict dict inverted so the keys are the
items and the items are the keys
"""
# inverted_dic = {}
# for k, v in old_dic.iteritems():
# keys = inverted_dic.setdefault(v, [])
# keys.append(k)
values = set([a for b in list(old_dic.values()) for a in b])
values = list(values)
inverted_dic = dict(
(new_key, [key for key, value in list(old_dic.items()) if new_key in value])
for new_key in values
)
return inverted_dic
def get_atoms_touch_mcs(mol):
"""
Function to find all neighbors for a set of molecules touching. Isolabeled
core atoms.
Inputs:
:param rdkit.Chem.rdchem.Mol mol: isolabeled with atoms in the core having
isotope. labels set as their idx number + 10000 and atoms not shared in
the common core isotope labels set as:
for lig_1: atom idx number + 1000
for lig_1: atom idx number + 2000
Returns:
:returns: dict mcs_touches dict: a dictionary with keys being the isotope
label of core atoms and the items being the idx's of all non-core atoms
which touch it. If a core atom touch no non-core atoms it will not be
added to the dictionary.
"""
mcs_touches = {}
all_atoms = mol.GetAtoms()
for atom in all_atoms:
# find all atoms in the mol which are also in the Core using iso
# labels
iso = atom.GetIsotope()
if iso > 9999:
# then its a core atom
neighbors = atom.GetNeighbors()
values = []
for neighbor_atom in neighbors:
# compile list of the Indexes of all neighbor of the atom
Idx_neighbor = neighbor_atom.GetIdx()
# Select for only neighbors which are not in the core using
# iso
iso_neighbor_x = neighbor_atom.GetIsotope()
if iso_neighbor_x < 9999:
# Then this is an atom which is not in the core but
# touches the core
idx_of_neighbor = neighbor_atom.GetIdx()
values.append(idx_of_neighbor)
mcs_touches[iso] = values
return mcs_touches
##########
# Handling after B-groups are chosen
##########
def get_rs_chosen_from_bs(bs_chosen, b_to_r_master_dict_1, b_to_r_master_dict_2):
"""
this function returns a list of R-groups chosen based on the list of
chosen B's. It requires the b_to_r_master_dict_1 for both ligands to
function.
Inputs:
:param list bs_chosen: A list of the chosen B-groups. ie) ['1B1', 1B2',
'2B3']
:param dict b_to_r_master_dict_1: a Dictionary to reference B and R-groups
from mol_1. keys are names of B-groups; items are R-groups that a B-group
represents. ie) {'1B1':['1R1'],'1B2':['1R2','1R3','1R4'],'1B3': ['1R5']}
:param dict b_to_r_master_dict_2: a Dictionary to reference B and R-groups
from mol_2. keys are names of B-groups; items are R-groups that a B-group
represents. ie) {'2B1':['2R1'],'2B2':['2R2','2R3','2R4'],'2B3':
['2R5','2R6]}
Returns:
:returns: list rs_chosen: a list containing all the R-groups represented
by the chosen B-groups. ie) ['1R1', '1R2', '1R3','1R4', '2R5', '2R6']
"""
rs_chosen = []
for B in bs_chosen:
Rs_for_the_B = []
lig_number = B[0]
B_number = B[2]
if lig_number == str(1):
for i in b_to_r_master_dict_1[B]:
Rs_for_the_B.append(i)
elif lig_number == str(2):
for i in b_to_r_master_dict_2[B]:
Rs_for_the_B.append(i)
for i in Rs_for_the_B:
rs_chosen.append(i)
# rs_chosen looks like ['1R1', '1R5', '2R2']
return rs_chosen
def get_rs_chosen_smiles(rs_chosen, r_smiles_dict_1, r_smiles_dict_2):
"""
This function returns a list of SMILES strings for every R-group chosen.
It requires the R_smile_dictionary for both ligands to function.
Inputs:
:param list rs_chosen: A list of the chosen R-groups which will be used to
generate a new mol. ie) ['2R2', '1R1']
:param dict r_smiles_dict_1: A dictionary which has can find the SMILES
string for each R-group of Ligand 1. ie) {'1R1':
'[10006*][1009N]=[1008N+]=[1007N-]'}
:param dict r_smiles_dict_2: A dictionary which has can find the SMILES
string for each R-group of Ligand 2. ie) {'2R2': '[10006*][2009OH]',
'2R1': '[10003*][2007CH2][2008OH]'}
Returns:
:returns: list rs_chosen_smiles: A list of all the SMILES string which are
to be added to make the child ligand. Each SMILES is a sublist.
ie)[['[10006*][1009N]=[1008N+]=[1007N-]'],['[10006*][2009OH]']]
"""
rs_chosen_smiles = []
for R in rs_chosen:
Rs_for_the_R = []
lig_number = R[0]
R_number = R[2]
if lig_number == str(1):
Rs_for_the_R.append(r_smiles_dict_1[R])
elif lig_number == str(2):
Rs_for_the_R.append(r_smiles_dict_2[R])
rs_chosen_smiles.append(Rs_for_the_R)
return rs_chosen_smiles
| 25,351 | 38.244582 | 155 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/crossover/smiles_merge/merge_functions/alignment_and_breaks.py | """This handles alignment and ring breaks for Crossover"""
import __future__
import random
import copy
import rdkit
from rdkit import Chem
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
import autogrow.operators.convert_files.gypsum_dl.gypsum_dl.MolObjectHandling as MOH
# handle_mcs_alignments_labeling_and_cyclicbreaks
def handle_mcs_align_labeling_and_cyclicbreaks(mol_1, mol_2, mcs_mol):
"""
This will take 2 aligned molecules and pick a specific alignment, check
for any ring and cyclic breaks and fragmentation removing any atoms from
the common core which caused those issues, renumber and isotope atoms in
mol_1, mol_2, mcs_mol to be tractable, and consistent amongst the three.
Inputs:
:param rdkit.Chem.rdchem.Mol mol_1: an rdkit molecule
:param rdkit.Chem.rdchem.Mol mol_2: an rdkit molecule
:param Chem.MolFromSmarts(mcs_res_SMART) mcs_mol: a result object from
rdkits MCS function
Returns:
:returns: rdkit.Chem.rdchem.Mol mol_1: an rdkit molecule isolabeled and
renumbered or None if it fails
:returns: rdkit.Chem.rdchem.Mol mol_2: an rdkit molecule isolabeled and
renumbered or None if it fails
:returns: mcs_res_Mol mcs_mol: an MCS result isolabeled and no breaks or
None if it fails
"""
if type(mol_1) is not rdkit.Chem.rdchem.Mol:
return None, None, None
if type(mol_2) is not rdkit.Chem.rdchem.Mol:
return None, None, None
if type(mcs_mol) is not rdkit.Chem.rdchem.Mol:
return None, None, None
# Set Alignment, Isotope label, and Handle breaks
picked_alignment = pick_mcs_alignment(mol_1, mol_2, mcs_mol)
if picked_alignment is None:
return None, None, None
# Isotope label the MCS core
index_tuple = add_mcs_isolabels(mol_1, mol_2, mcs_mol, picked_alignment)
## Reorder all atoms within ligands to be consistent
mol_1 = renumber_to_mcs(mol_1, picked_alignment[0])
mol_2 = renumber_to_mcs(mol_2, picked_alignment[1])
#### CHECK FOR RING BREAKS AND FRAGMENTATION BEFORE CONTINUING FORWARD
# SYSTEM ERRORS MAY OCCUR HERE WHEN MCS IS BAD
# Check cyclic ring breaks, if break remove atoms from common core
mcs_mol, new_index, are_there_breaks = check_cyclic_breaks(
index_tuple, mol_1, mol_2, mcs_mol
)
# prevents None type errors
if mcs_mol is None:
return None, None, None
# Check cyclic ring breaks, if break remove atoms from common core
if are_there_breaks is True:
# Run cyclic ring break again, if there are still breaks create sys
# error and abort merge
mcs_m3, new_index_2, are_there_breaks_2 = check_cyclic_breaks(
new_index, mol_1, mol_2, mcs_mol
)
if are_there_breaks_2 is True:
return None, None, None
# prevents None type errors
if mcs_mol is None:
return None, None, None
# Check for any additional fragmentation issues
confirm_no_frag = Chem.GetMolFrags(mcs_mol, asMols=True, sanitizeFrags=False)
if len(confirm_no_frag) != 1:
return None, None, None
# Renumber all atoms within both ligands to be consistent with the core
mol_1 = renumber_to_mcs(mol_1, new_index[0])
mol_2 = renumber_to_mcs(mol_2, new_index[1])
# Add correct isolabels to the core atoms. requires 1 final fake alignment
# tuple of range(0, len(mcs_mol.GetNumAtoms()))
final_alignment = (new_index[2], new_index[2], new_index[2])
# Isotope label the core
index_tuple = add_mcs_isolabels(mol_1, mol_2, mcs_mol, final_alignment)
# label the 1st atom in R-group with its idx + (1000 for lig1 and 2000 for
# lig 2) the 1000 vs 2000 label will be used to deconvelute later. these
# will be the iso values assigned to the 1st atom in an R-group.
add_r_atom_isolabels(mol_1, mol_2)
return mol_1, mol_2, mcs_mol
##########################
# CYCLE BREAKS AND REMOVE BAD ATOMS FUNCTIONS
##########################
# USE THESE FUNCTION TO TEST FOR CYCLIC BREYCLE BREAK AND REMOVE BAD ATOMS
# FUNCTIONS
##########################
def check_cyclic_breaks(alignment_tuple, mol_1, mol_2, core):
"""
Check for cyclic breaks and fixes them.
Fixing cyclic breaks is handled by removing the atoms from the core (MCS
substructure). Removing atoms will often cause fragmentation (ie. removing
a carbon from a methyl will leave 3 fragmented hydrogens). Fragmented
atoms need to be removed. The largest fragment is assumed to be the core
of interest which we don't want to remove.
Inputs:
:param tuple alignment_tuple: a tuple with the atoms with IDX which match
in the same order ie. alignment_tuple[0][0] is the IDX for the atom in
mol_1 which is matched to alignment_tuple[1][0] and alignment_tuple[2][0]
(for mol_2) alignment_tuple[3] (for core) is the reference index which
ranges from 0 to the number of atoms in the order (0,1,2,3,4...n)
:param rdkit.Chem.rdchem.Mol mol_1: rdkit mol for ligand 1
:param rdkit.Chem.rdchem.Mol mol_2: rdkit mol for ligand 2
:param rdkit.Chem.rdchem.Mol core: rdkit mol for shared common core
between mol_1 and mol_2
Returns:
:returns: rdkit.Chem.rdchem.Mol core: the original core rdkit mol returned
if did_a_ring_break is False
:returns: tuple alignment_tuple: the unaltered input param alignment_tuple
returned if did_a_ring_break is False
:returns: bool did_a_ring_break: True if the ring broke and was fixed;
False if there were no breaks and required no modifications to be made to
the alignment_tuple or core
:returns: rdkit.Chem.rdchem.Mol new_core: the modified core rdkit mol
returned if did_a_ring_break is True
:returns: tuple new_align_tuple: the modified alignment_tuple returned if
did_a_ring_break is True
:returns: bool None: returns 3 Nones if it failed to fix the cyclic breaks
"""
if type(mol_1) is not rdkit.Chem.rdchem.Mol:
return None, None, None
if type(mol_2) is not rdkit.Chem.rdchem.Mol:
return None, None, None
if type(core) is not rdkit.Chem.rdchem.Mol:
return None, None, None
mcs_ringbreak_idx = []
mol_1_ringbreak_idx = []
mol_2_ringbreak_idx = []
for l1, l2, c1 in zip(alignment_tuple[0], alignment_tuple[1], alignment_tuple[2]):
atom1 = mol_1.GetAtomWithIdx(l1)
atom2 = mol_2.GetAtomWithIdx(l2)
atom_c = core.GetAtomWithIdx(c1)
# ring breaks can occur when an atom in either lig is a ring atom
# but the common substructure has that as a non-ring atom
if atom_c.IsInRing() is False and (
atom1.IsInRing() is True or atom2.IsInRing() is True
):
mcs_ringbreak_idx.append(l1)
mol_1_ringbreak_idx.append(l2)
mol_2_ringbreak_idx.append(c1)
if len(mcs_ringbreak_idx) > 0:
new_align_list_l1 = []
new_align_list_l2 = []
new_align_list_c1 = []
# THIS IS A BIT COMPLEX HERE SO THIS IS THE IDEA: Using the list of
# ringbreak idx's we will delete those ringbreak atoms from the core.
# This will change the idx's within the core which is why we've
# iso-labeled the core in previous steps. Once we delete from the core
# we need to detemine the idx's of fragmented atoms but these will be
# the idx's in the original core, as determined by the isotope labels.
# ie. idx_of_atom_in_original_core = atom.GetIsotope()-10000 (We keep
# the largest fragment as the future core).
# After we have a list of all the atom idx's (in the original core)
# for all the atoms which are ringbreaks and all the atoms that will
# cause cyclicbreaks/fragmentation we will then delete all those atoms
# at once, thus preventing issues of the idx's changing as we delete
# and issues of fragmentation.
# Make a copy of the core to test fragments
temp_core_removed_breaks = copy.deepcopy(core)
# delete atoms causing ringbreaks
temp_core_removed_breaks = MOH.remove_atoms(
temp_core_removed_breaks, mcs_ringbreak_idx
)
if temp_core_removed_breaks is None:
return None, None, None
# check for fragmentation and add the smallest fragments indexes to
# the to delete list
all_atoms_to_delete = ringbreak_frag_handling(
temp_core_removed_breaks, mcs_ringbreak_idx
)
if all_atoms_to_delete is None:
return None, None, None
# Now work on the original core. THIS WILL BE THE OFFICIAL NEW CORE.
# delete any cyclic breaks or anything connected to a cyclic break
# which would fragment only delete from core mol
new_core = MOH.remove_atoms(core, all_atoms_to_delete)
if new_core is None:
return None, None, None
new_core = MOH.check_sanitization(new_core)
if new_core is None:
return None, None, None
# now that we've made a new core, the idx's are different so we need
# to relabel mol_1 and mol_2
# remove the Iso-labels from lig 1 and 2 for anything deleted
remove_iso_labels(mol_1, all_atoms_to_delete)
remove_iso_labels(mol_2, all_atoms_to_delete)
# make a new index series for comparing mol_1 and mol_2 to the core.
# this is done using the original indexing and the atoms which were
# removed from mcs.
count = 0
for l1, l2, c1 in zip(
alignment_tuple[0], alignment_tuple[1], alignment_tuple[2]
):
if c1 not in all_atoms_to_delete:
new_align_list_l1.append(l1)
new_align_list_l2.append(l2)
new_align_list_c1.append(count)
count = count + 1
new_align_tuple = (new_align_list_l1, new_align_list_l2, new_align_list_c1)
did_a_ring_break = True
return new_core, new_align_tuple, did_a_ring_break
# len(mcs_ringbreak_idx) less than or equal to 0
did_a_ring_break = False
return core, alignment_tuple, did_a_ring_break
def ringbreak_frag_handling(new_core, mcs_ringbreak_idx):
"""
This takes a rdkit mol of the core (after atoms were removed to resolve
cyclic breaks). It then tests and handles ringbreaks and fragmentation.
if an atom in the core was deleted, if it had additional atoms attached to
it it can cause fragmenation. so this step handles that and takes the
largest fragment as the new common core.
Inputs:
:param rdkit.Chem.rdchem.Mol new_core: common core mol object
:param list mcs_ringbreak_idx: list of the idx's of the common core for
iso labels and later adjustment iso labels and idx numbers
Returns:
:returns: list iso_core_frag_list: list of the idx's of common core; same
as mcs_ringbreak_idx unless there was fragmentation that needed to be
handled.
"""
##########################
# Check for fragmentation, if core is fragmented than additional
# processing is required to find the largest frag and to then reassign the
# indexes in the ligs and core
check_fragmentation = Chem.GetMolFrags(new_core, asMols=True, sanitizeFrags=False)
num_frag_len = len(check_fragmentation)
iso_core_frag_list = copy.deepcopy(mcs_ringbreak_idx)
if num_frag_len > 1:
# determine the largest fragment in the list of frags
largest_frag, largest_frag_index_num = find_biggest_frag(check_fragmentation)
# the core is now the largest fragment
core = check_fragmentation[largest_frag_index_num]
# make a list without the largest fragment
list_frag_mols = []
list_of_frag_idxs = range(0, len(check_fragmentation))
for i in list_of_frag_idxs:
if i == largest_frag_index_num:
continue
frag = check_fragmentation[int(i)]
list_frag_mols.append(frag)
# get the idx for all atoms in all frags EXCEPT THE LARGEST FRAG.
# these will be the idx's of the original common core, before deleting
# things which will be identified using the Isolabels we added before.
# We will be deleting these atoms shortly
for frag in list_frag_mols:
# get all atom idx's (for the original unaltered common_core) in
# the frag based on the Iso-labels we added before
for atoms in frag.GetAtoms():
index_val = atoms.GetIsotope() - 10000
iso_core_frag_list.append(index_val)
# Remove redundancy
iso_core_frag_list = list(set(iso_core_frag_list))
return iso_core_frag_list
# if no fragmentation occured
return iso_core_frag_list
#
def find_biggest_frag(frag_mols_obj):
"""
This will take a frag mol object and return the largest fragment and the
index in frag_mols_obj.
Inputs:
:param tuple frag_mols_obj: A tuple containing all the fragments of an
rdkit mol.
Returns:
:returns: rdkit.Chem.rdchem.Mol frag_mols_obj: The largest rdkit mol obj
in the provided tuple
:returns: int idx_of_max: the idx number of the largest rdkit mol obj in
the provided tuple.
"""
if len(frag_mols_obj) > 1:
idx_of_max = None
num_atoms_max = None
for i in range(0, len(frag_mols_obj)):
frag = frag_mols_obj[i]
atom_count = frag.GetNumAtoms()
if num_atoms_max is None:
idx_of_max = i
num_atoms_max = atom_count
elif num_atoms_max < atom_count:
idx_of_max = i
num_atoms_max = atom_count
else:
continue
return frag_mols_obj, idx_of_max
else:
return frag_mols_obj, 0
def remove_iso_labels(mol, list_of_idx_to_remove):
"""
Given a molecule and a set of idx numbers this will remove the isotope
labels for atoms with the idx numbers in the list.
Inputs:
:param rdkit.Chem.rdchem.Mol mol: rdkit mol which needs the iso labeles
removed
:param list list_of_idx_to_remove: a list of the idx's which need the
isolabels removed
"""
for i in list_of_idx_to_remove:
atom = mol.GetAtomWithIdx(i)
atom.SetIsotope(0)
def add_r_atom_isolabels(mol_1, mol_2):
"""
Label the 1st atom in R-group with its idx + (1000 for lig1 and 2000 for
lig 2) the 1000 vs 2000 label will be used to deconvelute later. These
will be the iso values assigned to the 1st atom in an R-group
Inputs:
:param rdkit.Chem.rdchem.Mol mol_1: rdkit mol for ligand 1
:param rdkit.Chem.rdchem.Mol mol_2: rdkit mol for ligand 2
"""
# isotope label mol_1
for atom in mol_1.GetAtoms():
if atom.GetIsotope() < 9999:
atom_iso = atom.GetIdx() + 1000
atom.SetIsotope(atom_iso)
# isotope label mol_2
for atom in mol_2.GetAtoms():
if atom.GetIsotope() < 9999:
atom_iso = atom.GetIdx() + 2000
atom.SetIsotope(atom_iso)
# alignment and labeling
def pick_mcs_alignment(mol_1, mol_2, common_core):
"""
This will take the common substructure (aka the common_core) and find
every match to it within each of the two ligands and produce to list of
tuples for the atom index (Idx) relative to the indexing of the
common_core substrure.
It will then randomly pick combination of an alignment for mol_1 and
mol_2.
This should pick the alignments for future numbering.
This should return picked_alignment (tuple)
picked_alignment[0] is alignment for mol_1
picked_alignment[1] is alignment for mol_2
picked_alignment[2] is the atom idx of the common_core
ie. picked_alignment[0][0] is the atom IDx for the atom in mol_1 which
corresponds to the atom in the common substructure index as 0
picked_alignment[0][1] is the atom IDx for the atom in mol_1 which
corresponds to the atom in the common substructure index as 1
picked_alignment[1][0] is the atom IDx for the atom in mol_2 which
corresponds to the atom in the common substructure index as 0
picked_alignment[1][1] is the atom IDx for the atom in mol_2 which
corresponds to the atom in the common substructure index as 1
Inputs:
:param rdkit.Chem.rdchem.Mol mol_1: rdkit mol for ligand 1
:param rdkit.Chem.rdchem.Mol mol_2: rdkit mol for ligand 2
:param rdkit.Chem.rdchem.Mol common_core: rdkit mol for the shared core
between mol_1 and mol_2
Returns:
:returns: tuple picked_alignment: tuple with 3 sub lists of the atoms IDx
which correspond to their respective mol object in the order that they
match the atom in the same index in the sublist for all 3 mols
(mol_1,mol_2,common_core)
"""
# Get the substructure match for the MCS within each ligand
mol_1_match_idx = mol_1.GetSubstructMatches(
common_core, uniquify=False, maxMatches=10
)
mol_2_match_idx = mol_2.GetSubstructMatches(
common_core, uniquify=False, maxMatches=10
)
all_drug_pairings = []
for mol_1_match in mol_1_match_idx:
for mol_2_match in mol_2_match_idx:
all_drug_pairings.append((mol_1_match, mol_2_match))
# Check that it is a list of tuples. otherwise the random.choice function
# breaks in python 2.7
if type(all_drug_pairings) != list:
return None
else:
if len(all_drug_pairings) == 0 or type(all_drug_pairings[0]) != tuple:
return None
if len(all_drug_pairings[0]) == 0:
return None
# chose an alignment
alignment_choice = random.choice(all_drug_pairings)
# add the common_core atom idx to the tuple
substruc_idx = tuple(list(range(0, len(common_core.GetAtoms()))))
picked_alignment = (alignment_choice[0], alignment_choice[1], substruc_idx)
return picked_alignment
###### Index and convert Ligs w isotope labels
def add_mcs_isolabels(mol_1, mol_2, common_core, picked_alignment):
"""
This will modify every atom in mol_1, mol_2, and the common_core to have
the same isotope labels based on the index of the common_core atoms.
Isotope number is set as the index number for the common_core atoms + 10,000
Input
:param rdkit.Chem.rdchem.Mol mol_1: an rdkit molecule
:param rdkit.Chem.rdchem.Mol mol_2: an rdkit molecule
:param Chem.MolFromSmarts(mcs_res_SMART) common_core: mcs_res_Mol
:param tupple picked_alignment: a tuple with 3 subtuples for
mol_1,mol_2,common_core. The numbers within the sublist is the atom IDx
for a given atom in a ligand. The sublist index for each atom in for
picked_alignment corresponds to the Idx of that atoms match in the
commmon_core. ie picked_alignment[1][3] = 10; thus the IDx atom in mol_2
which corresponds to the 3rd atom in the common_core is 10.
Returns:
:returns: tuple final_index: tuple with three sublists which are the same
as picked_alignment[2]).
"""
i = 0
index_list = []
for lig1, lig2, c1 in zip(
picked_alignment[0], picked_alignment[1], picked_alignment[2]
):
atom1 = mol_1.GetAtomWithIdx(lig1)
atom2 = mol_2.GetAtomWithIdx(lig2)
atom_c = common_core.GetAtomWithIdx(c1)
atom1.SetIsotope(10000 + i)
atom2.SetIsotope(10000 + i)
atom_c.SetIsotope(10000 + i)
index_list.append(i)
i = i + 1
final_index = index_list, index_list, index_list
return final_index
def renumber_to_mcs(mol, tuple_order_list):
"""
This renumbers the indexes of the atoms in a lig to that of the MCS and
returns a renumbered atom.
Inputs:
:param rdkit.Chem.rdchem.Mol mol: an rdkit molecule
:param tuple tuple_order_list: a tuple with 3 subtuples for
mol_1,mol_2,common_core. The numbers within the sublist is the atom IDx
for a given atom in a ligand. The sublist index for each atom in for
tuple_order_list corresponds to the Idx of that atoms match in the
commmon_core. ie tuple_order_list[1][3] = 10; thus the IDx atom in mol_2
which corresponds to the 3rd atom in the common_core is 10.
:returns:
:returns: rdkit.Chem.rdchem.Mol mol: the same rdkit molecule but with the
atom idx's renumbered to be consistent with the common core, as provided
by the tuple_order_list
"""
# make a tuple the same length as the mol the beggining needs to be the
# same as the aligment numbering to the MCS and there can be no redundancy
# in the tuple.
full_tuple_order = [x for x in tuple_order_list]
num = 0
while num < len(mol.GetAtoms()):
if num in full_tuple_order:
num = num + 1
else:
full_tuple_order.append(num)
num = num + 1
# reorder the Idx's of the mol to the full_tuple_order
mol = Chem.RenumberAtoms(mol, full_tuple_order)
return mol
| 21,264 | 37.593466 | 86 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/crossover/smiles_merge/merge_functions/merge_w_core.py | """
This script is handles the merging of two molecules and cleans up
the molecule.
"""
import __future__
import copy
import rdkit
from rdkit import Chem
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
import autogrow.operators.convert_files.gypsum_dl.gypsum_dl.MolObjectHandling as MOH
# HANDLE THE MERGING OF THE R-groups and the MCS And cleanup
# Final steps
def merge_smiles_with_core(rs_chosen_smiles, mcs_mol):
"""
This function runs most of the ligand merger portion of the script. It
merges the chosen R-groups (rs_chosen_smiles) with the common core
(mcs_mol) by bonding anchors in the core to the atoms bound to the
respective anchor in the R-group fragment.
Variables used in this function
anchor_to_connection_dict: dict of anchor to connected atoms and bond
types.
example anchor_to_connection_dict = {10008: [1016, rdkit.Chem.rdchem.BondType.AROMATIC],
10007: [1013, rdkit.Chem.rdchem.BondType.AROMATIC]})
mol_frag, keys are isotope label, value is Idx.
example mol_frag_iso_to_idx_dict = {10007: 0, 10008: 8, 1013: 1, 1014: 3, 1015: 5, 1016: 7,
1017: 9, 1018: 6, 1019: 4, 1020: 2})
Inputs:
:param list rs_chosen_smiles: A list containing the SMILES strings for
the chosen R-groups to add
:param rdkit.Chem.rdchem.Mol mcs_mol: an rdkit molecule representing the
Most common substructure (MCS) which will be expanded by adding R-groups
to make the child molecule
Returns:
:returns: rdkit.Chem.rdchem.Mol rw_core_merg: The child molecule with the
added R-groups built onto the mcs_mol. returns None if the process fails
or if a None-type makes it through.
"""
# convert to RWMOL class of molecule which are able to add and remove
# bonds. RWMOL class is the Read and Write-Mol Class in rdkit.
rw_core_merg = Chem.RWMol(mcs_mol)
# sanitize the mol_frag
rw_core_merg = MOH.check_sanitization(rw_core_merg)
if rw_core_merg is None:
# ("rw_core_merg failed to be sanitizable (merge_smiles_with_core)")
return None
for r_groups in rs_chosen_smiles:
for frag in r_groups:
# empty dicts which will only correspond to individual fragments
# for each R-group. Dicts will be passed into functions within the
# for loop which should handle the merging of each R-group to the
# MCS. Note that making mols with the sanitize=True will kill most
# of these fragmented mols So always Chem.MolFromSmiles(frag,
# sanitize = False)
# make a rdkit mol out of the smiles string of the R-group frag
mol_frag = Chem.MolFromSmiles(frag, sanitize=False)
# Try to sanitize the mol_frag
# It often fails but lets try on some
mol_frag_copy = copy.deepcopy(mol_frag)
mol_frag = MOH.check_sanitization(mol_frag)
if mol_frag is None:
# ("mol_frag failed to be sanitizable
# (merge_smiles_with_core)"). It often fails so if it didn't
# work lets just go back to it. Most often it fails when there
# is a broken ring group...
mol_frag = mol_frag_copy
# make dict of anchor to connected atoms and bond types.
# example anchor_to_connection_dict = {
# 10008: [1016, rdkit.Chem.rdchem.BondType.AROMATIC],
# 10007: [1013, rdkit.Chem.rdchem.BondType.AROMATIC]})
anchor_to_connection_dict = make_anchor_to_bonds_and_type_for_frag(mol_frag)
# Make Dict of all atoms in mol_frag, keys are isotope label,
# value is Idx.
# example mol_frag_iso_to_idx_dict = {10007: 0, 10008: 8, 1013: 1,
# 1014: 3, 1015: 5, 1016: 7, 1017: 9, 1018: 6, 1019: 4, 1020: 2})
mol_frag_iso_to_idx_dict = make_dict_all_atoms_iso_to_idx_dict(mol_frag)
# Make list of atoms idx to remove the anchor atoms from the frag.
# this is necessary to prevent the redundancy of multiple of the
# same anchors once the mol_frag and the core are merged.
anchors_idxs_to_remove = []
for anchors in list(anchor_to_connection_dict.keys()):
# anchors are iso numbers of 10,000 or higher
idx_val = mol_frag_iso_to_idx_dict[anchors]
anchors_idxs_to_remove.append(idx_val)
# remove the anchor atoms from mol_frag
mol_frag = MOH.remove_atoms(mol_frag, anchors_idxs_to_remove)
# Merge the frag with the core. ie) mol1="CCC" and mol2="CCCCCC"
# mol3 = Chem.CombineMols(mol1,mol2); mol3 == "CCC.CCCCCC"
rw_core_merg = Chem.CombineMols(rw_core_merg, mol_frag)
# convert to RWMOL class of molecule which are able to add and
# remove bonds
rw_core_merg = Chem.RWMol(rw_core_merg)
# make a dictionary of every atom in rw_core_merg with Iso as the
# key and the Idx as its value
core_merg_iso_to_idx_dict = make_dict_all_atoms_iso_to_idx_dict(rw_core_merg)
for anchor_atom_iso in list(anchor_to_connection_dict.keys()):
# Idx of the anchor in merged core
idx_for_anchor = core_merg_iso_to_idx_dict[anchor_atom_iso]
# unpack list of atom to connect idx and bond types
list_of_atom_idx, list_of_bond_types = unpack_lists_of_atoms_and_bond_type(
anchor_to_connection_dict, anchor_atom_iso, core_merg_iso_to_idx_dict
)
# USING THE BOND INFO DRAW BONDS TO MAKE NEW CHILD MOL. THIS
# IS AN ITERATIVE PROCESS FOR EACH R-GROUP.
for ai, bt in zip(list_of_atom_idx, list_of_bond_types):
# ai is the atom Idx
# bt is the bondtype
try:
rw_core_merg.AddBond(idx_for_anchor, ai, bt)
except:
return None
return rw_core_merg
def make_anchor_to_bonds_and_type_for_frag(mol_frag):
"""
Create a dictionary w anchor atoms as keys.
for each key, the items are broken into lists of lists with the 1st number
of each as the isotope of the atom bound and the second value as the bond
type to recreat bonds later to merge..
Inputs:
:param rdkit.Chem.rdchem.Mol mol_frag: an R-group which was converted into
a mol
Returns:
:returns: dict anchor_to_connection_dict: a dictionary of anchor atom
isolabels as keys, item is a list of the isolabel of the atom the key is
bound to and the bond type. ie. anchor_to_connection_dict[10007] =
[1004,Chem.BondType.AROMATIC]
"""
anchor_to_connection_dict = {}
isos_anchors_idxs_to_remove = []
for atom in mol_frag.GetAtoms():
if atom.GetIsotope() > 9999: # if atom is an anchor
iso_anchor = atom.GetIsotope() # isotopes of the anchor atom
anchor_idx = atom.GetIdx() # get that atoms idx
isos_anchors_idxs_to_remove.append(
anchor_idx
) # append to list to remove later
# empty lists for subloop
connection_iso_idx_list = (
[]
) # list of isotope number for atoms connected to an anchor
bond_type_list = (
[]
) # list of bond types in the same order as connection_iso_idx_list
neighbor = (
atom.GetNeighbors()
) # all neighbor atoms to the Atom from above loop
for x in neighbor: # Atoms which are neighbors of anchor
iso_neighbor_atom = x.GetIsotope()
neighbor_bond_idx = x.GetIdx()
connection_iso_idx_list.append(iso_neighbor_atom)
# get bond type between anchor and connected atoms
bond_object = mol_frag.GetBondBetweenAtoms(
anchor_idx, neighbor_bond_idx
)
bond_type = bond_object.GetBondType()
bond_type_list.append(bond_type)
for i, j in zip(connection_iso_idx_list, bond_type_list):
list_of_atom_and_bond = [i, j]
if iso_anchor in list(anchor_to_connection_dict.keys()):
tmp = anchor_to_connection_dict[iso_anchor]
tmp.append(list_of_atom_and_bond)
anchor_to_connection_dict[iso_anchor] = tmp
else:
anchor_to_connection_dict[iso_anchor] = [list_of_atom_and_bond]
return anchor_to_connection_dict
def make_dict_all_atoms_iso_to_idx_dict(mol):
"""
Make a dictionary of every atom in a molecule with Iso as the key and the
Idx as its value.
Inputs:
:param rdkit.Chem.rdchem.Mol mol: an rdkit molecule
Return
:returns: dict mol_iso_to_idx_dict: a dictionary of the iso-label of every
atom in the mol as the keys and the idx of that atom in the mol object.
ie) {1008: 7, 1009: 8, 1003: 4, 1004: 3, 1010: 9, 1006: 5, 1007: 6, 10000:
0, 10001: 1, 10002: 2, 1005: 10}
"""
mol_iso_to_idx_dict = {}
for atom in mol.GetAtoms():
iso = atom.GetIsotope()
idx = atom.GetIdx()
mol_iso_to_idx_dict[iso] = idx
return mol_iso_to_idx_dict
def unpack_lists_of_atoms_and_bond_type(anchor_to_connection_dict, anchor_atom_iso,
core_merg_iso_to_idx_dict):
"""
Iterate through all atoms which will be bound to the anchor and unpackage
all the bond types in a list.
Inputs:
:param dict anchor_to_connection_dict: a dictionary of anchor isotope
labels as keys and a lists as the items. these lists have 2 variables, the
1st is the atom iso-label of the atom connected to an anchor, and the
second variable is the rdkit bond type. ie) {10004: [1007,
rdkit.Chem.rdchem.BondType.SINGLE]}
:param int anchor_atom_iso: the interger of an anchor atom's isotope
label. ie) 10004
:param dict core_merg_iso_to_idx_dict: a dictionary of atom's isotope
labels as keys and their corresponding Idx as the items. ie) {1008: 14,
1014: 11, 1009: 15, 1010: 7, 1007: 13, 10000: 0, 10001: 1, 10002: 2,
10003: 3, 10004: 4}
Returns:
:returns: list list_of_atom_idx: a list containing the atom idx. ie) [13]
:returns: list list_of_bond_types: a list containing the bond types of
bonds connected to an anchor atom. ie) [rdkit.Chem.rdchem.BondType.SINGLE]
"""
list_of_atom_idx = []
list_of_bond_types = []
# this if statement determines if there are multiple connections to the
# anchor and how to unpack the dictionary. if there are 2 connections it
# will look like the len is 2 (with 2 sub lists) which is the same as if
# there is there is only 1 connection with two parts in the list. so if
# type(anchor_to_connection_dict[atom_iso][0]) = int then theres only 1
# connection. if type(anchor_to_connection_dict[atom_iso][0]) = list then
# there are multiple lists of lists.
connection_list = anchor_to_connection_dict[anchor_atom_iso]
if type(connection_list[0]) == int:
# get the atom iso label
atom_iso = anchor_to_connection_dict[anchor_atom_iso][0]
# get the atoms idx in the merged core
atom_idx = core_merg_iso_to_idx_dict[atom_iso]
# get bond type
bond_type = anchor_to_connection_dict[anchor_atom_iso][1]
# append to the lists
list_of_atom_idx.append(atom_idx)
list_of_bond_types.append(bond_type)
elif type(connection_list[0]) == list:
if type(connection_list[0][0]) == int:
for i in range(0, len(connection_list)):
# get the atom iso label
atom_iso = anchor_to_connection_dict[anchor_atom_iso][i][0]
# get the atoms idx in the merged core
atom_idx = core_merg_iso_to_idx_dict[atom_iso]
# get bond type
bond_type = anchor_to_connection_dict[anchor_atom_iso][i][1]
# append to the lists
list_of_atom_idx.append(atom_idx)
list_of_bond_types.append(bond_type)
return list_of_atom_idx, list_of_bond_types
def remove_all_isolabels(rw_core_merg):
"""
Remove all the isotope labels from a molecule. One of the finalizing steps
used when LigSmiles is nearly complete.
Inputs:
:param rdkit.Chem.rdchem.RWMol rw_core_merg: a read-write rdkit molecule
of the child molecule (after R-groups have been added) to have the
isotopes to be removed
Returns:
:returns: rdkit.Chem.rdchem.RWMol rw_core_merg: the read-write rdkit
molecule of the child molecule with all the isotope labels removed
"""
# None's often end up in a pipeline use of RDKit so we handle this data
# type as return None instead of raise TypeError
if rw_core_merg is None:
return None
# If mol is wrong data type (excluding None) raise TypeError
if (
type(rw_core_merg) != rdkit.Chem.rdchem.Mol
and type(rw_core_merg) != rdkit.Chem.rdchem.RWMol
):
printout = "rw_core_merg is the wrong data type. \n"
printout = (
printout
+ "Input should be a rdkit.Chem.rdchem.Mol or rdkit.Chem.rdchem.RWMol\n"
)
printout = printout + "Input mol was {} type.".format(type(rw_core_merg))
raise TypeError(printout)
for atom in rw_core_merg.GetAtoms():
atom.SetIsotope(0)
return rw_core_merg
| 13,928 | 40.088496 | 99 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/crossover/smiles_merge/merge_functions/mapping_class.py | """
This script holds the Mapping class.
This is used when mapping most common substructure (MCS)
to combine two molecules.
"""
import __future__
import random
import copy
class Mapping(object):
"""
# Notes on terminology:
-most common substructure (MCS): The substructure shared between
the two parent ligands
-node/anchor/I: an atom in the MCS which has 1 or more atom(s)
connected to it which are not part of the MCS
-the anchors are labeled by their Isotope numbers as
those do not get modified, where as atom Idx are modified
by many Rdkit functions. Anchors have Isotope labels of
10,000 or higher and that label is applied to the MCS and
ligands 1 and 2 so everything is trackable.
-R-group: a chain of one or more atoms connected to a single node
-if an anchor has dimethyl's which are not part of MCS then
each methyl is considered its own R-group
-B-group: 1 or more R-groups which branch off a single node.
-if an anchor has dimethyl's which are not part of MCS then
the combination of both methyls is considered a single
B-group.
B-group Naming scheme: '{first_number}B{second_number}'
-first_number: the number before the B corresponds to
the parent ligand from which the B-group is
derived.
-second_number: the number which follows the B is the
order for which that B-group was determined when
condensing the R-groups into B-groups. Numbering
is indexed to 1. So the 1st three B groups for
parent ligand 1 are: 1B1,1B2,1B3
ie) 1B1 is the 1st B-group from parent ligand 1
1B2 is the second B-group from parent ligand 1
2B1 is the 1st B-group from parent ligand 2
2B2 is the second B-group from parent ligand 2
This class handles mapping for Bs and Is to chose B-groups which will
later be used to make a child molecule. All the choices for B-groups are
handled here.
This is important because if a B-group connects to more than one anchor
atom, the selection of that B-group determines the selection of both
anchor atoms.
ie) if 1B1 connects to anchor atom 10003 and 10004; and 2B1 connects to
10003 and 10005 then the decision for which B-group is chosen for
anchor 10003 determines the options which will be viable for anchor
atoms 10003,10004, and 10005.
These type of decisions are handled by this class.
"""
def __init__(self, b_to_is, i_to_bs):
"""
When a Mapping object is initialized, it imports 2 input dictionaries,
which can be referenced throughout the class.
Inputs:
:param dict b_to_is: Dictionary converting B-groups to anchor/node/I
atoms. This contains groups from both parent molecules. This is the
inverse of i_to_bs. keys are B-groups; items are the anchor atoms
isotope label. ie) {'1B1': [10003], '2B3': [10005], '2B2': [10003],
'2B1': [10002]}
:param dict i_to_bs: Dictionary converting Anchor/node/I atoms to
corresponding B-groups. This contains groups from both parent
molecules. This is the inverse of b_to_is. keys are the anchor
atoms
isotope labels; items are B-groups ie) {10002: ['2B1'], 10003: ['1B1',
'2B2'], 10005: ['2B3']}
"""
self.b_to_is = copy.deepcopy(
b_to_is
) # B-I mapping dictionary from outside class
self.i_to_bs = copy.deepcopy(
i_to_bs
) # I-B mapping dictionary from outside class
def locate_b(self, i):
"""
Given a specified anchor/I return a list of all the B-groups from both
parent ligands, bound to that anchor
Inputs:
:param int i: the isolabel of an anchor atom which will be used to
search for B-groups within self.i_to_bs.
Returns
:returns: list self.i_to_bs[i]: A list of all the B-groups, from both
parent ligands which are bound to that anchor. ie) ['1B1','2B1']
"""
return self.i_to_bs[i]
#
def locate_i(self, b):
"""
Given a specified B-group return the anchor/I/node it connects to.
Inputs:
:param str b: the name of a B-groups within self.b_to_is.
Returns
:returns: list self.b_to_is[b]: A list of the anchor the given
B-groups is bound. ie) [10001]
"""
return self.b_to_is[b]
#
def delete_b(self, b):
"""
Removes the b from b_to_is and all references to b in i_to_bs.
b is a Key in b_to_is. B is one or more items in i_to_bs.
Inputs:
:param str b: A B-group to be removed from the b_to_is and B in
i_to_bs dicts.
"""
i_list_to_modify = self.locate_i(b)
for i in i_list_to_modify:
blank = self.i_to_bs[i].remove(b)
del self.b_to_is[b]
#
def delete_i(self, i):
"""
Removes the i from i_to_bs and all references to i in b_to_is. i is
a Key in i_to_bs. i is one or more items in b_to_is.
Inputs:
:param int i: An interger representing the isolabel for an
anchor/node/i atom to be removed from the b_to_is and b in i_to_bs
dicts.
"""
bs_to_modify = self.locate_b(i)
for b in bs_to_modify:
self.b_to_is[b].remove(i)
del self.i_to_bs[i]
#
def chose_b_from_i(self, i):
"""
Chose your B from a given i. This makes the decision which B-group
will be chosen for a specific i.
Current implementation is that there are no null choice options. ie.
if an anchor has only 1 option to pick from then it must pick that
B-group. It can not chose nothing or to leave it blank, even if
choosing that B-group forces the future decision because of it's
connections to anchors which have yet to have B-group decisions.
this has bearings on B-groups which connect to multiple anchors as
well as on anchors which have B-groups from only one parent ligand,
but the other parent has nothing connected to that anchor.
in the case of one parent having a B-group attached to an anchor but
nothing attached to the anchor for the other parent, this
implementation will always chose to keep the B-group and never can
leave it blank.
ie (if 1B1 is connected to multiple anchors)
Lack of an B-groups bound to an anchor is not considered a B-group
Inputs:
:param int i: An interger representing the isolabel for an
anchor/node/i atom. This function choses which B-group will be bound
to this anchor in the child molecule.
Returns:
:returns: str b_x: A string of the name of a chosen B-group; None if
not in the dictionary or if there is no available choices
"""
# Developers Notes
# Current implementation has no Null/None as choices this means that
# if one parent has a B-group bound to anchor and the other has
# nothing bound to that anchor, then the program will always chose to
# add the B-group, resulting in a larger child molecule.
# This biases the output. It also results in B-groups with multiple
# connections are weighted against because 1 decision on 1 node will
# determine if they can't be chosen...
# Two alternative implementations which could be added are listed
# below, both with advantages and disadvantages:
# 1) Dominant Nulls: (Null is an option which cannot be override)
# When we reach an anchor which only has only one B-group choice then
# we add a Null B-group. This Null group means nothing can be added
# to that anchor.
# This could be implemented in this step in mapping_class.py or this
# could be implemented at the R-groups to B-group consolidation step
# -implemented it at the R-group to B-group consolidation may be a
# better option because it will simplify issues of when is it
# appropriate to add Null.
# ie) if parent lig_1 has 1B1 attached to anchors 10000,10001,10002
# 1B2 attached to anchors 10003
# if parent lig_2 has 2B1 at 10000 and 10003
# 2B at 10002
# if 1B1 is chosen 1st using anchor 10000, then anchors 10000,10001,10002
# are determined
# THIS ALSO MEANS THAT 2B1 is impossible eliminating 2B1
# from an option for anchor 10003
# When the program needs to make a decision for anchor
# 10003 what should its option be????:
# - It should have to chose 1B2
# IF WE implement THE NULLS IN THIS PART OF THE CODE WE
# WOULD HAVE TO CODE IN THAT AS A CONSIDERATION IF WE
# implement NULLS AT THE R- TO B-GROUP CONSOLIDATION PHASE
# WE WOULDN'T NEED TO ADD EXTRA CODE HERE TO PREVENT A NULL
# FROM BEING ADDED
# If a dominant null group (which is only added when an anchor has no
# R-groups attached for 1 parent but some on the other) then when the
# decision for B-group is occuring here; if a null is chosen then
# nothing can be added
# Effects:
# 1) easier to implement
# 2) affects the weighting of multi connection point B-groups -A
# single decision more dramatically impacts chains with many
# connections
# 3) a Null is permanent so its easier to code and process
# 2) Recessive Nulls: (A Null can be chosen but it can be overriden)
# (soft Null). If a recessive null is chosen instead of a B-group
# with multiple connections to the MCS then the B-group which
# wasn't chosen does not get removed from the dictionaries.
# -Then the next anchor that the not chosen B-group is connected
# to is assessed, there is still the option to chose that group.
# -If that group is chosen then we write over the Null option.
# -If that group is not chosen then the null remains as the choice
# for the 1st anchor
# Effects:
# -Recessive Nulls favors the selection of B-groups with multiple
# connections, but still allows for a null to be chosen.
# -A more balanced option between No-Nulls (the current
# implementation) and Dominant Nulls. But this does bias the
# statistics of choices
# -this also makes the decision tree more complicated and makes
# coding this more difficult
# There's no right answer to this but there are certain pro's and
# con's to each approach. The current approach is justified as the
# most code and computational efficient method, with no distict
# preference for multi-chain B-groups, but certainly with some biases
# against shrinking the child molecule
# Select an B to keep
if i in list(self.i_to_bs.keys()):
options = self.locate_b(i)
if len(options) > 1:
b_x = random.choice(options)
elif len(options) == 1:
b_x = options[0]
else:
return "None"
list_is = self.locate_i(b_x)
list_bs = []
for x in list_is:
list_bs.append(self.locate_b(x))
flattened = [val for sublist in list_bs for val in sublist]
unique_bs = list(
set(flattened)
) # convert list to set to list to remove redundant B's
# delete the B's and I's
for b in unique_bs:
self.delete_b(b)
for x in list_is:
self.delete_i(x)
return b_x
# the i is not in list(self.i_to_bs.keys())
# return the string "None"
return "None"
#
def testing_function_return_self_dicts(self):
"""
Return the properties: self.b_to_is and self.i_to_bs
Returns:
:returns: dict b_to_is: Dictionary converting B-groups to
anchor/node/I atoms. This contains groups from both parent molecules.
This is the inverse of i_to_bs. keys are B-groups; items are the
anchor atoms isotope label. ie) {'1B1': [10003], '2B3': [10005],
'2B2': [10003], '2B1': [10002]}
:returns: dict i_to_bs: Dictionary converting Anchor/node/I atoms to
corresponding B-groups. This contains groups from both parent
molecules. This is the inverse of b_to_is. keys are the anchor atoms
isotope labels; items are B-groups. ie) {10002: ['2B1'], 10003:
['1B1', '2B2'], 10005: ['2B3']}
"""
return self.b_to_is, self.i_to_bs
# i_dict = {10000: ['1B1', '2B1'], 10004: ['2B2'], 10005: ['2B3'], 10006: \
# ['2B4'], 10007: ['1B3'], 10008: ['1B2']}
# b_dict = {'1B1': [10000], '1B2': [10008], '1B3': [10007], '2B4': [10006], \
# '2B3': [10005], '2B2': [10004], '2B1': [10000]}
def run_mapping(b_dict, i_dict):
"""
This runs the mapping class which can determine which B-groups/R-groups we
will append in SmileMerge.
Inputs:
:param dict b_dict: Dictionary converting B-groups to anchor/node/I
atoms. This contains groups from both parent molecules. This is the
inverse of i_to_bs. keys are B-groups; items are the anchor atoms isotope
label. ie) {'1B1': [10003], '2B3': [10005], '2B2': [10003], '2B1':
[10002]}
:param dict i_dict: Dictionary converting Anchor/node/I atoms to
corresponding B-groups. This contains groups from both parent molecules.
This is the inverse of b_to_is. keys are the anchor atoms isotope labels;
items are B-groups. ie) {10002: ['2B1'], 10003: ['1B1', '2B2'], 10005:
['2B3']}
Returns:
:returns: list bs_chosen: A list of all the chosen B-groups to be used to
generate a child molecule later.
"""
a_mapping_object = Mapping(b_dict, i_dict)
bs_chosen = []
for i in i_dict:
b_choice = a_mapping_object.chose_b_from_i(i)
bs_chosen.append(b_choice)
bs_chosen = list(set(bs_chosen))
for i in bs_chosen:
if i == "None":
bs_chosen.remove(i)
return bs_chosen
#
#
| 15,165 | 41.481793 | 88 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/crossover/smiles_merge/merge_functions/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/mutation/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/mutation/execute_mutations.py | """
This function should contain all the info for executing the mutation functions
"""
import __future__
import random
import copy
import autogrow.operators.mutation.smiles_click_chem.smiles_click_chem as SmileClickClass
#######################################
# Functions for creating molecular models
##########################################
def make_mutants(vars, generation_num,
number_of_processors,
num_mutants_to_make,
ligands_list, ### ************************************* has been random.shuffled.
new_mutation_smiles_list,
rxn_library_variables,
mutate_ligand_select_policy_net,
mutate_reaction_select_policy_net, ):
"""
Make mutant compounds in a list to be returned
This runs SmileClick and returns a list of new molecules (smiles, ligand_id)
Inputs:
:param dict vars: a dictionary of all user variables
:param int generation_num: generation number
:param int number_of_processors: number of processors as specified by the user
:param int num_mutants_to_make: number of mutants to return
***************************************************************************************************************
:param list ligands_list: list of ligand/name pairs which are the order in which to be sampled
***************************************************************************************************************
:param list new_mutation_smiles_list: is the list of mutants made for the current generation being populated but in a previous
iteration of the loop in Operations
:param list rxn_library_variables: a list of user variables which define
the rxn_library, rxn_library_file, and function_group_library. ie.
rxn_library_variables = [vars['rxn_library'], vars['rxn_library_file'],
vars['function_group_library']]
Returns:
:returns: list new_ligands_list: ligand/name pairs OR returns None if
sufficient number was not generated. None: bol if mutations failed
"""
if len(new_mutation_smiles_list) == 0:
new_ligands_list = []
else:
new_ligands_list = new_mutation_smiles_list
loop_counter = 0
number_of_processors = int(vars["parallelizer"].return_node())
# initialize the smileclickclass
a_smiles_click_chem_object = SmileClickClass.SmilesClickChem(rxn_library_variables, new_mutation_smiles_list, vars["filter_object_dict"])
while loop_counter < 2000 and len(new_ligands_list) < num_mutants_to_make:
react_list = copy.deepcopy(ligands_list)
while len(new_ligands_list) < num_mutants_to_make and len(react_list) > 0:
a_smiles_click_chem_object.update_list_of_already_made_smiles(new_ligands_list)
num_to_grab = num_mutants_to_make - len(new_ligands_list)
num_to_make = num_to_grab
# to minimize a big loop of running a single mutation at a time we
# will make 1 new lig/processor. This will help to prevent wasting
# reasources and time.
if num_to_make < number_of_processors:
num_to_make = number_of_processors
smile_pairs = [
react_list.pop() for x in range(num_to_make) if len(react_list) > 0
]
smile_inputs = [x[0] for x in smile_pairs]
smile_names = [x[1] for x in smile_pairs]
job_input = tuple(
[tuple([smile, a_smiles_click_chem_object]) for smile in smile_inputs]
)
#####################################
########### main mutation ###########
#####################################
results = vars["parallelizer"].run(job_input, run_smiles_click_for_multithread)
for index, i in enumerate(results):
if i is not None:
# Get the new molecule's (aka the Child lig) Smile string
child_lig_smile = i[0]
# get the reaction id number
reaction_id_number = i[1]
# get the ID for the parent of a child mol and the
# complementary parent mol. comp mol could be None or a
# zinc database ID
parent_lig_id = smile_names[index]
zinc_id_comp_mol = i[2]
# Make a list of all smiles and smile_id's of all
# previously made smiles in this generation
list_of_already_made_smiles = []
list_of_already_made_id = []
# fill lists of all smiles and smile_id's of all
# previously made smiles in this generation
for x in new_ligands_list:
list_of_already_made_smiles.append(x[0])
list_of_already_made_id.append(x[1])
if child_lig_smile not in list_of_already_made_smiles:
# if the smiles string is unique to the list of
# previous smile strings in this round of reactions
# then we append it to the list of newly created
# ligands we append it with a unique ID, which also
# tracks the progress of the reactant
is_name_unique = False
while is_name_unique is False:
# make unique ID with the 1st number being the
# parent_lig_id for the derived mol, Followed by
# Mutant, folowed by the generationnumber,
# followed by a unique.
# get the unique ID (last few diget ID of the
# parent mol
parent_lig_id = parent_lig_id.split(")")[-1]
random_id_num = random.randint(100, 1000000)
if zinc_id_comp_mol is None:
new_lig_id = "({})Gen_{}_Mutant_{}_{}".format(
parent_lig_id,
generation_num,
reaction_id_number,
random_id_num,
)
else:
new_lig_id = "({}+{})Gen_{}_Mutant_{}_{}".format(
parent_lig_id,
zinc_id_comp_mol,
generation_num,
reaction_id_number,
random_id_num,
)
# check name is unique
if new_lig_id not in list_of_already_made_id:
is_name_unique = True
# make a temporary list containing the smiles string
# of the new product and the unique ID
ligand_info = [child_lig_smile, new_lig_id]
# append the new ligand smile and ID to the list of
# all newly made ligands
new_ligands_list.append(ligand_info)
loop_counter = loop_counter + 1
if len(new_ligands_list) < num_mutants_to_make:
return None
# once the number of mutants we need is generated return the list
return new_ligands_list
def run_smiles_click_for_multithread(smile, a_smiles_click_chem_object):
"""
This function takes a single smilestring and performs SmileClick on it.
This is necessary for Multithreading as it is unable to execute
multithread on a class function, but can thread a class run within a
function.
Inputs:
:param str smile: a SMILES string
Returns:
:returns: str result_of_run: either a smile string of a child mol or None
if the reactions failed
"""
result_of_run = a_smiles_click_chem_object.run_smiles_click(smile)
return result_of_run
| 8,245 | 41.287179 | 141 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/mutation/smiles_click_chem/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/mutation/smiles_click_chem/smiles_click_chem.py | """SMILECLICK Class"""
import __future__
import random
import os
import json
import copy
import rdkit
from rdkit import Chem
from rdkit.Chem import AllChem
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
import autogrow.operators.convert_files.gypsum_dl.gypsum_dl.MolObjectHandling as MOH
import autogrow.operators.filter.execute_filters as Filter
class SmilesClickChem(object):
""" This class will take a molecule and Mutate it by reacting it. """
def __init__(self, rxn_library_variables, list_of_already_made_smiles,
filter_object_dict):
"""
init for SmilesClickChem. This will set up all the reaction and
functional dictionaries required to Mutate a molecular
Inputs:
:param list rxn_library_variables: a list of user variables which
define the rxn_library, rxn_library_file,
complementary_mol_directory, and function_group_library. ie.
rxn_library_variables = [vars['rxn_library'],
vars['rxn_library_file'],
vars['function_group_library'],vars['complementary_mol_directory']]
:param list list_of_already_made_smiles: a list of lists. Each
sublist contains info about a smiles made in this generation via
mutation ie.[['O=C([O-])',
'(Gen_3_Mutant_37_747+ZINC51)Gen_4_Mutant_15_52']]
:param dict filter_object_dict: a dictionary of all filter objects
which are to be applied to the newly created ligands.
"""
# Unpackage the rxn_library_variables
rxn_library = rxn_library_variables[0]
rxn_library_file = rxn_library_variables[1]
function_group_library = rxn_library_variables[2]
complementary_mol_dir = rxn_library_variables[3]
self.reaction_dict = self.retrieve_reaction_dict(
rxn_library, rxn_library_file
)
# Retrieve the dictionary containing
# all the possible ClickChem Reactions
self.list_of_reaction_names = list(self.reaction_dict.keys())
self.functional_group_dict = self.retrieve_functional_group_dict(
rxn_library, function_group_library
)
self.complementary_mol_dict = self.retrieve_complementary_dictionary(
rxn_library, complementary_mol_dir
)
# List of already predicted smiles
self.list_of_already_made_smiles = [x[0] for x in list_of_already_made_smiles]
# Dictionary containing all Filter class
# objects to be impossed on the ligand
self.filter_object_dict = filter_object_dict
def update_list_of_already_made_smiles(self, list_of_already_made_smiles):
"""
This updates the list of Smiles which have been made in this
generation via mutation.
Inputs:
:param list list_of_already_made_smiles: a list of lists. Each sublist
contains info about a smiles made in this generation via mutation.
ie. [['O=C([O-])',
'(Gen_3_Mutant_37_747+ZINC51)Gen_4_Mutant_15_52']]
"""
list_of_already_made_smiles = [x[0] for x in list_of_already_made_smiles]
self.list_of_already_made_smiles.extend(list_of_already_made_smiles)
def rxn_lib_format_json_dict_of_dict(self, old_dict):
"""
json dictionaries import as type unicode. This script converts all
the keys and items to strings, with a few specific exceptions. It
takes both the functional group dictionary and the reaction library.
The reaction library is a dictionary of dictionary and has a few
exceptions which are not intended to be strings. ie. the num_reactants
which converts to interger and functional_groups which convert to a
list of strings.
The functional_group_dictionary is simply a dictionary with all items
and keys needing to be strings.
Inputs:
:param dic old_dict: a dictionary of the the reaction library or
functional groups. This is what is importanted from the .json file.
Returns:
:returns: dic new_dict: a dictionary of the the reaction library or
functional groups where the unicode type items have been replaced with
the proper python data types.
"""
new_dict = {}
for rxn_key in old_dict.keys():
rxn_dic_old = old_dict[rxn_key]
key_str = str(rxn_key)
# For reaction libraries
if type(rxn_dic_old) == dict:
new_sub_dict = {}
for key in rxn_dic_old.keys():
sub_key_str = str(key)
item = rxn_dic_old[key]
if sub_key_str == "num_reactants":
item = int(item)
elif sub_key_str == "functional_groups":
new_list = []
for i in item:
i_str = str(i)
new_list.append(i_str)
item = new_list
else:
item = str(item)
new_sub_dict[sub_key_str] = item
new_dict[key_str] = new_sub_dict
# For functional groups
else:
item = old_dict[rxn_key]
new_dict[key_str] = str(item)
return new_dict
def retrieve_reaction_dict(self, rxn_library, rxn_library_file):
"""
This is where all the chemical reactions for SmartClickChem are
retrieved. If you want to add more just add a Custom set of reactions
please add a folder to
PATH/autogrow/operators/mutation/smiles_click_chem/Reaction_libraries/.
They should be formatted as a dictionary of dictionary using the same
format as :
os.path.join(pwd,"reaction_libraries",
"click_chem_rxns","ClickChem_rxn_library.json")
The reactions are written as SMARTS-reaction strings.
This dictionary uses the reaction name as the key and the Reaction
Smarts as the value.
Inputs:
:param str rxn_library: A string defining the choice of the reaction
library. ClickChem uses the set of reactions from Autogrow 3.1.2.
Custom means you've defined a path to a Custom library in
vars['rxn_library_file']
:param str rxn_library_file: a PATH to a Custom reaction library file
formatted in a dictionary of dictionaries. in a .json file. This will
be a blank string if one choses a predefined rxn_library option.
Returns:
:returns: dict reaction_dict: A dictionary containing all the
reactions for ClickChemistry and all the information required to run
the reaction
"""
# Get the JSON file to import the proper reaction library
pwd = os.path.dirname(__file__)
if rxn_library_file == "":
if rxn_library == "click_chem_rxns":
rxn_library_file = os.path.join(
pwd,
"reaction_libraries",
"click_chem_rxns",
"ClickChem_rxn_library.json"
)
elif rxn_library == "robust_rxns":
rxn_library_file = os.path.join(
pwd,
"reaction_libraries",
"robust_rxns",
"Robust_Rxns_rxn_library.json"
)
elif rxn_library == "all_rxns":
rxn_library_file = os.path.join(
pwd,
"reaction_libraries",
"all_rxns",
"All_Rxns_rxn_library.json"
)
elif rxn_library == "Custom":
if os.path.exists(rxn_library_file) is False:
raise Exception(
"Custom rxn_library_file cannot be found. "
+ "Please check the path: ",
rxn_library_file,
)
else:
raise Exception(
"rxn_library is not incorporated into smiles_click_chem.py"
)
# Import the proper reaction library JSON file
try:
with open(rxn_library_file, "r") as rxn_file:
reaction_dict_raw = json.load(rxn_file)
except:
raise Exception(
"rxn_library_file json file not able to be imported."
+ " Check that the rxn_library is formatted correctly"
)
elif type(rxn_library_file) == str:
if os.path.exists(rxn_library_file) is False:
raise Exception(
"Custom specified rxn_library_file directory can not be found"
)
if os.path.isfile(rxn_library_file) is False:
raise Exception(
"Custom specified rxn_library_file is not a file"
)
try:
extension = os.path.splitext(rxn_library_file)[1]
except:
raise Exception(
"Custom specified rxn_library_file is not .json file."
+ " It must be a .json dictionary"
)
if extension != ".json":
raise Exception(
"Custom specified rxn_library_file is not .json file."
+ " It must be a .json dictionary"
)
# Import the proper reaction library JSON file
try:
with open(rxn_library_file, "r") as rxn_file:
reaction_dict_raw = json.load(rxn_file)
except:
raise Exception(
"Custom specified rxn_library_file json file not able to "
+ "be imported. Check that the rxn_library is "
+ "formatted correctly"
)
else:
raise Exception(
"Custom specified rxn_library_file directory can not be found"
)
# Convert the reaction_dict_raw from unicode to the proper
reaction_dict = self.rxn_lib_format_json_dict_of_dict(reaction_dict_raw)
return reaction_dict
def retrieve_functional_group_dict(self, rxn_library, function_group_library):
"""
This retrieves a dictionary of all functional groups required for the
respective reactions. This dictionary will be used to identify
possible reactions.
This is where all the functional groups which will be used in the
SmartClickChem reactions are retrieved. If you want to add more just
add a Custom set of reactions please add a folder to
PATH/autogrow/operators/mutation/smiles_click_chem/Reaction_libraries/.
They should be formatted as a dictionary of dictionary using the same
format as :
os.path.join(pwd,"reaction_libraries","click_chem_rxns",
"ClickChem_functional_groups.json")
IF YOU CHOSE TO DO A Custom REACTION SET YOU MUST PROVIDE A DICTIONARY
OF ALL FUNCTIONAL GROUPS IT WILL REACT. IF YOU FORGET TO ADD A
FUNCTIONAL GROUP TO YOUR Custom DICTIONARY, THE REACTION MAY NEVER BE
UTILIZED.
Please note if your functional groups involve stereochemistry
notations such as '\' please replace with '\\' (all functional
groups should be formatted as SMARTS)
Inputs:
:param str rxn_library: A string defining the choice of the reaction
library. ClickChem uses the set of reactions from Autogrow 3.1.2.
Custom means you've defined a path to a Custom library in
vars['function_group_library']
:param str function_group_library: a PATH to a Custom functional group
dictionary in a .json file. This will be a blank string if one choses
a predefined functional groups option.
Returns:
:returns: dict functional_group_dict: A dictionary containing all
SMARTS for identifying the functional groups for ClickChemistry
"""
# Get the JSON file to import the proper reaction library
pwd = os.path.dirname(__file__)
if function_group_library == "":
if rxn_library == "click_chem_rxns":
function_group_library = os.path.join(
pwd, "reaction_libraries",
"click_chem_rxns",
"ClickChem_functional_groups.json",
)
elif rxn_library == "robust_rxns":
function_group_library = os.path.join(
pwd, "reaction_libraries",
"robust_rxns",
"Robust_Rxns_functional_groups.json",
)
elif rxn_library == "all_rxns":
function_group_library = os.path.join(
pwd, "reaction_libraries",
"all_rxns", "All_Rxns_functional_groups.json",
)
elif rxn_library == "Custom":
if os.path.exists(function_group_library) is False:
raise Exception(
"Custom function_group_library cannot be found. "
+ "Please check the path: ",
function_group_library,
)
else:
raise Exception(
"rxn_library is not incorporated into smiles_click_chem.py"
)
# Import the proper function_group_library JSON file
try:
with open(function_group_library, "r") as func_dict_file:
functional_group_dict_raw = json.load(func_dict_file)
except:
raise Exception(
"function_group_library json file not able to be imported. "
+ "Check that the rxn_library is formatted correctly"
)
elif type(function_group_library) == str:
if os.path.exists(function_group_library) is False:
raise Exception(
"Custom specified function_group_library directory can not be found"
)
if os.path.isfile(function_group_library) is False:
raise Exception("Custom specified function_group_library is not a file")
try:
extension = os.path.splitext(function_group_library)[1]
except:
raise Exception(
"Custom specified function_group_library is not .json "
+ "file. It must be a .json dictionary"
)
if extension != ".json":
raise Exception(
"Custom specified function_group_library is not .json "
+ "file. It must be a .json dictionary"
)
# Import the proper function_group_library JSON file
try:
with open(function_group_library, "r") as func_dict_file:
functional_group_dict_raw = json.load(func_dict_file)
except:
raise Exception(
"function_group_library json file not able to be imported."
+ " Check that the rxn_library is formatted correctly"
)
else:
raise Exception(
"Custom specified function_group_library directory can not be found"
)
# Convert the reaction_dict_raw from unicode to the proper
functional_group_dict = self.rxn_lib_format_json_dict_of_dict(
functional_group_dict_raw
)
return functional_group_dict
def rand_key_list(self, dictionary):
"""
Get a random ordered list of all the keys from a dictionary.
Inputs:
:param dict dictionary: any dictionary
Returns:
:returns: list keys: a randomly ordered list containing all the keys
from the dictionary
"""
keys = list(dictionary.keys()) # List of keys
random.shuffle(keys)
return keys
def retrieve_complementary_dictionary(self, rxn_library, complementary_mol_dir):
"""
Based on user controlled variables, this definition will retrieve a
dictionary of molecules separated into classes by their functional
groups. The sorting of a .smi file into this should be handled in the
user parameter testing when autogrow is initially started.
Inputs:
:param str rxn_library: A string defining the choice of the reaction
library. ClickChem uses the set of reactions from Autogrow 3.1.2.
Custom means you've defined a path to a Custom library in
vars['complementary_mol_dir']
:param dict complementary_mol_dir: the path to the
complementary_mol_dir directory. It may be an empty string in which
case the complementary_mol_dir directory will default to those of the
rxn_library
Returns:
:returns: dict complementary_mols_dict: a dictionary of complementary molecules
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
if complementary_mol_dir == "":
if rxn_library == "click_chem_rxns":
complementary_mol_dir = os.path.join(
script_dir,
"reaction_libraries",
"click_chem_rxns",
"complementary_mol_dir",
)
elif rxn_library == "robust_rxns":
complementary_mol_dir = os.path.join(
script_dir,
"reaction_libraries",
"robust_rxns",
"complementary_mol_dir",
)
elif rxn_library == "all_rxns":
complementary_mol_dir = os.path.join(
script_dir,
"reaction_libraries",
"all_rxns",
"complementary_mol_dir",
)
elif rxn_library == "Custom":
if os.path.isdir(complementary_mol_dir) is False:
raise Exception(
"Custom complementary_mol_dir cannot be found. "
+ "Please check the path: ",
complementary_mol_dir,
)
else:
raise Exception(
"rxn_library is not incorporated into smiles_click_chem.py"
)
else:
if os.path.isdir(complementary_mol_dir) is False:
raise Exception(
"complementary_mol_dir is not a directory. It must be a \
directory with .smi files containing SMILES specified by \
functional groups.These .smi files must be named the same \
as the files in the complementary_mol_dir."
)
# Make a list of all the functional groups. These will be the name of
# the .smi folders already separated by group.
functional_groups = self.functional_group_dict.keys()
missing_smi_files = []
complementary_mols_dict = {}
for group in functional_groups:
filepath = "{}{}{}.smi".format(complementary_mol_dir, os.sep, group)
if os.path.isfile(filepath) is True:
complementary_mols_dict[group] = filepath
else:
missing_smi_files.append(filepath)
print(
"Could not find the following .smi file for complementary "
+ " molecules for Mutation: {}".format(filepath)
)
if len(missing_smi_files) != 0:
raise Exception(
"The following .smi file for complementary molecules "
+ "for Mutation is missing: ",
missing_smi_files,
)
return complementary_mols_dict
def make_reactant_order_list(self, substructure_search_result,
has_substructure_matches_count):
"""
make an ordered list of reactants which composed of 0 and 1. This list
will be used (in later steps) to determine which reactant is the
ligand and which requires a complementary molecule.
Inputs:
:param list substructure_search_result: list composed of 0 and 1. 1
for if it has the substructure 0 for not
:param int has_substructure_matches_count: how many substructure
matches there are
Returns:
:returns: list reactant_order_list: an ordered list of reactants which
composed of 0 and 1.
"""
# for mols w at least 1 substructure
if has_substructure_matches_count == 1:
reactant_order_list = substructure_search_result
elif has_substructure_matches_count > 1:
# if more than 1 reactant is found in the ligand than we need to
# randomly pick 1 to be the molecule in the reaction and the
# other(s) to be mols chosen from the complementary molecule
# dictionary
# create a list to be used to determine which reactants need
# complementary mol and which will use the Ligand
reactant_order_list = []
chosen_as_mol_num = random.randint(0, has_substructure_matches_count - 1)
counter_of_matches = 0
for i in range(0, len(substructure_search_result)):
if (
substructure_search_result[i] == 1
and counter_of_matches == chosen_as_mol_num
):
reactant_order_list.append(1)
counter_of_matches = counter_of_matches + 1
elif (
substructure_search_result[i] == 1
and counter_of_matches != chosen_as_mol_num
):
reactant_order_list.append(0)
counter_of_matches = counter_of_matches + 1
else:
reactant_order_list.append(0)
return reactant_order_list
def get_random_complementary_mol(self, functional_group):
"""
This function will get a dictionary of complementary mols
Inputs:
:param str functional_group: the functional group of the needed
complementary molecule for the reaction
Returns:
:returns: list random_comp_mol: list with the SMILES string and name
of molecule for the randomly chosen comp mol
"""
infile = self.complementary_mol_dict[functional_group]
with open(infile, "r") as f:
random_comp_mol_line = random.choice(f.readlines())
random_comp_mol_line = (
random_comp_mol_line.replace("\n", "")
.replace("\t", " ")
.replace(" ", " ")
)
for i in range(10):
random_comp_mol_line.replace(" ", " ")
parts = random_comp_mol_line.split(
" "
) # split line into parts separated by 4-spaces
# parts = [x for x in random_comp_mol_line.split(" ") if x!= ""]
# # split line into parts separated by 4-spaces
smile_list = parts[0]
zinc_name_list = parts[1]
random_comp_mol = [smile_list, zinc_name_list]
return random_comp_mol
def determine_functional_groups_in_mol(self, mol_deprotanated, mol_reprotanated):
"""
This function will take a molecule and find which functional groups it
has. This will save time for picking reactions, particularly as
reaction lists become larger.
Inputs:
:param rdkit.Chem.rdchem.Mol mol_deprotanated: an rdkit molecule which
has been sanitized and deprotanated
:param rdkit.Chem.rdchem.Mol mol_reprotanated: an rdkit molecule which
has been sanitized and fully protanated
Returns:
:returns: list list_subs_within_mol: a list of the name of every
functional group found within the molecule. these will be used later
to filter for reactions.
"""
list_subs_within_mol = []
functional_group_dict = self.functional_group_dict
for key in list(functional_group_dict.keys()):
substructure = Chem.MolFromSmarts(functional_group_dict[key])
if mol_reprotanated.HasSubstructMatch(substructure):
list_subs_within_mol.append(key)
else:
if mol_deprotanated.HasSubstructMatch(substructure):
list_subs_within_mol.append(key)
else:
continue
return list_subs_within_mol
def run_smiles_click(self, ligand_smiles_string):
"""
###################################
############### main ##############
###################################
This will take the shuffled list of reaction names
(self.shuffled_reaction_list) and test the Ligand to see if it is
capable of being used in the reaction. If the ligand is unable to be
used in the reaction, then we move on to the next reaction in the
list. If none work, we return a None.
Inputs:
:param str ligand_smiles_string: SMILES string of a molecule to be
reacted
Returns:
:returns: list product_info: list containing the reaction product, the
id_number of the reaction as found in the reaction_dict and the id for
the complementary mol (None if it was a single reactant reaction)
[reaction_product_smilestring, reaction_id_number,
zinc_database_comp_mol_name]. returns None if all reactions failed or
input failed to convert to a sanitizable rdkit mol.
"""
try:
mol = Chem.MolFromSmiles(
ligand_smiles_string, sanitize=False
) # This is the input molecule which serves as the parent molecule
except:
# mol object failed to initialize
return None
# try sanitizing, which is necessary later
mol = MOH.check_sanitization(mol)
if mol is None:
return None
# Is important for some functional groups while being deprotanated are
# useful for other reaction
mol_reprotanated = copy.deepcopy(mol)
mol_reprotanated = MOH.try_reprotanation(mol_reprotanated)
if mol_reprotanated is None:
return None
mol_deprotanated = copy.deepcopy(mol)
mol_deprotanated = MOH.try_deprotanation(mol_deprotanated)
if mol_deprotanated is None:
return None
# Determine which functional groups are within a ligand
list_subs_within_mol = self.determine_functional_groups_in_mol(mol_deprotanated, mol_reprotanated)
if len(list_subs_within_mol) == 0:
print("{} had no functional groups to react with.".format(ligand_smiles_string))
return None
shuffled_reaction_list = self.rand_key_list(
self.reaction_dict
) # Randomize the order of the list of reactions (shuffle)
tries = 0
is_rxn_complete = False
# go through all possible rxns in dictionary of rxns using the random
# order of rxns loop ends when a rxn is successful or when it runs out
# of reactions
while tries < len(shuffled_reaction_list) and is_rxn_complete is False:
reaction_name = shuffled_reaction_list[tries]
a_reaction_dict = self.reaction_dict[reaction_name]
fun_groups_in_rxn = a_reaction_dict["functional_groups"]
contains_group = None
for i in range(0, len(fun_groups_in_rxn)):
if fun_groups_in_rxn[i] in list_subs_within_mol:
contains_group = i
# The number i which contains_group is now equal to will
# be used to remember the placement of the molecule later
# in the reaction.
break
continue
if contains_group is None:
# Reaction doesn't contain a functional group found in the
# reactant molecule. So lets move on to the next molecule
tries = tries + 1
continue
# Determine whether to react using the protanated or
# deprotanated form of the ligand
substructure = Chem.MolFromSmarts(
self.functional_group_dict[fun_groups_in_rxn[i]]
)
if mol_deprotanated.HasSubstructMatch(substructure) is True:
mol_to_use = copy.deepcopy(mol_deprotanated)
else:
mol_to_use = copy.deepcopy(mol_reprotanated)
substructure = None
rxn = AllChem.ReactionFromSmarts(str(a_reaction_dict["reaction_string"]))
rxn.Initialize()
# if the reaction requires only a single reactant we will attempt
# to run the reaction
if a_reaction_dict["num_reactants"] == 1:
# "Try reaction"
zinc_database_comp_mol_name = None
comp_mol_id = None
try:
# if reaction works keep it
reaction_products_list = [
x[0] for x in rxn.RunReactants((mol_to_use,))
]
# randomly shuffle the lists of products so that we don't
# bias a single product type. ie ClickChem Reactions
# 5_Alkyne_and_Azide produces two products: a 1,5 isomer
# and a 1,4 isomer; This will shuffle the list and try
# each option
random.shuffle(reaction_products_list)
if (
reaction_products_list in [(), []]
or len(reaction_products_list) == 0
):
# if reaction fails then lets move on to the next
# reaction
tries = tries + 1
else:
is_rxn_complete = False
for reaction_product in reaction_products_list:
# Filter and check the product is valid
reaction_product_smilestring = self.check_if_product_is_good(
reaction_product
)
if reaction_product_smilestring is None:
is_rxn_complete = False
else:
# REACTION WORKED!
is_rxn_complete = True
break #### break while loop
if (
reaction_product_smilestring is not None
and is_rxn_complete is True
):
# REACTION WORKED!
break #### break while loop
# else:
tries = tries + 1
except:
# if reaction fails then lets move on to the next reaction
mol_to_use = None
tries = tries + 1
break #### ???
else: ### a_reaction_dict['num_reactants'] > 1
# for each functional group in the reaction, test
# if the ligand has that as a substructure
list_reactant_mols = []
comp_mol_id = []
for i in range(0, len(fun_groups_in_rxn)):
if i == contains_group:
# This is where the molecule goes
list_reactant_mols.append(mol_to_use)
else:
# for reactants which need to be taken from the
# complementary dictionary. Find the reactants
# functional group
functional_group_name = str(
a_reaction_dict["functional_groups"][i]
)
# Determine whether to react using the protanated or
# deprotanated form of the ligand
substructure = Chem.MolFromSmarts(
self.functional_group_dict[fun_groups_in_rxn[i]]
)
# lets give up to 100 tries to find a comp molecule
# which is viable
for find_mol_tries in range(0, 100):
# find that group in the complementary dictionary.
# comp_molecule = ["cccc", "ZINC123"]
comp_molecule = self.get_random_complementary_mol(
functional_group_name
)
# zinc_database name
zinc_database_comp_mol_name = comp_molecule[1]
# Smiles String of complementary molecule
comp_smiles_string = comp_molecule[0]
# check this is a santizable molecule
comp_mol = Chem.MolFromSmiles(
comp_smiles_string, sanitize=False
)
# try sanitizing, which is necessary later
comp_mol = MOH.check_sanitization(comp_mol)
# Try with deprotanated molecule rdkit to
# recognize for the reaction
comp_mol = MOH.try_deprotanation(comp_mol)
if comp_mol is None:
continue
if comp_mol.HasSubstructMatch(substructure) is True:
comp_mol = comp_mol
# append to ordered list
list_reactant_mols.append(comp_mol)
comp_mol_id.append(zinc_database_comp_mol_name)
break
# Try with deprotanated molecule rdkit to
# recognize for the reaction
comp_mol = MOH.try_deprotanation(comp_mol)
if comp_mol is None:
continue
if comp_mol.HasSubstructMatch(substructure) is True:
comp_mol = comp_mol
# append to ordered list
list_reactant_mols.append(comp_mol)
comp_mol_id.append(zinc_database_comp_mol_name)
break
comp_mol = None
continue
# we will make a tuple of the molecules as rdkit mol objects
# 1st we generate a list of reactant mol objects then we
# convert to tuple
# convert list to tuple
tuple_reactant_mols = tuple(list_reactant_mols)
# Run the reaction: We use a try/except statement incase an
# error occurs and rdkit is unable to complete the reaction.
# without this a failure to complete the reaction would result
# in the terminating.
# Try to run reaction
try:
# if reaction works keep it
reaction_products_list = [
x[0] for x in rxn.RunReactants(tuple_reactant_mols)
]
# randomly shuffle the lists of products so that we don't
# bias a single product type. ie ClickChem Reactions
# 5_Alkyne_and_Azide produces two products: a 1,5 isomer
# and a 1,4 isomer; This will shuffle the list and try
# each option
random.shuffle(reaction_products_list)
except:
reaction_product = None
tries = tries + 1
continue
if (
reaction_products_list in [(), []]
or len(reaction_products_list) == 0
):
reaction_id_number = a_reaction_dict["RXN_NUM"]
tries = tries + 1
continue
else:
is_rxn_complete = False
for reaction_product in reaction_products_list:
# Filter and check the product is valid
reaction_product_smilestring = self.check_if_product_is_good(
reaction_product
)
if reaction_product_smilestring is None:
is_rxn_complete = False
else:
# REACTION WORKED!
is_rxn_complete = True
break
if reaction_product_smilestring is not None and is_rxn_complete is True:
# REACTION WORKED!
break
# try again
tries = tries + 1
# end of the big while loop (while tries < len(shuffled_reaction_list)
# and is_rxn_complete is False)
# check that a reaction was successful
if is_rxn_complete is True:
reaction_product = MOH.check_sanitization(reaction_product)
if reaction_product is None:
return None
reaction_product_smilestring = Chem.MolToSmiles(
reaction_product, isomericSmiles=True
)
reaction_id_number = a_reaction_dict["RXN_NUM"]
# RETURNS THE NEW PRODUCTS SMILESTRING, THE REACTION ID NUMBER (SO
# ONE CAN TRACK THE MOLS LINEAGE). THE COMP_MOL ZINC DATABASE ID
# NUMBER (IF IT WAS A RXN WITH ONLY 1 REACTANT THIS IS None)
if comp_mol_id is None:
zinc_database_comp_mol_names = None
elif len(comp_mol_id) == 1:
zinc_database_comp_mol_names = comp_mol_id[0]
else:
zinc_database_comp_mol_names = "+".join(comp_mol_id)
product_info = [
reaction_product_smilestring,
reaction_id_number,
zinc_database_comp_mol_names,
]
return product_info
# reaction failed
return None
def run_smiles_click2(self, ligand_smiles_string):
"""This will take the full list of reaction names(self.shuffled_reaction_list) and return all the products smiles
Inputs:
:param str ligand_smiles_string: SMILES string of a molecule to be reacted (parent smiles)
Returns:
- list of all product smiles
- returns None if all reactions failed or input failed to convert to a sanitizable rdkit mol.
"""
try:
mol = Chem.MolFromSmiles(ligand_smiles_string, sanitize=False) # This is the input molecule which serves as the parent molecule
except:
return None
# try sanitizing, which is necessary later
mol = MOH.check_sanitization(mol)
if mol is None:
return None
# Is important for some functional groups while being deprotanated are useful for other reaction
mol_reprotanated = copy.deepcopy(mol)
mol_reprotanated = MOH.try_reprotanation(mol_reprotanated)
if mol_reprotanated is None:
return None
mol_deprotanated = copy.deepcopy(mol)
mol_deprotanated = MOH.try_deprotanation(mol_deprotanated)
if mol_deprotanated is None:
return None
# Determine which functional groups are within a ligand
list_subs_within_mol = self.determine_functional_groups_in_mol(mol_deprotanated, mol_reprotanated)
if len(list_subs_within_mol) == 0:
print("{} had no functional groups to react with.".format(ligand_smiles_string))
return None
shuffled_reaction_list = self.rand_key_list(self.reaction_dict) # Randomize the order of the list of reactions (shuffle)
tries = 0
is_rxn_complete = False
# go through all possible rxns in dictionary of rxns using the random
# order of rxns loop ends when a rxn is successful or when it runs out
# of reactions
returned_mol_list = [] #####
################################# main loop ########################################
while tries < len(shuffled_reaction_list): # and is_rxn_complete is False:
reaction_name = shuffled_reaction_list[tries]
a_reaction_dict = self.reaction_dict[reaction_name]
fun_groups_in_rxn = a_reaction_dict["functional_groups"]
contains_group = None
for i in range(0, len(fun_groups_in_rxn)):
if fun_groups_in_rxn[i] in list_subs_within_mol:
contains_group = i
# The number i which contains_group is now equal to will
# be used to remember the placement of the molecule later in the reaction.
break
continue
if contains_group is None: # Reaction doesn't contain a functional group found in the reactant molecule. move on to the next molecule
tries = tries + 1
continue
# Determine whether to react using the protanated or deprotanated form of the ligand
substructure = Chem.MolFromSmarts(self.functional_group_dict[fun_groups_in_rxn[i]])
if mol_deprotanated.HasSubstructMatch(substructure) is True:
mol_to_use = copy.deepcopy(mol_deprotanated)
else:
mol_to_use = copy.deepcopy(mol_reprotanated)
substructure = None
rxn = AllChem.ReactionFromSmarts(str(a_reaction_dict["reaction_string"]))
rxn.Initialize()
# if the reaction requires only a single reactant we will attempt to run the reaction
if a_reaction_dict["num_reactants"] == 1:
# "Try reaction"
zinc_database_comp_mol_name = None
comp_mol_id = None
try:
# if reaction works keep it
reaction_products_list = [x[0] for x in rxn.RunReactants((mol_to_use,))]
# randomly shuffle the lists of products so that we don't
# bias a single product type. ie ClickChem Reactions
# 5_Alkyne_and_Azide produces two products: a 1,5 isomer
# and a 1,4 isomer; This will shuffle the list and try each option
random.shuffle(reaction_products_list)
if not (reaction_products_list in [(), []] or len(reaction_products_list) == 0):
is_rxn_complete = False
for reaction_product in reaction_products_list:
# Filter and check the product is valid
reaction_product_smilestring = self.check_if_product_is_good(reaction_product)
if reaction_product_smilestring is not None:
returned_mol_list.append(reaction_product)
except: # if reaction fails then lets move on to the next reaction
mol_to_use = None
tries = tries + 1
else: ### a_reaction_dict['num_reactants'] > 1
# for each functional group in the reaction, test if the ligand has that as a substructure
list_reactant_mols = []
comp_mol_id = []
for i in range(0, len(fun_groups_in_rxn)):
if i == contains_group:
# This is where the molecule goes
list_reactant_mols.append(mol_to_use)
else:
# for reactants which need to be taken from the
# complementary dictionary. Find the reactants functional group
functional_group_name = str(a_reaction_dict["functional_groups"][i])
# Determine whether to react using the protanated or deprotanated form of the ligand
substructure = Chem.MolFromSmarts(self.functional_group_dict[fun_groups_in_rxn[i]])
# lets give up to 100 tries to find a comp molecule which is viable
for find_mol_tries in range(0, 100):
# find that group in the complementary dictionary. comp_molecule = ["cccc", "ZINC123"]
comp_molecule = self.get_random_complementary_mol(functional_group_name)
# zinc_database name
zinc_database_comp_mol_name = comp_molecule[1]
# Smiles String of complementary molecule
comp_smiles_string = comp_molecule[0]
# check this is a santizable molecule
comp_mol = Chem.MolFromSmiles(comp_smiles_string, sanitize=False)
# try sanitizing, which is necessary later
comp_mol = MOH.check_sanitization(comp_mol)
# Try with deprotanated molecule rdkit to recognize for the reaction
comp_mol = MOH.try_deprotanation(comp_mol)
if comp_mol is None:
continue
if comp_mol.HasSubstructMatch(substructure) is True:
comp_mol = comp_mol
# append to ordered list
list_reactant_mols.append(comp_mol)
comp_mol_id.append(zinc_database_comp_mol_name)
break
# Try with deprotanated molecule rdkit to recognize for the reaction
comp_mol = MOH.try_deprotanation(comp_mol)
if comp_mol is None:
continue
if comp_mol.HasSubstructMatch(substructure) is True:
comp_mol = comp_mol
# append to ordered list
list_reactant_mols.append(comp_mol)
comp_mol_id.append(zinc_database_comp_mol_name)
break
comp_mol = None
continue
# we will make a tuple of the molecules as rdkit mol objects
# 1st we generate a list of reactant mol objects then we
# convert to tuple
tuple_reactant_mols = tuple(list_reactant_mols)
# Run the reaction: We use a try/except statement incase an
# error occurs and rdkit is unable to complete the reaction.
# without this a failure to complete the reaction would result
# in the terminating.
try:
# if reaction works keep it
reaction_products_list = [x[0] for x in rxn.RunReactants(tuple_reactant_mols)]
# randomly shuffle the lists of products so that we don't
# bias a single product type. ie ClickChem Reactions
# 5_Alkyne_and_Azide produces two products: a 1,5 isomer
# and a 1,4 isomer; This will shuffle the list and try each option
random.shuffle(reaction_products_list)
except:
reaction_product = None
tries = tries + 1
continue
if reaction_products_list in [(), []] or len(reaction_products_list) == 0:
reaction_id_number = a_reaction_dict["RXN_NUM"]
tries = tries + 1
continue
else:
is_rxn_complete = False
for reaction_product in reaction_products_list:
# Filter and check the product is valid
reaction_product_smilestring = self.check_if_product_is_good(reaction_product)
if reaction_product_smilestring is not None:
returned_mol_list.append(reaction_product)
tries = tries + 1
################################# end of while loop #########################################
# end of the big while loop (while tries < len(shuffled_reaction_list)
# and is_rxn_complete is False)
## output is reaction_product & comp_mol_id
returned_smiles_list = []
for mol in returned_mol_list:
mol = MOH.check_sanitization(mol)
if mol is not None:
smiles = Chem.MolToSmiles(mol, isomericSmiles = True)
returned_smiles_list.append(smiles)
return returned_smiles_list
# # check that a reaction was successful
# if is_rxn_complete is True:
# reaction_product = MOH.check_sanitization(reaction_product)
# if reaction_product is None:
# return None
# reaction_product_smilestring = Chem.MolToSmiles(reaction_product, isomericSmiles=True)
# reaction_id_number = a_reaction_dict["RXN_NUM"]
# # RETURNS THE NEW PRODUCTS SMILESTRING, THE REACTION ID NUMBER (SO
# # ONE CAN TRACK THE MOLS LINEAGE). THE COMP_MOL ZINC DATABASE ID
# # NUMBER (IF IT WAS A RXN WITH ONLY 1 REACTANT THIS IS None)
# if comp_mol_id is None:
# zinc_database_comp_mol_names = None
# elif len(comp_mol_id) == 1:
# zinc_database_comp_mol_names = comp_mol_id[0]
# else:
# zinc_database_comp_mol_names = "+".join(comp_mol_id)
# product_info = [
# reaction_product_smilestring,
# reaction_id_number,
# zinc_database_comp_mol_names,
# ]
# return product_info
# # reaction failed
# return None
def check_if_product_is_good(self, reaction_product):
"""
This function will test whether the product passes all of the
requirements:
1) Mol sanitizes
2) It isn't in the self.list_of_already_made_smiles
3) It passes Filters
Returns the smile if it passes; returns None if it fails.
Inputs:
:param rdkit.Chem.rdchem.Mol reaction_product: an rdkit
molecule to be checked.
Returns:
:returns: str reaction_product_smilestring:
this will return either a SMILES string if it is a good molecule
or None if it can not sanitize and be cleaned
"""
reaction_product = MOH.check_sanitization(reaction_product)
if reaction_product is None:
return None
# Remove any fragments incase 1 made it through
reaction_product = MOH.handle_frag_check(reaction_product)
if reaction_product is None:
return None
# Make sure there are no unassigned atoms which made it through. These
# are very unlikely but possible
reaction_product = MOH.check_for_unassigned_atom(reaction_product)
if reaction_product is None:
return None
reaction_product = MOH.try_reprotanation(reaction_product)
if reaction_product is None:
return None
# Remove H's
reaction_product = MOH.try_deprotanation(reaction_product)
if reaction_product is None:
return None
reaction_product = MOH.check_sanitization(reaction_product)
if reaction_product is None:
return None
# Check if product SMILE has been made before
reaction_product_smilestring = Chem.MolToSmiles(
reaction_product, isomericSmiles=True
)
if reaction_product_smilestring in self.list_of_already_made_smiles:
return None
# Run through filters
pass_or_not = Filter.run_filter_on_just_smiles(
reaction_product_smilestring, self.filter_object_dict
)
if pass_or_not is False:
return None
# passes
return reaction_product_smilestring
| 53,667 | 42.03769 | 145 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/mutation/smiles_click_chem/reaction_libraries/all_rxns/fix.py | """
Developers script for merging robust_rxns and click_chem_rxns
into all_rxns... This script renumbers a rxn_libraries numbering
"""
import sys
def renumber_file(old_path, new_path, new_rxn_num):
"""
This is a developers tool to renumber a rxn_library .json file
Must provide the following:
1) old_path to original .json rxn_library file
2) new_path to output renumbered .json rxn_library file
3) new_rxn_num: number to reindex the 1st reaction to:
ie if new_rxn_num=37 then rxn_num=1 becomes rxn_num=37
Inputs:
:param str old_path: path to original .json rxn_library
file to be renumbered
:param str new_path: path to output renumbered .json rxn_library
:param int new_rxn_num: number to reindex the 1st reaction to:
ie if new_rxn_num=37 then rxn_num=1 becomes rxn_num=37
"""
printout = ""
original_rxn_num = 1 # index of the 1st reaction to adjust to rxn_num
with open(old_path, "r") as f:
for line in f.readlines():
if '": {' in line or "reaction_name" in line:
line = line.replace("{}_".format(
original_rxn_num), "{}_".format(new_rxn_num))
elif '"RXN_NUM": ' in line:
line = line.split(": ")[0] + ": {}\n".format(new_rxn_num)
new_rxn_num = new_rxn_num + 1
original_rxn_num = original_rxn_num + 1
printout = printout + line
with open(new_path, "w") as f:
f.write(printout)
if __name__ == "__main__":
# OLD_PATH ="$PATH/Input_rxn_library/temp.json"
# new_path="$PATH/output_renumbered_rxn_library/temp_new.json"
# NEW_RXN_NUM = 37
try:
OLD_PATH = sys.argv[1]
NEW_PATH = sys.argv[2]
NEW_RXN_NUM = sys.argv[3]
except:
print("This is a developers tool to renumber a rxn_library .json file \
Must provide the following: \
1) OLD_PATH to original .json rxn_library file \
2) NEW_PATH to output renumbered .json rxn_library file \
3) NEW_RXN_NUM: number to reindex the 1st reaction to: \
ie if NEW_RXN_NUM=37 then rxn_num=1 becomes rxn_num=37")
renumber_file(OLD_PATH, NEW_PATH, NEW_RXN_NUM)
#
| 2,288 | 35.333333 | 79 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/concatenate_files.py | """
This script compresses files which makes it easier to transfer data To
decompress the files use the script in
$PATH/autogrow4/accessory_scripts/file_concatenation_and_compression.py .
"""
import __future__
import glob
import os
import gzip
import shutil
def compress_file(file_name):
"""
Compress the concatenated file
Inputs:
:param str file_name: the path to the file to compress.
"""
with open(file_name, "r") as f:
printout = f.read()
printout = printout.encode("utf-8")
with gzip.open(file_name + ".gz", "wb") as f:
f.write(printout)
#######
def decompress_file(decompressed_file):
"""
Decompress a file. Not used in running the program but is the counter of
def compress_file(file_name)
Inputs:
:param str decompressed_file: the path to the file to decompress.
Returns:
:returns: str decompressed_file: the path to the file to decompress.
"""
out_file = decompressed_file.replace(".gz", "")
with gzip.open(decompressed_file, "rb") as f_comp:
with open(out_file, "wb") as f_decomp:
shutil.copyfileobj(f_comp, f_decomp)
return out_file
#######
def separate_files(compressed_file):
"""
separate a concatenated file. Not used in running the program but is the
counter of def compress_file(file_name)
Inputs:
:param str compressed_file: the path to the file to separate/decompress.
"""
directory = (
os.path.abspath(compressed_file.split(os.path.basename(compressed_file))[0])
+ os.sep
)
compressed_file = os.path.abspath(compressed_file)
decompressed_file = decompress_file(directory, compressed_file)
if os.path.exists(decompressed_file) is False:
raise Exception("Failed to decompress the file")
printout = ""
list_of_new_files = []
out_file = None
with open(decompressed_file, "r") as f:
for line in f.readlines():
if "$$END_FILE$$" in line:
if out_file is not None and os.path.exists(out_file) is False:
with open(out_file, "w") as f:
f.write(printout + "\n")
out_file = None
printout = ""
continue
elif "File_name:" in line:
printout = ""
# Split the line up and grab the relative file path convert to
# absolute path
out_file = (
directory
+ os.sep
+ line.split("##############################File_name: ")[
1
].replace("\n", "")
)
out_file = os.path.abspath(out_file)
list_of_new_files.append(out_file)
continue
else:
printout = printout + line
continue
all_are_made = True
for f in list_of_new_files:
if os.path.exists(f) is False:
print("file failed to decompress: {}".format(f))
all_are_made = False
if all_are_made is True:
torun = "rm {}".format(decompressed_file)
os.system(torun)
#######
def get_file_info(file_name):
"""
Used for concatenating files together. This function appends a seperator
and the filename of a file before and after the text of the file
file_name. It returns it as a string
Inputs:
:param str file_name: the path to the file to compress.
Returns:
:returns: str concat: the text of the file file_name with a seperator and
label before and after the file text.
"""
file_name_insert = "\n##############################File_name: {}\n".format(
os.path.basename(file_name)
)
file_termination_insert = "\n##############################$$END_FILE$$ {}".format(
os.path.basename(file_name)
)
concat = file_name_insert + open(file_name).read() + file_termination_insert
return concat
#######
def del_files(file_name):
"""
This function deletes a given file file_name.
Inputs:
:param str file_name: the path to delete.
"""
if os.path.exists(file_name):
try:
os.system("rm {}".format(file_name))
except:
print("couldn't delete file: {}".format(file_name))
#######
def run_concatenation(parallelizer_object, directory):
"""
This function concatenates and compresses every file in a directory. This
makes data transfer easier later on.
To decompress the folder please use script in
$PATH/autogrow4/file_concatenation_and_compression.py
parallelizer_object type is <class
'autogrow.operators.convert_files.gypsum_dl.gypsum_dl.Parallelizer.Parallelizer'>
Inputs:
:param Parallelizer_obj parallelizer_object: a paralellizer object used to
multiprocess. initialized from
autogrow/operators/convert_files/gypsum_dl/gypsum_dl/Parallelizer.py.
:param str directory: the path to the folder which will be compiled and compressed.
"""
concat_file = directory + os.sep + "compressed_PDBS.txt"
print(
"Start Concatenation: To separate files use the \
file_concatenation_and_compression.py in the Utility script folder."
)
file_list = glob.glob(directory + os.sep + "*")
file_list = [os.path.abspath(x) for x in file_list]
with open(concat_file, "a+") as f:
for file_name in file_list:
f.write(get_file_info(file_name))
job_list = tuple([(file_path,) for file_path in file_list])
print("\tFinish Concatenation")
print("\tRemoving files that were concatenated")
parallelizer_object.run(job_list, del_files)
print("\tCompressing file")
compress_file(concat_file)
if os.path.exists(concat_file + ".gz"):
del_files(concat_file)
print("Finished Compression")
| 5,893 | 30.021053 | 87 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/execute_docking.py | """
This script handles the docking and file conversion for docking.
"""
import __future__
import os
from autogrow.docking.docking_class.get_child_class import get_all_subclasses
from autogrow.docking.docking_class.docking_class_children import *
from autogrow.docking.docking_class.parent_dock_class import ParentDocking
# from autogrow.docking.docking_class.docking_class_children \
# import VinaDocking, QuickVina2Docking
from autogrow.docking.docking_class.docking_file_conversion import *
from autogrow.docking.docking_class.parent_pdbqt_converter import ParentPDBQTConverter
# from autogrow.docking.docking_class.docking_file_conversion \
# import convert_with_obabel, convert_with_mgltools
def pick_docking_class_dict(dock_choice):
"""
This will retrieve all the names of every child class of the parent class
ParentDocking
Inputs:
:param list dock_choice: List with the User specified docking choices
Returns:
:returns: object child_dict[dock_choice]: the class for running the chosen
docking method
"""
children = get_all_subclasses(ParentDocking)
child_dict = {}
for child in children:
child_name = child.__name__
child_dict[child_name] = child
return child_dict[dock_choice]
def pick_run_conversion_class_dict(conversion_choice):
"""
This will retrieve all the names of every child class of the parent class
ParentDocking
Inputs:
:param list conversion_choice: List with the User specified docking
choices
Returns:
:returns: object child_dict[conversion_choice]: the class for running the
chosen docking method
"""
children = get_all_subclasses(ParentPDBQTConverter)
child_dict = {}
for child in children:
child_name = child.__name__
child_dict[child_name] = child
return child_dict[conversion_choice]
def run_docking_common(vars, current_gen_int, current_generation_dir,
smile_file_new_gen):
"""
where is SMILES -> pdb
A. pdb -> pdbqt
B. run docking
---------------------
This section runs the functions common to all Docking programs.
IF ONE INCORPORATES A NEW DOCKING SOFTWARE, CONFIRM THAT ITS INPUT/OUTPUTS
CONFORM TO THIS SECTION.
############## VERY IMPORTANT SECTION ########################
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param int current_gen_int: the interger of the current generation indexed
to zero
:param str current_generation_dir: the current generation directory to
find the subfolder with pdb files
:param str smile_file_new_gen: the name of the file containing the
molecules in the new population
Returns:
:returns: str unweighted_ranked_smile_file: the name of the
unweighted-ranked SMILES with their docking score
"""
# Get directory string of PDB files for Ligands
current_generation_pdb_dir = current_generation_dir + "PDBs" + os.sep
dock_choice = vars["dock_choice"]
conversion_choice = vars["conversion_choice"]
receptor = vars["filename_of_receptor"]
# Use a temp vars dict so you don't put mpi multiprocess info through
# itself...
temp_vars = {}
for key in list(vars.keys()):
if key == "parallelizer":
continue
temp_vars[key] = vars[key]
file_conversion_class_object = pick_run_conversion_class_dict(conversion_choice)
file_conversion_class_object = file_conversion_class_object(temp_vars, receptor, test_boot=False)
dock_class = pick_docking_class_dict(dock_choice)
docking_object = dock_class(temp_vars, receptor, file_conversion_class_object, test_boot=False)
if vars["docking_executable"] is None:
docking_executable = docking_object.get_docking_executable_file(temp_vars)
vars["docking_executable"] = docking_executable
##### vina or Qvina
# Find PDB's
pdbs_in_folder = docking_object.find_pdb_ligands(current_generation_pdb_dir)
job_input_convert_lig = tuple([tuple([docking_object, pdb]) for pdb in pdbs_in_folder])
##############################################################
##### part A. ########
# print("####################")
# print("Convert Ligand from PDB to PDBQT format")
smiles_names_failed_to_convert = vars["parallelizer"].run(job_input_convert_lig, lig_convert_multithread)
########### print #############
# deleted_smiles_names_list_convert = [x for x in smiles_names_failed_to_convert if x is not None]
# deleted_smiles_names_list_convert = list(set(deleted_smiles_names_list_convert))
# if len(deleted_smiles_names_list_convert) != 0:
# print("THE FOLLOWING LIGANDS WHICH FAILED TO CONVERT:")
# print(deleted_smiles_names_list_convert)
# print("####################")
# Docking the ligands which converted to PDBQT Find PDBQT's
pdbqts_in_folder = docking_object.find_converted_ligands(current_generation_pdb_dir)
job_input_dock_lig = tuple([tuple([docking_object, pdbqt]) for pdbqt in pdbqts_in_folder])
#############################################################
###### part B. #######
# print("####################")
# print("Docking Begun")
#############################
########### main ############
#############################
smiles_names_failed_to_dock = vars["parallelizer"].run(job_input_dock_lig, run_dock_multithread)
# print("Docking Completed")
# print("####################")
######################################
############### print ##############
deleted_smiles_names_list_dock = [x for x in smiles_names_failed_to_dock if x is not None]
deleted_smiles_names_list_dock = list(set(deleted_smiles_names_list_dock))
# print("THE FOLLOWING LIGANDS WHICH FAILED TO DOCK:", deleted_smiles_names_list_dock)
# print("####################")
deleted_smiles_names_list = deleted_smiles_names_list_convert + deleted_smiles_names_list_dock
if len(deleted_smiles_names_list) != 0:
pass
# print("\nTHE FOLLOWING LIGANDS WHERE DELETED FOR FAILURE TO CONVERT OR DOCK:")
# print(deleted_smiles_names_list)
###################################################
############## part B2. retrieve the results
# print("#################### save results #####################")
# print("\nBegin Ranking and Saving results")
unweighted_ranked_smile_file = docking_object.rank_and_save_output_smi(vars, current_generation_dir, current_gen_int, smile_file_new_gen, deleted_smiles_names_list)
# print("\nCompleted Ranking and Saving results")
return unweighted_ranked_smile_file
def lig_convert_multithread(docking_object, pdb):
"""
Run the ligand conversion of a single molecule. If it failed
failed_smiles_name will be a string of the SMILE which failed to convert
If it converts failed_smiles_name will be a None.
Inputs:
:param object docking_object: the class for running the chosen docking
method
:param str pdb: the path to the pdb of a molecule
Returns:
:returns: list failed_smiles_name: if the molecule failed to convert to
final format. (ie. pdbqt conversion fail)
"""
failed_smiles_name = docking_object.run_ligand_handling_for_docking(pdb)
return failed_smiles_name
def run_dock_multithread(docking_object, pdb):
"""
Run the docking of a single molecule.
Inputs:
:param object docking_object: the class for running the chosen docking
method
:param str pdb: the path to the pdb of a molecule
Returns:
:returns: list failed_smiles_names: any smiles which were deleted (ie.
docking failed)
"""
# print("Attempt to Dock: ", pdb)
failed_smiles_names = docking_object.run_dock(pdb)
# print('------------- run_dock_multithread in execute_docking.py -----------')
return failed_smiles_names
| 8,035 | 35.035874 | 168 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/delete_failed_mol.py | """
delete_failed_mol.py deletes all files associated with a ligand
This is done to clean up failed attempts to dock.
"""
#######################
# Delete extra ligands #
########################
import glob
import os
def delete_all_associated_files(pdb_filename):
"""Delete files associated with a compound
Inputs:
:param str pdb_filename: the filename of the compounds.
"""
toremove = [pdb_filename]
toremove.extend(glob.glob(pdb_filename[:-3] + "*"))
toremove.extend(
glob.glob(
os.path.dirname(pdb_filename)
+ os.sep
+ "support"
+ os.sep
+ os.path.basename(pdb_filename)[:-3]
+ "*"
)
)
# Remove any redundancy
toremove = list(set(toremove))
# print("DELETING FOLLOWING!:", toremove)
for todel in toremove:
if os.path.exists(todel):
os.remove(todel)
#
| 920 | 20.418605 | 63 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/docking_class/get_child_class.py | """
An object for auto-detecting and creating jobs with the proper templates.
You'll need to import the base class first
"""
def get_all_subclasses(base_class):
"""
Method for getting all child classes from a parent object. Taken from:
http://stackoverflow.com/questions/3862310/how-can-i-find-all-subclasses-of-a-class-given-its-name
Inputs:
:param class base_class: The parent class which we are looking towards.
Returns:
:returns: list all_subclasses: A list of classes representing the child
classes of the base_class
"""
all_subclasses = []
for subclass in base_class.__subclasses__():
all_subclasses.append(subclass)
all_subclasses.extend(get_all_subclasses(subclass))
return all_subclasses
| 772 | 27.62963 | 102 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/docking_class/parent_pdbqt_converter.py | """
This script holds the parent class for file conversion for docking.
This is used as the basis for all file conversion classes.
"""
import __future__
class ParentPDBQTConverter(object):
"""
Docking
Inputs:
:param class object: a class to initialize on
"""
def __init__(self, vars=None, receptor_file=None, test_boot=True):
"""
Require to initialize any pdbqt conversion class.
Inputs:
:param dict vars: Dictionary of User variables
:param str receptor_file: the path for the receptor pdb
:param bool test_boot: used to initialize class without objects for
testing purpose
"""
pass
def get_name(self):
"""
Returns the current class name.
Returns:
:returns: str self.__class__.__name__: the current class name.
"""
return self.__class__.__name__
def convert_receptor_pdb_files_to_pdbqt(self, receptor_file, mgl_python,
receptor_template,
number_of_processors):
"""
Make sure a PDB file is properly formatted for conversion to pdbqt
Inputs:
:param str receptor_file: the file path of the receptor
:param str mgl_python: file path of the pythonsh file of mgl tools
:param str receptor_template: the receptor4.py file path from mgl
tools.
:param int number_of_processors: number of processors to multithread
"""
raise NotImplementedError(
"convert_receptor_pdb_files_to_pdbqt() not implemented"
)
def convert_ligand_pdb_file_to_pdbqt(self, pdb_file):
"""
Convert the ligands of a given directory from pdb to pdbqt format
Inputs:
:param str pdb_file: the file name, a string.
Returns:
:returns: bool bool: True if it worked; False if its the gypsum param
file or if it failed to make PDBQT
:returns: str smile_name: name of the SMILES string from a pdb file
None if its the param file
"""
raise NotImplementedError("rank_and_save_output_smi() not implemented")
| 2,221 | 30.742857 | 79 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/docking_class/parent_dock_class.py | """
This script holds the parent class for docking.
This is used as the basis for all docking classes.
"""
import __future__
class ParentDocking(object):
"""
Docking
Inputs:
:param class object: a class to initialize on
"""
def __init__(self, vars=None, receptor_file=None,
file_conversion_class_object=None, test_boot=True):
"""
Require to initialize any pdbqt conversion class.
Inputs:
:param dict vars: Dictionary of User variables
:param str receptor_file: the path for the receptor pdb
:param obj file_conversion_class_object: object which is used to
convert files from pdb to pdbqt
:param bool test_boot: used to initialize class without objects for
testing purpose
"""
pass
def get_name(self):
"""
Returns the current class name.
Returns:
:returns: str self.__class__.__name__: the current class name.
"""
return self.__class__.__name__
def run_dock(self, pdbqt_filename):
"""
run_dock is needs to be implemented in each class.
Inputs:
:param str pdbqt_filename: a string for docking process raise exception
if missing
"""
raise NotImplementedError("run_dock() not implemented")
def rank_and_save_output_smi(self, vars, current_generation_dir,
current_gen_int, smile_file,
deleted_smiles_names_list):
"""
rank_and_save_output_smi is needs to be implemented in each class.
raise exception if missing
Given a folder with PDBQT's, rank all the SMILES based on docking
score (High to low). Then format it into a .smi file. Then save the
file.
Inputs:
:param dict vars: vars needs to be threaded here because it has the
paralizer object which is needed within Scoring.run_scoring_common
:param str current_generation_dir: path of directory of current
generation
:param int current_gen_int: the interger of the current generation
indexed to zero
:param str smile_file: File path for the file with the ligands for the
generation which will be a .smi file
:param list deleted_smiles_names_list: list of SMILES which may have
failed the conversion process
Returns:
:returns: str output_ranked_smile_file: the path of the output ranked
.smi file
"""
raise NotImplementedError("rank_and_save_output_smi() not implemented")
| 2,657 | 32.225 | 79 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/docking_class/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/docking_class/docking_class_children/vina_docking.py | """
The child classes from ParentExample
"""
import __future__
import os
import sys
import glob
import autogrow.docking.delete_failed_mol as Delete
import autogrow.docking.ranking.ranking_mol as Ranking
from autogrow.docking.docking_class.parent_dock_class import ParentDocking
import autogrow.docking.scoring.execute_scoring_mol as Scoring
class VinaDocking(ParentDocking):
"""
RUN VINA DOCKING
Inputs:
:param class ParentDocking: Parent docking class to inherit from
"""
def __init__(self, vars=None, receptor_file=None,
file_conversion_class_object=None, test_boot=True):
"""
get the specifications for Vina/QuickVina2 from vars load them into
the self variables we will need and convert the receptor to the proper
file format (ie pdb-> pdbqt)
Inputs:
:param dict vars: Dictionary of User variables
:param str receptor_file: the path for the receptor pdb
:param obj file_conversion_class_object: object which is used to
convert files from pdb to pdbqt
:param bool test_boot: used to initialize class without objects for
testing purpose
"""
if test_boot is False:
self.vars = vars
self.debug_mode = vars["debug_mode"]
self.file_conversion_class_object = file_conversion_class_object
# VINA SPECIFIC VARS
receptor_file = vars["filename_of_receptor"]
# mgl_python = vars["mgl_python"]
# receptor_template = vars["prepare_receptor4.py"]
# number_of_processors = vars["number_of_processors"]
# docking_executable = vars["docking_executable"]
###########################
self.receptor_pdbqt_file = receptor_file + "qt"
self.vars["docking_executable"] = self.get_docking_executable_file(
self.vars
)
def run_ligand_handling_for_docking(self, pdb_file):
"""
this function converts the ligands from PDB to PDBQT format. Returns
NONE if it worked and the name if it failed to convert.
Inputs:
:param str pdb_file: the pdb file of a ligand to format, dock and
score
Returns:
:returns: str smile_name: name of smiles if it failed to dock returns
None if it docked properly
"""
# convert ligands to pdbqt format
# log("\nConverting ligand PDB files to PDBQT format...")
did_it_convert, smile_name = self.file_conversion_class_object.convert_ligand_pdb_file_to_pdbqt(pdb_file)
if did_it_convert is False:
# conversion failed
return smile_name
# Conversion pass. Return None
# only return failed smile_names which will be handled later
return None
def run_dock(self, pdbqt_filename):
"""
this function runs the docking. Returns None if it worked and the name
if it failed to dock.
Inputs:
:param str pdbqt_filename: the pdbqt file of a ligand to dock and
score
Returns:
:returns: str smile_name: name of smiles if it failed to dock returns
None if it docked properly
"""
# print(" -------- Docking compounds using AutoDock Vina...")
self.dock_ligand(pdbqt_filename)
# check that it docked
pdb_filename = pdbqt_filename.replace("qt", "")
did_it_dock, smile_name = self.check_docked(pdb_filename)
if did_it_dock is False:
# Docking failed
if smile_name is None:
# print("Missing pdb and pdbqt files for : ", pdbqt_filename)
pass
return smile_name
return None
#######################################
# STUFF DONE BY THE INIT
##########################################
def get_docking_executable_file(self, vars):
"""
This retrieves the docking executable files Path.
Inputs:
:param dict vars: Dictionary of User variables
Returns:
:returns: str docking_executable: String for the docking executable
file path
"""
if vars["docking_executable"] is None:
# get default docking_executable for vina
script_dir = str(os.path.dirname(os.path.realpath(__file__)))
docking_executable_directory = (
script_dir.split(os.sep + "docking_class")[0]
+ os.sep
+ "docking_executables"
+ os.sep
)
if sys.platform == "linux" or sys.platform == "linux2":
# Use linux version of Autodock Vina
docking_executable = (
docking_executable_directory
+ "vina"
+ os.sep
+ "autodock_vina_1_1_2_linux_x86"
+ os.sep
+ "bin"
+ os.sep
+ "vina"
)
elif sys.platform == "darwin":
# Use OS X version of Autodock Vina
docking_executable = (
docking_executable_directory
+ "vina"
+ os.sep
+ "autodock_vina_1_1_2_mac"
+ os.sep
+ "bin"
+ os.sep
+ "vina"
)
elif sys.platform == "win32":
# Windows...
raise Exception("Windows is currently not supported")
else:
raise Exception("This OS is currently not supported")
else:
# if user specifies a different vina executable
docking_executable = vars["docking_executable"]
if os.path.exists(docking_executable) is False:
printout = "Docking executable could not be found at: "
printout = printout + "{}".format(docking_executable)
print(printout)
raise Exception(printout)
return docking_executable
# Finding PDBs for ligands in a folder
def find_pdb_ligands(self, current_generation_pdb_dir):
"""
This finds all the pdb files of ligands in a directory
Inputs:
:param str current_generation_pdb_dir: the dir path which contains the
pdb files of ligands to be converted
Returns:
:returns: list pdbs_in_folder: a list of all PDB's in the dir
"""
# make list of every pdb in the current generations pdb folder
pdbs_in_folder = []
for filename in glob.glob(current_generation_pdb_dir + "*.pdb"):
pdbs_in_folder.append(filename)
return pdbs_in_folder
# Find ligands which converted to PDBQT
def find_converted_ligands(self, current_generation_pdb_dir):
"""
This finds all the pdbqt files of ligands in a directory
Inputs:
:param str current_generation_pdb_dir: the dir path which contains the
pdbqt files of ligands to be docked
Returns:
:returns: list pdbqts_in_folder: a list of all PDBqt's in the dir
"""
# make list of every pdbqt in the current generations pdb folder
pdbqts_in_folder = []
for filename in glob.glob(current_generation_pdb_dir + "*.pdbqt"):
pdbqts_in_folder.append(filename)
return pdbqts_in_folder
#######################################
# DOCK USING VINA #
#######################################
def dock_ligand(self, lig_pdbqt_filename):
"""
Dock the ligand pdbqt files in a given directory using AutoDock Vina
Inputs:
:param str lig_pdbqt_filename: the ligand pdbqt filename
"""
vars = self.vars
timeout_option = vars["timeout_vs_gtimeout"]
docking_timeout_limit = vars["docking_timeout_limit"]
# do the docking of the ligand Run with a timeout_option limit.
# Default setting is 5 minutes. This is excessive as most things run
# within 30seconds This will prevent stalling out. timeout or gtimeout
torun = (
"{} {} {}".format(timeout_option, docking_timeout_limit, vars["docking_executable"])
+ " --center_x "
+ str(vars["center_x"])
+ " --center_y "
+ str(vars["center_y"])
+ " --center_z "
+ str(vars["center_z"])
+ " --size_x "
+ str(vars["size_x"])
+ " --size_y "
+ str(vars["size_y"])
+ " --size_z "
+ str(vars["size_z"])
+ " --receptor "
+ self.receptor_pdbqt_file
+ " --ligand "
+ lig_pdbqt_filename
+ " --out "
+ lig_pdbqt_filename
+ ".vina --cpu 1"
)
# Add optional user variables additional variable
if (
vars["docking_exhaustiveness"] is not None
and vars["docking_exhaustiveness"] != "None"
):
if (
type(vars["docking_exhaustiveness"]) == int
or type(vars["docking_exhaustiveness"]) == float
):
torun = (
torun
+ " --exhaustiveness "
+ str(int(vars["docking_exhaustiveness"]))
)
if vars["docking_num_modes"] is not None and vars["docking_num_modes"] != "None":
if (
type(vars["docking_num_modes"]) == int
or type(vars["docking_num_modes"]) == float
):
torun = torun + " --num_modes " + str(int(vars["docking_num_modes"]))
# Add output line MUST ALWAYS INCLUDE THIS LINE
torun = (
torun
+ " >>"
+ lig_pdbqt_filename
+ "_docking_output.txt "
+ " 2>>"
+ lig_pdbqt_filename
+ "_docking_output.txt"
)
# print("\tDocking: {}".format(lig_pdbqt_filename))
results = self.execute_docking_vina(torun)
if results is None or results is None or results == 256:
made_changes = self.replace_atoms_not_handled_by_forcefield(
lig_pdbqt_filename
)
if made_changes is True:
results = self.execute_docking_vina(torun)
if results == 256 or results is None:
# print(
# "\nLigand failed to dock after corrections: {}\n".format(
# lig_pdbqt_filename
# )
# )
pass
else:
# print("\tFinished Docking: {}".format(lig_pdbqt_filename))
pass
else:
pass
# print("\tFinished Docking: {}".format(lig_pdbqt_filename))
def replace_atoms_not_handled_by_forcefield(self, lig_pdbqt_filename):
"""
Replaces atoms not handled by the forcefield to prevent errors. Atoms
include B and Si.
Inputs:
:param str lig_pdbqt_filename: the ligand pdbqt filename
Returns:
:returns: bool retry: If True it will be ligand will be redocked, if
False its dones and wont be docked again.
"""
# VINA/QuickVINA and MGL have problems with the forcefields for
# certain atom types To correct this, Autodock Vina suggests replacing
# the
atoms_to_replace = [
"B \n",
"B\n",
"Si \n",
"Si\n",
] # add the \n at the end so we replace the end portion of the line
printout_of_file = ""
printout_info = ""
retry = False
line_count = 0
with open(lig_pdbqt_filename, "r") as f:
for line in f.readlines():
line_count = line_count + 1
if "HETATM" in line:
for x in atoms_to_replace:
if x in line:
line = line.replace(x, "A \n")
retry = True
printout_info = (
printout_info
+ "Changing '{}' to 'A ' in line: {} of {}".format(
str(x.strip()), line_count, lig_pdbqt_filename
)
) # x Need to remove whitespaces on both ends
printout_of_file = printout_of_file + line
if retry is True:
print(printout_info)
with open(lig_pdbqt_filename, "w") as f:
f.write(printout_of_file)
else:
printout_info = "\nCheck the docking message for 'Parse error on'"
printout_info = (
printout_info
+ "\n\t This ligand failed to dock. Please check that all "
+ "atoms are covered by the docking forcefield"
)
printout_info = (
printout_info
+ "\n\t Any atoms not covered by the forcefield should be "
+ "added to atoms_to_replace in the function "
+ "replace_atoms_not_handled_by_forcefield"
)
printout_info = printout_info + "\n\t Verify for this ligand: {}\n".format(
lig_pdbqt_filename
)
print(printout_info)
return retry
def execute_docking_vina(self, command):
"""
Run a single docking execution command
Inputs:
:param str command: string of command to run.
Returns:
:returns: int result: the exit output for the command. If its None of
256 it failed.
"""
try:
result = os.system(command)
except:
result = None
print("Failed to execute: " + command)
return result
def check_docked(self, pdb_file):
"""
given a pdb_file name, test if a pdbqt.vina was created. If it failed
to dock delete the file pdb and pdbqt file for that ligand -then
return false
if it docked properly return True
Inputs:
:param str pdb_file: pdb file path
Returns:
:returns: bool bool: false if not vina was unsuccessful
:returns: str smile_name: name of the pdb file
"""
if not os.path.exists(pdb_file):
# PDB file doesn't exist
return False, None
smile_name = self.file_conversion_class_object.get_smile_name_from_pdb(
pdb_file
)
if not os.path.exists(pdb_file + "qt.vina"):
# so this pdbqt.vina file didn't exist
if self.debug_mode is False:
# print("Docking unsuccessful: Deleting "
# + os.path.basename(pdb_file) + "...")
# REMOVE Failed molecules. Delete ones that were not docked
# successfully
Delete.delete_all_associated_files(pdb_file)
# # delete pdbqt_file
pdbqt_file = pdb_file + "qt"
Delete.delete_all_associated_files(pdbqt_file)
return False, smile_name
# Failed to dock but in debug mode
# print("Docking unsuccessful: " + os.path.basename(pdb_file) + "...")
return False, smile_name
# Successfully docked
return True, smile_name
##########################################
# Convert the dock outputs to a usable formatted .smi file
# This is mandatory for all Docking classes but
# implementation and approach varies by docking and scoring choice
##########################################
def rank_and_save_output_smi(self, vars, current_generation_dir,
current_gen_int, smile_file,
deleted_smiles_names_list):
"""
Given a folder with PDBQT's, rank all the SMILES based on docking
score (High to low). Then format it into a .smi file. Then save the
file.
Inputs:
:param dict vars: vars needs to be threaded here because it has the
paralizer object which is needed within Scoring.run_scoring_common
:param str current_generation_dir: path of directory of current
generation
:param int current_gen_int: the interger of the current generation
indexed to zero
:param str smile_file: File path for the file with the ligands for
the generation which will be a .smi file
:param list deleted_smiles_names_list: list of SMILES which may have
failed the conversion process
Returns:
:returns: str output_ranked_smile_file: the path of the output ranked
.smi file
"""
# Get directory string of PDB files for Ligands
folder_with_pdbqts = current_generation_dir + "PDBs" + os.sep
# Run any compatible Scoring Function
smiles_list = Scoring.run_scoring_common(vars, smile_file, folder_with_pdbqts)
# Before ranking these we need to handle Pass-Through ligands from the
# last generation If it's current_gen_int==1 or if
# vars['redock_elite_from_previous_gen'] is True -Both of these states
# dock all ligands from the last generation so all of the pass-through
# lig are already in the PDB's folder thus they should be accounted
# for in smiles_list If vars['redock_elite_from_previous_gen'] is False
# and current_gen_int != 1 - We need to append the scores form the
# last gen to smiles_list
# Only add these when we haven't already redocked the ligand
if (
self.vars["redock_elite_from_previous_gen"] is False
and current_gen_int != 0
):
# Go to previous generation folder
prev_gen_num = str(current_gen_int - 1)
run_folder = self.vars["output_directory"]
previous_gen_folder = run_folder + "generation_{}{}".format(
str(prev_gen_num), os.sep
)
ranked_smi_file_prev_gen = (
previous_gen_folder
+ "generation_{}_ranked.smi".format(str(prev_gen_num))
)
# Also check sometimes Generation 1 won't have a previous
# generation to do this with and sometimes it will
if (
current_gen_int == 1
and os.path.exists(ranked_smi_file_prev_gen) is False
):
pass
else:
print("Getting ligand scores from the previous generation")
# Shouldn't happen but to be safe.
if os.path.exists(ranked_smi_file_prev_gen) is False:
raise Exception(
"Previous generation ranked .smi file does not exist. "
+ "Check if output folder has been moved"
)
# Get the data for all ligands from previous generation ranked
# file
prev_gen_data_list = Ranking.get_usable_format(ranked_smi_file_prev_gen)
# Get the list of pass through ligands
current_gen_pass_through_smi = (
current_generation_dir
+ "SeedFolder{}Chosen_Elite_To_advance_Gen_{}.smi".format(
os.sep, str(current_gen_int)
)
)
pass_through_list = Ranking.get_usable_format(
current_gen_pass_through_smi
)
# Convert lists to searchable Dictionaries.
prev_gen_data_dict = Ranking.convert_usable_list_to_lig_dict(
prev_gen_data_list
)
pass_through_data = []
for lig in pass_through_list:
smile_plus_id = str(lig[0] + lig[1])
lig_data = prev_gen_data_dict[smile_plus_id]
lig_info_remove_diversity = [
lig_data[x] for x in range(0, len(lig_data) - 1)
]
pass_through_data.append(lig_info_remove_diversity)
smiles_list.extend(pass_through_data)
# Output format of the .smi file will be: SMILES Full_lig_name
# shorthandname ...AnyCustominfo... Fitness_metric diversity
# Normally the docking score is the fitness metric but if we use a
# Custom metric than dock score gets moved to index -3 and the new
# fitness metric gets -2
# sort list by the affinity of each sublist (which is the last index
# of sublist)
smiles_list.sort(key=lambda x: float(x[-1]), reverse=False)
# score the diversity of each ligand compared to the rest of the
# ligands in the group this adds on a float in the last column for the
# sum of pairwise comparisons the lower the diversity score the more
# unique a molecule is from the other mols in the same generation
smiles_list = Ranking.score_and_append_diversity_scores(smiles_list)
# name for the output file
output_ranked_smile_file = smile_file.replace(".smi", "") + "_ranked.smi"
# save to a new output smiles file. ie. save to ranked_smiles_file
with open(output_ranked_smile_file, "w") as output:
for ligand_info_list in smiles_list:
str_ligand_info_list = [str(x) for x in ligand_info_list]
output_line = "\t".join(str_ligand_info_list) + "\n"
output.write(output_line)
return output_ranked_smile_file
| 21,978 | 36.315789 | 113 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/docking_class/docking_class_children/quick_vina_2_docking.py | """
The child classes from ParentExample
"""
import __future__
import os
import sys
from autogrow.docking.docking_class.docking_class_children.vina_docking import VinaDocking
class QuickVina2Docking(VinaDocking):
"""
RUN QuickVina2 Docking
Inputs:
:param class ParentDocking: Parent docking class to inherit from
"""
def __init__(self, vars=None, receptor_file=None,
file_conversion_class_object=None, test_boot=True):
"""
get the specifications for Vina/QuickVina2 from vars load them into
the self variables we will need and convert the receptor to the proper
file format (ie pdb-> pdbqt)
Inputs:
:param dict vars: Dictionary of User variables
:param str receptor_file: the path for the receptor pdb
:param obj file_conversion_class_object: object which is used to
convert files from pdb to pdbqt
:param bool test_boot: used to initialize class without objects for
testing purpose
"""
if test_boot is False:
self.vars = vars
self.debug_mode = vars["debug_mode"]
self.file_conversion_class_object = file_conversion_class_object
# VINA SPECIFIC VARS
receptor_file = vars["filename_of_receptor"]
# mgl_python = vars["mgl_python"]
# receptor_template = vars["prepare_receptor4.py"]
# number_of_processors = vars["number_of_processors"]
# docking_executable = vars["docking_executable"]
###########################
self.receptor_pdbqt_file = receptor_file + "qt"
self.vars["docking_executable"] = self.get_docking_executable_file(
self.vars
)
def get_docking_executable_file(self, vars):
"""
This retrieves the docking executable files Path.
Inputs:
:param dict vars: Dictionary of User variables
Returns:
:returns: str docking_executable: String for the docking executable
file path
"""
# This must already be true if we are here vars["dock_choice"] ==
# "QuickVina2Docking"
if vars["docking_executable"] is None:
# get default docking_executable for QuickVina2
script_dir = str(os.path.dirname(os.path.realpath(__file__)))
docking_executable_directory = (
script_dir.split(os.sep + "docking_class")[0]
+ os.sep
+ "docking_executables"
+ os.sep
)
if sys.platform == "linux" or sys.platform == "linux2":
# Use linux version of Autodock Vina
docking_executable = (
docking_executable_directory
+ "q_vina_2"
+ os.sep
+ "q_vina_2_1_linux"
+ os.sep
+ "qvina2.1"
)
elif sys.platform == "darwin":
# Use OS X version of Autodock Vina
docking_executable = (
docking_executable_directory
+ "q_vina_2"
+ os.sep
+ "q_vina_2_1_mac"
+ os.sep
+ "qvina2.1"
)
elif sys.platform == "win32":
# Windows...
raise Exception("Windows is currently not supported")
else:
raise Exception("This OS is currently not supported")
else:
# if user specifies a different QuickVina executable
docking_executable = vars["docking_executable"]
if os.path.exists(docking_executable) is False:
printout = "Docking executable could not be found at: "
printout = printout + "{}".format(docking_executable)
print(printout)
raise Exception(printout)
return docking_executable
| 4,016 | 32.198347 | 90 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/docking_class/docking_class_children/__init__.py |
"""
This imports all of the filters within the FilterClasses file. This is very
important as the filters will not work if this doesn't exist.
This is a dynamic and modular way of doing these imports.
Code is taken from:
https://stackoverflow.com/questions/1057431/how-to-load-all-modules-in-a-folder
"""
from os.path import dirname, basename, isfile
import glob
modules = glob.glob(dirname(__file__)+"/*.py")
__all__ = [basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]
from . import *
| 524 | 26.631579 | 92 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/docking_class/docking_file_conversion/convert_with_mgltools.py | """
The child classes from ParentExample
"""
import __future__
import os
import subprocess
import rdkit.Chem as Chem
import autogrow.docking.delete_failed_mol as Delete
import autogrow.operators.convert_files.gypsum_dl.gypsum_dl.MolObjectHandling as MOH
from autogrow.docking.docking_class.parent_pdbqt_converter import ParentPDBQTConverter
class MGLToolsConversion(ParentPDBQTConverter):
"""
This is a class to convert ligands from PDB to PDBQT format using MGLTools
MGLTools citations:
- Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K.,
Goodsell, D. S. and Olson, A. J. (2009) Autodock4 and AutoDockTools4:
automated docking with selective receptor flexiblity. J. Computational
Chemistry 2009, 16: 2785-91
Inputs:
:param class ParentPDBQTConverter: Parent PDBQTConverter class to inherit from
"""
def __init__(self, vars=None, receptor_file=None, test_boot=True):
"""
get the specifications for Vina from vars load them into the self
variables we will need and convert the receptor to the proper file
format (ie pdb-> pdbqt)
Inputs:
:param dict vars: Dictionary of User variables
:param str receptor_file: the path for the receptor pdb
:param bool test_boot: used to initialize class without objects for testing purpose
"""
if test_boot is False:
self.vars = vars
self.debug_mode = vars["debug_mode"]
# VINA SPECIFIC VARS
receptor_file = vars["filename_of_receptor"]
mgl_python = vars["mgl_python"]
receptor_template = vars["prepare_receptor4.py"]
number_of_processors = vars["number_of_processors"]
docking_executable = vars["docking_executable"]
###########################
# convert Receptor from PDB to PDBQT
self.convert_receptor_pdb_files_to_pdbqt(
receptor_file, mgl_python, receptor_template, number_of_processors
)
self.receptor_pdbqt_file = receptor_file + "qt"
def convert_receptor_pdb_files_to_pdbqt(self, receptor_file, mgl_python,
receptor_template,
number_of_processors):
"""
Make sure a PDB file is properly formatted for conversion to pdbqt
Inputs:
:param str receptor_file: the file path of the receptor
:param str mgl_python: file path of the pythonsh file of mgl tools
:param str receptor_template: the receptor4.py file path from mgl tools.
:param int number_of_processors: number of processors to multithread
"""
count = 0
while not os.path.exists(receptor_file + "qt"):
count = count + 1
if count > 10000:
print(
"ERROR: I've tried 10,000 times to convert the file \""
+ receptor_file
+ '" to the PDBQT format. Aborting program...'
)
raise ValueError(
"ERROR: I've tried 10,000 times to convert the file \""
+ receptor_file
+ '" to the PDBQT format. Aborting program...'
)
# make sure the receptors have been converted to PDBQT. If not, do
# the conversion.
receptors = [receptor_file]
need_to_covert_receptor_to_pdbqt = []
for filename in receptors:
if not os.path.exists(filename + "qt"):
need_to_covert_receptor_to_pdbqt.append(filename)
# This should only be 1 receptor. If Autogrow is expanded to
# handle multiple receptor, one will need Multiprocess this. Fix
# this to 1 processor, so no overwriting issues, but could be
# expanded if we ever wanted to do multiple receptors
# create a file to run the pdbqt
for i in need_to_covert_receptor_to_pdbqt:
output = self.prepare_receptor_multiprocessing(
mgl_python, receptor_template, i
)
def prepare_receptor_multiprocessing(self, mgl_python, prepare_script,
mol_filename):
"""
This prepares the receptor for multiprocessing.
Inputs:
:param str mgl_python: file path of the pythonsh file of mgl tools
:param str prepare_script: the file path for the mgltool receptor
prep file receptor4
:param str mol_filename: the file path of the receptor
"""
command = (
mgl_python
+ " "
+ prepare_script
+ " -r "
+ mol_filename
+ " -o "
+ mol_filename
+ "qt"
)
try:
os.system(command)
except:
raise Exception("Could not convert receptor with MGL_tools")
#######################################
# Convert the Ligand from PDB to PDBQT DockingModel
##########################################
def convert_ligand_pdb_file_to_pdbqt(self, pdb_file):
"""
Convert the ligands of a given directory from pdb to pdbqt format
Inputs:
:param str pdb_file: the file name, a string.
Returns:
:returns: bool bool: True if it worked; False if its the gypsum param
file or if it failed to make PDBQT
:returns: str smile_name: name of the SMILES string from a pdb file
None if its the param file
"""
assert pdb_file is not None
smile_name = self.get_smile_name_from_pdb(pdb_file)
ligand4_template = self.vars["prepare_ligand4.py"]
mgl_python = self.vars["mgl_python"]
# gypsum makes 1 files labeled params which is not a valid pdb, but is
# actually a log Do not convert the params files
if "params" in pdb_file:
return False, None
# if the file already has been converted to a .pbdqt skip this file
# take all other pdb file names
if not os.path.exists(pdb_file + "qt"):
# make sure its in proper format
self.convert_pdb_to_pdbqt_acceptable_format(pdb_file)
self.prepare_ligand_processing(mgl_python, ligand4_template, pdb_file)
if not os.path.exists(pdb_file + "qt"):
# FILE FAILED TO CONVERT TO PDBQT DELETE PDB AND RETURN FALSE
if self.debug_mode is False:
print("PDBQT not generated: Deleting " + os.path.basename(pdb_file) + "...")
# REMOVED FOR LIGANDS WHICH FAILED TO CONVERT TO PDBQT
Delete.delete_all_associated_files(pdb_file)
return False, smile_name
# In debug mode but pdbqt file does not exist
print("PDBQT not generated: " + os.path.basename(pdb_file) + "...")
return False, smile_name
return True, smile_name
# Convert Ligand from PDB to PDBQT conversion
def prepare_ligand_processing(self, mgl_python, prepare_script, mol_filename):
"""
This function will convert a single ligand from PDB to PDBQT using
MGLTools. It has 10seconds to successfully convert this. It will try to
convert the ligand up to 3 times If it fails to do so 3 times, whether
because it timed out or because MGLTools failed or because of an
MGLTools Glitch, it will stop and the ligand won't be docked.
It will print the ligand if it fails 3 times. It will also fail if the
molecule is unable to be imported into rdkit and sanitized. This is
because MGLTools is sensitive to issues like atoms replaced with *,
formatting errors, and improper valences. Because MGLTools will crash
with these issues the RDKit check is especially useful to prevent hard
crashes.
Inputs:
:param str mgl_python: file path of the pythonsh file of mgl tools
:param str prepare_script: the file path for the mgltool ligand prep
file receptor4
:param str mol_filename: the file path of the ligand
"""
vars = self.vars
timeout_option = vars["timeout_vs_gtimeout"]
# Check that the PDB is a valid PDB file in RDKIT
try:
mol = Chem.MolFromPDBFile(mol_filename, sanitize=False, removeHs=False)
if mol is not None:
mol = MOH.check_sanitization(mol)
except:
mol = None
temp_file = "{}_temp".format(mol_filename)
# print('-----------------------------', mol_filename, 'mol_filename')
if mol is not None:
count = 0
# timeout or gtimeout
command = (
timeout_option + " 10 "
+ mgl_python
+ " " + prepare_script
+ " -g -l " + mol_filename
+ " -o " + mol_filename + "qt"
)
while not os.path.exists(mol_filename + "qt"):
if count < 3:
# We will try up to 3 times
try:
subprocess.check_output(
command + " 2> " + temp_file, shell=True
)
except:
try:
os.system(command + " 2> " + temp_file)
except:
pass
if os.path.exists(mol_filename + "qt") is False:
printout = "Failed to convert {} times: {}".format(
count, mol_filename
)
print(printout)
count = count + 1
else:
printout = "COMPLETELY FAILED TO CONVERT: {}".format(mol_filename)
print(printout)
break
if self.debug_mode is False:
if os.path.exists(temp_file) is True:
command = "rm {}".format(temp_file)
try:
os.system(command)
except:
print("Check permissions. Could not delete {}".format(temp_file))
# Convert PDB to acceptable PDBQT file format before converting
def convert_pdb_to_pdbqt_acceptable_format(self, filename):
"""
Make sure a PDB file is properly formatted for conversion to pdbqt
Inputs:
:param str filename: the file path of the pdb file to be converted
"""
# read in the file
output_lines = []
with open(filename, "r") as f:
for line in f.readlines():
line = line.replace("\n", "")
if line[:5] == "ATOM " or line[:7] == "HETATM ":
# fix things like atom names with two letters
first = line[:11]
middle = (
line[11:17].upper().strip()
) # Need to remove whitespaces on both ends
last = line[17:]
middle_firstpart = ""
middle_lastpart = middle
for i in range(len(middle_lastpart)):
if middle_lastpart[:1].isupper() is True:
middle_firstpart = middle_firstpart + middle_lastpart[:1]
middle_lastpart = middle_lastpart[1:]
else:
break # you reached the first number
# now if there are more than two letters in
# middle_firstpart, keep just two
if len(middle_firstpart) > 2:
middle_lastpart = middle_firstpart[2:] + middle_lastpart
middle_firstpart = middle_firstpart[:2]
if middle_firstpart not in ["BR", "ZN", "FE",
"MN", "CL", "MG"]:
# so just keep the first letter for the element part
# of the atom name
middle_lastpart = middle_firstpart[1:] + middle_lastpart
middle_firstpart = middle_firstpart[:1]
middle = middle_firstpart.rjust(3) + middle_lastpart.ljust(3)
line = first + middle + last
# make sure all parts of the molecule belong to the same
# chain and resid
line = line[:17] + "LIG X 999" + line[26:]
output_lines.append(line)
else:
output_lines.append(line)
with open(filename, "w") as f:
for line in output_lines:
f.write(line + "\n")
#######################################
# Handle Failed PDBS #
#######################################
def get_smile_name_from_pdb(self, pdb_file):
"""
This will return the unique identifier name for the compound
Inputs:
:param str pdb_file: pdb file path
Returns:
:returns: str line_stripped: the name of the SMILES string with the
new lines and COMPND removed
"""
if os.path.exists(pdb_file):
with open(pdb_file, "r") as f:
for line in f.readlines():
if "COMPND" in line:
line_stripped = line.replace(
"COMPND", ""
).strip() # Need to remove whitespaces on both ends
line_stripped = line_stripped.replace(
"\n", ""
).strip() # Need to remove whitespaces on both ends
compound_name = line_stripped
# line_stripped is now the name of the smile for this compound
else:
line_stripped = "unknown"
return line_stripped
| 14,188 | 38.745098 | 96 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/docking_class/docking_file_conversion/convert_with_obabel.py | """
The child classes from ParentExample
"""
import __future__
import os
import subprocess
import datetime
import rdkit.Chem as Chem
import autogrow.docking.delete_failed_mol as Delete
import autogrow.operators.convert_files.gypsum_dl.gypsum_dl.MolObjectHandling as MOH
from autogrow.docking.docking_class.parent_pdbqt_converter import ParentPDBQTConverter
class ObabelConversion(ParentPDBQTConverter):
"""
This is a class to convert ligands from PDB to PDBQT format using
commandline obabel
Openbabel citations:
- N M O'Boyle, M Banck, C A James, C Morley, T Vandermeersch, and G R
Hutchison. "Open Babel: An open chemical toolbox." J. Cheminf. (2011),
3, 33. DOI:10.1186/1758-2946-3-33
- The Open Babel Package, version 2.3.1 http://openbabel.org (accessed
Oct 2011)
Inputs:
:param class ParentPDBQTConverter: Parent PDBQTConverter class to inherit
from
"""
def __init__(self, vars=None, receptor_file=None, test_boot=True):
"""
get the specifications for Vina from vars load them into the self
variables we will need and convert the receptor to the proper file
format (ie pdb-> pdbqt)
Inputs:
:param dict vars: Dictionary of User variables
:param str receptor_file: the path for the receptor pdb
:param bool test_boot: used to initialize class without objects for
testing purpose
"""
if test_boot is False:
self.vars = vars
self.debug_mode = vars["debug_mode"]
# VINA SPECIFIC VARS
receptor_file = vars["filename_of_receptor"]
obabel_path = self.vars["obabel_path"]
number_of_processors = vars["number_of_processors"]
# docking_executable = vars["docking_executable"]
###########################
# convert Receptor from PDB to PDBQT
self.convert_receptor_pdb_files_to_pdbqt(
receptor_file, obabel_path, number_of_processors
)
self.receptor_pdbqt_file = receptor_file + "qt"
def convert_receptor_pdb_files_to_pdbqt(self, receptor_file, obabel_path,
number_of_processors):
"""
Make sure a PDB file is properly formatted for conversion to pdbqt
Inputs:
:param str receptor_file: the file path of the receptor
:param str obabel_path: file path of the obabel_path executable
:param int number_of_processors: number of processors to multithread
"""
count = 0
while not os.path.exists(receptor_file + "qt"):
count = count + 1
if count > 10000:
print(
"ERROR: I've tried 10,000 times to convert the file \""
+ receptor_file
+ '" to the PDBQT format. Aborting program...'
)
raise ValueError(
"ERROR: I've tried 10,000 times to convert the file \""
+ receptor_file
+ '" to the PDBQT format. Aborting program...'
)
# make sure the receptors have been converted to PDBQT. If not, do
# the conversion.
receptors = [receptor_file]
need_to_covert_receptor_to_pdbqt = []
for filename in receptors:
if not os.path.exists(filename + "qt"):
need_to_covert_receptor_to_pdbqt.append(filename)
# This should only be 1 receptor. If Autogrow is expanded to
# handle multiple receptor, one will need Multiprocess this. Fix
# this to 1 processor, so no overwriting issues, but could be
# expanded if we ever wanted to do multiple receptors
# create a file to run the pdbqt
for i in need_to_covert_receptor_to_pdbqt:
self.prepare_receptor_multiprocessing(obabel_path, i)
def prepare_receptor_multiprocessing(self, obabel_path, mol_filename):
"""
This prepares the receptor for multiprocessing.
Inputs:
:param str obabel_path: file path of the obabel_path executable
:param str mol_filename: the file path of the receptor
"""
# This converts PDB to PDBQT with rigid converter. The -xrp option
# preserves the atom index and prevents root and BRANCHES in the
# output file. Unfortunately these may not be the best converted pdbqt
# files. We recommend using MGLTools for converting the receptor file.
# Developer note should replace this when better option becomes
# available
print("Converting receptor PDB file to PDBQT using obabel")
command = "{} -ipdb {} -opdbqt -xrp > {}qt".format(
obabel_path, mol_filename, mol_filename
)
try:
os.system(command)
except:
raise Exception("Could not convert receptor with obabel")
if os.path.exists(mol_filename+"qt") is False:
raise Exception("Could not convert receptor with obabel")
# Run Clean up on pdbqt receptor
# obabel leaves a ROOT, ENDROOT, TORSDOF comments in file which
# need to be removed prior to docking
# We will comment them out with REMARK
# We will also add in a header
printout = "REMARK Receptor file prepared using obabel on: "
printout = printout + str(datetime.datetime.now()) + "\n"
printout = printout + "REMARK Filename is: {}\n".format(mol_filename + "qt")
printout = printout + "REMARK Prepared by running: {}\n".format(command)
with open(mol_filename+"qt", "r") as fil_path:
for line in fil_path.readlines():
if "ROOT" in line:
line = "REMARK " + line
if "TORSDOF" in line:
line = "REMARK " + line
printout = printout + line
with open(mol_filename+"qt", "w") as fil_path:
fil_path.write(printout)
###################################################
# Convert the Ligand from PDB to PDBQT DockingModel
###################################################
def convert_ligand_pdb_file_to_pdbqt(self, pdb_file):
"""
Convert the ligands of a given directory from pdb to pdbqt format
Inputs:
:param str pdb_file: the file name, a string.
Returns:
:returns: bool bool: True if it worked; False if its the gypsum param
file or if it failed to make PDBQT
:returns: str smile_name: name of the SMILES string from a pdb file
None if its the param file
"""
smile_name = self.get_smile_name_from_pdb(pdb_file)
obabel_path = self.vars["obabel_path"]
# gypsum makes 1 files labeled params which is not a valid pdb, but is
# actually a log Do not convert the params files
if "params" in pdb_file:
return False, None
# if the file already has been converted to a .pbdqt skip this file
# take all other pdb file names
if not os.path.exists(pdb_file + "qt"):
# make sure its in proper format
self.convert_pdb_to_pdbqt_acceptable_format(pdb_file)
self.prepare_ligand_processing(obabel_path, pdb_file)
if not os.path.exists(pdb_file + "qt"):
# FILE FAILED TO CONVERT TO PDBQT DELETE PDB AND RETURN FALSE
if self.debug_mode is False:
print(
"PDBQT not generated: Deleting "
+ os.path.basename(pdb_file)
+ "..."
)
# REMOVED FOR LIGANDS WHICH FAILED TO CONVERT TO PDBQT
Delete.delete_all_associated_files(pdb_file)
return False, smile_name
# In debug mode but pdbqt file does not exist
print("PDBQT not generated: " + os.path.basename(pdb_file) + "...")
return False, smile_name
return True, smile_name
# Convert Ligand from PDB to PDBQT conversion
def prepare_ligand_processing(self, obabel_path, mol_filename):
"""
This function will convert a single ligand from PDB to PDBQT using
obabel. It has 10seconds to successfully convert this. It will try to
convert the ligand up to 3 times If it fails to do so 3 times, whether
because it timed out or because obabel failed or because of an obabel
Glitch, it will stop and the ligand won't be docked.
It will print the ligand if it fails 3 times. It will also fail if the
molecule is unable to be imported into rdkit and sanitized. This is
because obabel is sensitive to issues like atoms replaced with *,
formatting errors, and improper valences. Because obabel will crash
with these issues the RDKit check is especially useful to prevent hard
crashes.
Inputs:
:param str obabel_path: file path of the obabel_path executable
:param str mol_filename: the file path of the ligand
"""
vars = self.vars
timeout_option = vars["timeout_vs_gtimeout"]
# Check that the PDB is a valid PDB file in RDKIT
try:
mol = Chem.MolFromPDBFile(mol_filename, sanitize=False, removeHs=False)
if mol is not None:
mol = MOH.check_sanitization(mol)
except:
mol = None
temp_file = "{}_temp".format(mol_filename)
if mol is not None:
count = 0
# timeout or gtimeout
command = timeout_option + " 10 {} -ipdb {} -opdbqt > {}qt".format(
obabel_path, mol_filename, mol_filename
)
while not os.path.exists(mol_filename + "qt"):
if count < 3:
# We will try up to 3 times
try:
subprocess.check_output(
command + " 2> " + temp_file, shell=True
)
except:
try:
os.system(command + " 2> " + temp_file)
except:
pass
if os.path.exists(mol_filename + "qt") is False:
printout = "Failed to convert {} times: {}".format(
count, mol_filename
)
print(printout)
count = count + 1
else:
printout = "COMPLETELY FAILED TO CONVERT: {}".format(mol_filename)
print(printout)
break
if self.debug_mode is False:
if os.path.exists(temp_file) is True:
command = "rm {}".format(temp_file)
try:
os.system(command)
except:
print("Check permissions. Could not delete {}".format(temp_file))
# Convert PDB to acceptable PDBQT file format before converting
def convert_pdb_to_pdbqt_acceptable_format(self, filename):
"""
Make sure a PDB file is properly formatted for conversion to pdbqt
Inputs:
:param str filename: the file path of the pdb file to be converted
"""
# read in the file
output_lines = []
with open(filename, "r") as f:
for line in f.readlines():
line = line.replace("\n", "")
if line[:5] == "ATOM " or line[:7] == "HETATM ":
# fix things like atom names with two letters
first = line[:11]
middle = (
line[11:17].upper().strip()
) # Need to remove whitespaces on both ends
last = line[17:]
middle_firstpart = ""
middle_lastpart = middle
for i in range(len(middle_lastpart)):
if middle_lastpart[:1].isupper() is True:
middle_firstpart = middle_firstpart + middle_lastpart[:1]
middle_lastpart = middle_lastpart[1:]
else:
break # you reached the first number
# now if there are more than two letters in
# middle_firstpart, keep just two
if len(middle_firstpart) > 2:
middle_lastpart = middle_firstpart[2:] + middle_lastpart
middle_firstpart = middle_firstpart[:2]
if middle_firstpart not in ["BR", "ZN", "FE",
"MN", "CL", "MG"]:
# so just keep the first letter for the element part
# of the atom name
middle_lastpart = middle_firstpart[1:] + middle_lastpart
middle_firstpart = middle_firstpart[:1]
middle = middle_firstpart.rjust(3) + middle_lastpart.ljust(3)
line = first + middle + last
# make sure all parts of the molecule belong to the same
# chain and resid
line = line[:17] + "LIG X 999" + line[26:]
output_lines.append(line)
else:
output_lines.append(line)
with open(filename, "w") as f:
for line in output_lines:
f.write(line + "\n")
#######################################
# Handle Failed PDBS #
#######################################
def get_smile_name_from_pdb(self, pdb_file):
"""
This will return the unique identifier name for the compound
Inputs:
:param str pdb_file: pdb file path
Returns:
:returns: str line_stripped: the name of the SMILES string
with the new lines and COMPND removed
"""
if os.path.exists(pdb_file):
with open(pdb_file, "r") as f:
for line in f.readlines():
if "COMPND" in line:
line_stripped = line.replace(
"COMPND", ""
).strip() # Need to remove whitespaces on both ends
line_stripped = line_stripped.replace(
"\n", ""
).strip() # Need to remove whitespaces on both ends
# line_stripped is now the name of the smile for this compound
else:
line_stripped = "unknown"
return line_stripped
| 14,937 | 38.834667 | 86 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/docking_class/docking_file_conversion/__init__.py |
"""
This imports all of the filters within the filter_children_classes file.
This is very important as the filters will not work if this doesn't exist.
This is a dynamic and modular way of doing these imports.
Code is taken from:
https://stackoverflow.com/questions/1057431/how-to-load-all-modules-in-a-folder
"""
from os.path import dirname, basename, isfile
import glob
modules = glob.glob(dirname(__file__)+"/*.py")
__all__ = [basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]
from . import *
| 535 | 25.8 | 92 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/execute_scoring_mol.py | """
This function handles the scoring/rescoring of docked molecules.
"""
import __future__
from autogrow.docking.docking_class.get_child_class import get_all_subclasses
# importing scoring_functions is necessary to find rescoring modules
import autogrow.docking.scoring.scoring_classes.scoring_functions
from autogrow.docking.scoring.scoring_classes.parent_scoring_class import ParentScoring
def pick_run_class_dict(scoring_choice):
"""
This will retrieve all the names of every child class of the parent class
ParentScoring
Inputs:
:param list scoring_choice: List with the User specified scoring choices
Returns:
:returns: object child_dict[scoring_choice]: the class for running the
chosen scoring method
"""
children = get_all_subclasses(ParentScoring)
child_dict = {}
for child in children:
child_name = child.__name__
child_dict[child_name] = child
return child_dict[scoring_choice]
############
############
def run_scoring_common(vars, smile_file, folder_to_search):
"""
This section runs the functions common to all scoring functions.
IF ONE INCORPORATES A NEW DOCKING OR SCORING SOFTWARE, CONFIRM THAT ITS
INPUT/OUTPUTS CONFORM TO THIS SECTION.
############## VERY IMPORTANT SECTION########################
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param str smile_file: File path for the file with the ligands for the
generation which will be a .smi file
:param str folder_to_search: a directory to search containing docked
ligands
Returns:
:returns: dict lig_dict: a dictionary where the keys are the ligand
shorthand names and the items are a list of any information about the
ligand with the fitness measure as the -1 idx in each list
"""
# Retrieve a list of all files with the proper information within
# folder_to_search
scoring_choice = vars["scoring_choice"]
scoring_class = pick_run_class_dict(scoring_choice)
# Make a dictionary of all ligands which may have been docked. This will
# be taken from the .smi file gen_n_to_convert.smi file Keys are the
# shortened name of the ligand id and the item is the SMILES string and
# the ligand id full name
smiles_dict = make_dict_of_smiles(smile_file)
# print('------------ smiles_dict', smiles_dict)
# Use a temp vars dict so you don't put mpi multiprocess info through
# itself...
temp_vars = {}
for key in list(vars.keys()):
if key == "parallelizer":
continue
temp_vars[key] = vars[key]
# Initialize the scoring class
scoring_object = scoring_class(temp_vars, smiles_dict, test_boot=False)
# Find all the files that need to be scored. Because this depends on the
# scoring function these may be different file types
files_to_score = scoring_object.find_files_to_score(folder_to_search)
# print('---------- files_to_score', files_to_score)
# Run Rescoring If applicable (All classes should have this even if its
# just returning None)
files_to_score = run_rescoring(vars, scoring_object, files_to_score)
files_to_score = [x for x in files_to_score if x is not None]
# Determine if the values from the Scoring function should be adjusted by
# the number of non-H atoms in the ligand ie) rescoring_lig_efficiency
rescore_lig_efficiency = vars["rescore_lig_efficiency"]
# create rescore_lig_efficiency object if needed
if rescore_lig_efficiency is True:
rescore_lig_efficiency_class = pick_run_class_dict("LigEfficiency")
# Initialize the scoring class
rescore_lig_efficiency_scoring_object = rescore_lig_efficiency_class(
temp_vars, smiles_dict, test_boot=False
)
else:
rescore_lig_efficiency_scoring_object = None
job_input_files_to_score = tuple(
[
tuple(
[
scoring_object,
file_path,
rescore_lig_efficiency,
rescore_lig_efficiency_scoring_object,
]
)
for file_path in files_to_score
]
)
# Format for list_of_raw_data must be [lig_id_shortname, any_details,
# fitness_score_to_use]
list_of_list_of_lig_data = vars["parallelizer"].run(
job_input_files_to_score, score_files_multithread
)
# Convert all list_of_list_of_lig_data to a searchable dictionary This
# removes redundancies from multiple conformations and any Nones. This is
# left as a dictionary because it could be an area for expansion.
lig_dict = make_lig_score_dictionary(list_of_list_of_lig_data)
# Convert Dictionary to the final list form
list_of_smiles_with_scores = []
for key in list(lig_dict.keys()):
lig_info = lig_dict[key]
if lig_info is None:
continue
lig_info = [str(x) for x in lig_info]
# Make sure every value is a string
list_of_smiles_with_scores.append(lig_info)
# Make sure every value is
return list_of_smiles_with_scores
#
def run_rescoring(vars, scoring_object, files_to_score):
"""
Run a rescoring function on docked ligands.
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param object scoring_object: the class for running the chosen scoring
method
:param list files_to_score: list of files to rescores
Returns:
:returns: list completed_rescore: a list of all ligands which passed a
scoring function.
"""
files_to_score = [x for x in files_to_score if x is not None]
# Run Rescoring If applicable (All classes should have this even if its
# just returning None)
job_input_files_to_score = tuple(
[tuple([file_path, scoring_object]) for file_path in files_to_score]
)
# Format for list_of_raw_data must be [lig_id_shortname, any_details,
# fitness_score_to_use]
results_rescore = vars["parallelizer"].run(
job_input_files_to_score, rescore_single_file
)
if len(results_rescore) == 0:
return files_to_score
if results_rescore[0] == "Not Applicable":
return files_to_score
results_rescore = [x for x in results_rescore if x is not None]
completed_rescore = [x[0] for x in results_rescore if x[1] is True]
failed_to_rescore = [x[0] for x in results_rescore if x[1] is False]
# print fails which made it through the try statement but failed to
# produce an output file.
if len(failed_to_rescore) != 0:
print("The following files failed to be rescored: ")
print(failed_to_rescore)
else:
print("All rescoring attempts were successful")
pass
if len(completed_rescore) == 0:
printout = (
"All Rescoring attempts failed to create output files. No data to report."
)
raise Exception(printout)
print("Finished rescoring")
print("######################\n")
return completed_rescore
#
def rescore_single_file(file_path, scoring_object):
"""
Run scoring_object.run_rescoring through this function so multithread
doesn't break.
Inputs:
:param str file_path: Path to a vina output file to be rescored
:param object scoring_object: object that rescores such as an NN1 or NN2
class object
Returns:
:returns: list results of a rescoring function: [file_path, it_rescored]
[PATH, True] means it passed [PATH, False] means it failed a results of
all NN1 files
"""
return scoring_object.run_rescoring(file_path)
def make_lig_score_dictionary(list_of_list_of_lig_data):
"""
Given a list of ligands with the scoring data make a dictionary.
This will also reduce multiple Conformers and Poses down to the top score.
# REQUIRES THE BEST SCORE TO BE MOST NEGATIVE
Inputs:
:param list list_of_list_of_lig_data: a list of lists containing all info
on ligands after scoring [[SMILES, id, Shortid, additional_info...,
fitness_score], [SMILES, id, Shortid, additional_info..., fitness_score]]
Returns:
:returns: dict lig_dict: a dictionary containing the information of all
ligands from list_of_list_of_lig_data this dictionary has the short_id as
the key and the item is a list from list_of_list_of_lig_data for that
ligand. Because there can be multiple files for multiple conformations of
a given ligand, this will reduce down multiple confirmations to a single
ligand with the most negative fitness score.
"""
lig_dict = {}
for lig in list_of_list_of_lig_data:
if lig is None:
continue
lig_short_id = str(lig[2])
fitness_score = float(lig[-1])
# Handle if Multiple Conformers and Poses
if lig_short_id in lig_dict.keys():
if float(lig_dict[lig_short_id][-1]) > float(fitness_score):
lig_dict[lig_short_id] = lig
else:
continue
else:
lig_dict[lig_short_id] = lig
return lig_dict
def score_files_multithread(scoring_object, file_path, rescore_lig_efficiency,
lig_efficiency_scoring_object):
"""
Run the scoring of a single molecule.
Inputs:
:param object scoring_object: the class for running the chosen scoring
method
:param str file_path: the path to the file to be scored
:param bol rescore_lig_efficiency: if True than the final score will be
adjusted to the ligand efficieny score, otherwise it will remain the
output of the scoring_object
:param object lig_efficiency_scoring_object: the class for running the
rescoring by ligand efficieny
Returns:
:returns: list list_of_lig_data: information about the scored ligand.
Score is last index (ie. [SMILES, lig_id, lig_id_shortname, any_details,
fitness_score_to_use] )
"""
list_of_lig_data = scoring_object.run_scoring(file_path)
if rescore_lig_efficiency is True:
list_of_lig_data = lig_efficiency_scoring_object.get_lig_efficiency_rescore_from_a_file(
file_path, list_of_lig_data
)
return list_of_lig_data
############
############
def make_dict_of_smiles(smile_file):
"""
This will take a .smi file and make a dictionary with all of the info
about the smiles. This list won't have scores yet but will have all of the
string info. This will be used later to search through.
The keys will be the shorthand id for each ligand.
Inputs:
:param str smile_file: the path for the receptor pdb
Returns:
:return dict smiles_dict: a list of ligand info before docking
"""
smiles_dict = {}
# load smile file and convert to list with index
with open(str(smile_file), "r") as smi:
# index for this for-loop is zero based indexing while ligand naming
# index is one-based-indexing so we will use index = index+1
for index, line in enumerate(smi.readlines()):
# split_line = line.replace("\n", "").split("\t")
# ligand_name = line.replace("\n", "").split("\t")[1]
split_line = line.replace("\n", "").split()
ligand_name = line.replace("\n", "").split()[1]
# ligand_name should be something like: ['CCC', '(ZINC123+ZINC345)Gen_0_Cross_99571'] or ['CCC', 'ZINC123']
if len(ligand_name.split(")")) == 2:
lig_name_short = ligand_name.split(")")[1]
elif len(ligand_name.split(")")) == 1:
lig_name_short = ligand_name
smiles_dict[lig_name_short] = split_line
return smiles_dict
| 11,825 | 33.782353 | 119 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/scoring_classes/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/scoring_classes/parent_scoring_class.py | """
This script holds the parent class for scoring/rescoring.
This is used as the basis for all scoring/rescoring classes.
"""
import __future__
class ParentScoring(object):
"""
This is a script containing all of the scoring functions.
"""
def get_name(self):
"""
Returns the current class name.
Returns:
:returns: str self.__class__.__name__: the current class name.
"""
return self.__class__.__name__
def run_scoring(self, input_string):
"""
run_scoring is needs to be implemented in each class.
Inputs:
:param str input_string: A string to raise an exception
"""
raise NotImplementedError("run_scoring() not implemented")
| 748 | 23.966667 | 70 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/scoring_classes/scoring_functions/nn1.py | """
This script contains the class NN1 that rescores Vina type docking
using the program NNScore1.
"""
import __future__
import glob
import os
import sys
from autogrow.docking.scoring.scoring_classes.parent_scoring_class import ParentScoring
from autogrow.docking.scoring.scoring_classes.scoring_functions.vina import VINA
class NN1(VINA):
"""
This will Score a given ligand for its binding affinity based on VINA or
QuickVina02 type docking.
Inputs:
:param class ParentFilter: a parent class to initialize off of.
"""
def __init__(self, vars=None, smiles_dict=None, test_boot=True):
"""
This will take vars and a list of smiles.
Inputs:
:param dict vars: Dictionary of User variables
:param dict smiles_dict: a dict of ligand info of SMILES, IDS, and
short ID
:param bool test_boot: used to initialize class without objects for
testing purpose
"""
if test_boot is False:
self.vars = vars
self.smiles_dict = smiles_dict
print("")
print("######################")
print("Running NN1 rescoring on vina files")
#######################
# Executed by the Execute_Scoring.py script
#######################
def find_files_to_score(self, file_path):
"""
Find all files of the appropriate file format within the dir. For this
class its .pdbqt.vina files.
ALL SCORING FUNCTIONS MUST HAVE THIS FUNCTION.
Inputs:
:param str file_path: the path to the file to be scored
Returns:
:returns: list list_of_NN1_files: list of all files to be scored
within the dir
"""
self.file_path = file_path
list_of_docked_files = []
list_of_docked_files = glob.glob(file_path + "*.pdbqt.vina")
return list_of_docked_files
def run_rescoring(self, vina_output_file):
"""
Run the NN1 scoring on all of these files. Return a list of a rescored
file with NN1 ie *.pdbqt.vina.nn1
Inputs:
:param str vina_output_file: Path to a vina output file to be rescored
Returns:
:returns: list results of the rescoring function: [file_path,
it_rescored]. [PATH, True] means it passed. [PATH, False] means it
failed a results of all NN1 files.
"""
result_of_rescore = run_nn_rescoring(self.vars, vina_output_file)
return result_of_rescore
def run_scoring(self, file_path):
"""
Get all relevant scoring info and return as a list
This is required for all Scoring Functions. Additional manipulations
may go here but there are none for this script..
Inputs:
:param str file_path: the path to the file to be scored
Returns:
:returns: list list_of_lig_data: information about the scored ligand.
Score is last index (ie. [lig_id_shortname, any_details,
fitness_score_to_use] )
"""
if os.path.exists(file_path):
lig_info = self.get_score_from_a_file(file_path)
return lig_info
# file_path does not exist
return None
def get_score_from_a_file(self, file_path):
"""
Make a list of a ligands information including its docking score.
Because a higher score is better for both NNScore functions, but
AutoGrow4 selects based on most negative score, we multiple each NNScore
value by -1.0. This ensures that the best score is the most negative
score.
Inputs:
:param str file_path: the path to the file to be scored
Returns:
:returns: list lig_info: a list of the ligands short_id_name and the
docking score from the best pose.
"""
if ".nn1" not in file_path:
if ".vina" in file_path:
file_path = file_path + ".nn1"
if os.path.exists(file_path) is False:
return None
else:
return None
if os.path.exists(file_path) is False:
return None
# grab the index of the ligand for the score
basefile = os.path.basename(file_path)
ligand_pose = basefile.split(".pdbqt.vina.nn1")[0]
basefile_split = basefile.split("__")
ligand_short_name = basefile_split[0]
score = None
with open(file_path, "r") as f:
for line in f.readlines():
if "Best score: " in line:
try:
tmp = line.split(" ")
# because for both NNScore functions, a higher score is better
# multiply score to be negative
temp_score = tmp[2].replace(",", "")
temp_score = float(temp_score) * float(-1.0)
except:
continue
if score is None:
score = temp_score
else:
if score > temp_score:
score = temp_score
else:
continue
# Pose info not used here but could be useful in some
# context
# tmp = line.replace(")",'')
# tmp = tmp.split(', MODEL ')
# vina_pose_to_use = int(tmp[1])
if score is None:
# This file lacks a pose to use
return None
lig_info = [ligand_short_name, ligand_pose, score]
# Obtain additional file info
lig_info = self.merge_smile_info_w_affinity_info(lig_info)
if lig_info is None:
return None
lig_info = [str(x) for x in lig_info]
return lig_info
###Outside class for multithreading
# Run NN1 rescoring
def run_nn_rescoring(vars, vina_output_file):
"""
This will run NN1 on all of the vina files in the list. This is outside
the class for multifunction purposes
Returns A list containing the file name as item 1 and whether it passed as
item 2. [PATH, True] means it passed. [PATH, False] means it failed a
results of all NN1 files.
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param str vina_output_file: Path to a vina output file to be rescored
Returns:
:returns: list results of the rescoring function: [file_path,
it_rescored]. [PATH, True] means it passed. [PATH, False] means it failed
a results of all NN1 files.
"""
if vina_output_file is None:
return None
# Unpackage vars
receptor = vars["filename_of_receptor"] + "qt"
nn1_executable = vars["nn1_script"]
networks_dir = (
os.path.dirname(vars["nn1_script"])
+ os.sep
+ "networks"
+ os.sep
+ "top_3_networks"
+ os.sep
)
nn1_output = vina_output_file + ".nn1"
# sys.executable is the path to python executable
torun = (
sys.executable + " "
+ nn1_executable
+ " -receptor "
+ receptor
+ " -vina_output "
+ vina_output_file
+ " -networks_dir "
+ networks_dir
+ " > "
+ nn1_output
)
# A list containing the file name as item 1 and whether it passed as item
# 2
results = execute_nn_scoring(torun, nn1_output)
# Will be None if it passed. A list containing the file name as item 1 and
# whether it passed as item 2. [PATH, True] means it passed. [PATH, False]
# means it failed.
return results
def execute_nn_scoring(command, file_path):
"""
Run an individual NN scoring function.
returns A list containing the file name as item 1 and whether it passed as
item 2. [PATH, True] means it passed. [PATH, False] means it failed.
Inputs:
:param str command: the rescoring bash style command to execute
:param str file_path: Path to a vina output file to be rescored
Returns:
:returns: list results of the rescoring function: [file_path,
it_rescored]. [PATH, True] means it passed. [PATH, False] means it failed
a results of all NN1 files.
"""
try:
os.system(command)
it_rescored = confirm_file_has_scoring(file_path)
except:
return [file_path, False]
return [file_path, it_rescored]
def confirm_file_has_scoring(file_path):
"""
Check the file has a rescore value
Inputs:
:param str file_path: Path to a vina output file to be rescored
Returns:
:returns: bol has_scoring: True if has score;
False if no score found
"""
if os.path.exists(file_path) is False:
return False
with open(file_path, "r") as f:
has_scoring = False
for line in f.readlines():
if "Best Score:" in line or "Best score:":
has_scoring = True
return has_scoring
return has_scoring
| 9,094 | 29.726351 | 87 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/scoring_classes/scoring_functions/nn2.py | """
This script contains the class NN2 that rescores Vina type docking
using the program NNScore2.
"""
import __future__
import glob
import os
import sys
from autogrow.docking.scoring.scoring_classes.parent_scoring_class import ParentScoring
from autogrow.docking.scoring.scoring_classes.scoring_functions.vina import VINA
class NN2(VINA):
"""
This will Score a given ligand for its binding affinity based on VINA or
QuickVina02 type docking.
Inputs:
:param class ParentFilter: a parent class to initialize off of.
"""
def __init__(self, vars=None, smiles_dict=None, test_boot=True):
"""
This will take vars and a list of smiles.
Inputs:
:param dict vars: Dictionary of User variables
:param dict smiles_dict: a dict of ligand info of SMILES, IDS, and
short ID
:param bool test_boot: used to initialize class without objects for
testing purpose
"""
if test_boot is False:
self.vars = vars
self.smiles_dict = smiles_dict
print("")
print("######################")
print("Running NN2 rescoring on vina files")
#######################
# Executed by the Execute_Scoring.py script
#######################
def find_files_to_score(self, file_path):
"""
Find all files of the appropriate file format within the dir. For this
class its .pdbqt.vina files.
ALL SCORING FUNCTIONS MUST HAVE THIS FUNCTION.
Inputs:
:param str file_path: the path to the file to be scored
Returns:
:returns: list list_of_NN2_files: list of all files to be scored
within the dir
"""
self.file_path = file_path
list_of_docked_files = []
list_of_docked_files = glob.glob(file_path + "*.pdbqt.vina")
return list_of_docked_files
def run_rescoring(self, vina_output_file):
"""
Run the NN2 scoring on all of these files. Return a list of a rescored
file with NN2 ie *.pdbqt.vina.nn2
Inputs:
:param str vina_output_file: Path to a vina output file to be rescored
Returns:
:returns: list results of the rescoring function: [file_path,
it_rescored] [PATH, True] means it passed [PATH, False] means it
failed a results of all NN2 files
"""
result_of_rescore = run_nn_rescoring(self.vars, vina_output_file)
return result_of_rescore
def run_scoring(self, file_path):
"""
Get all relevant scoring info and return as a list
This is required for all Scoring Functions. Additional manipulations
may go here but there are none for this script..
Inputs:
:param str file_path: the path to the file to be scored
Returns:
:returns: list list_of_lig_data: information about the scored ligand.
Score is last index (ie. [lig_id_shortname, any_details,
fitness_score_to_use] )
"""
if os.path.exists(file_path):
lig_info = self.get_score_from_a_file(file_path)
return lig_info
# file_path does not exist
return None
def get_score_from_a_file(self, file_path):
"""
Make a list of a ligands information including its docking score.
Because a higher score is better for both NNScore functions, but
AutoGrow4 selects based on most negative score, we multiple each NNScore
value by -1.0. This ensures that the best score is the most negative
score.
Inputs:
:param str file_path: the path to the file to be scored
Returns:
:returns: list lig_info: a list of the ligands short_id_name and the
docking score from the best pose.
"""
if ".nn2" not in file_path:
if ".vina" in file_path:
file_path = file_path + ".nn2"
if os.path.exists(file_path) is False:
return None
else:
return None
if os.path.exists(file_path) is False:
return None
# grab the index of the ligand for the score
basefile = os.path.basename(file_path)
ligand_pose = basefile.split(".pdbqt.vina.nn2")[0]
basefile_split = basefile.split("__")
ligand_short_name = basefile_split[0]
score = None
with open(file_path, "r") as f:
while True:
line = f.readline()
if len(line) == 0:
break
if "Best Score:" in line:
try:
score = line.split(", ")[1]
score = float(score)
except:
continue
# because for both NNScore functions, a higher score is better
# multiply score to be negative
score = score * -1.0
break
elif (
"When the poses were ranked by the best of the 20 network scores"
in line
):
line = f.readline()
line = f.readline()
try:
tmp = line.split(" ")
score = float(tmp[0])
except:
continue
score = float(tmp[0])
# Make it a negative number since NN2 & NN2 a higher
# number is best But autogrow 4.0.3 uses the most negative
# number is best.
score = score * -1.0
break
if score is None:
# This file lacks a pose to use
return None
lig_info = [ligand_short_name, ligand_pose, score]
# Obtain additional file
lig_info = self.merge_smile_info_w_affinity_info(lig_info)
if lig_info is None:
return None
lig_info = [str(x) for x in lig_info]
return lig_info
###Outside class for multithreading
# Run NN2 rescoring
def run_nn_rescoring(vars, vina_output_file):
"""
This will run NN2 on all of the vina files in the list. This is outside
the class for multifunction purposes
Returns A list containing the file name as item 1 and whether it passed as
item 2. [PATH, True] means it passed. [PATH, False] means it failed a
results of all NN2 files.
Inputs:
:param dict vars: User variables which will govern how the programs runs
:param str vina_output_file: Path to a vina output file to be rescored
Returns:
:returns: list results of the rescoring function: [file_path,
it_rescored]. [PATH, True] means it passed. [PATH, False] means it failed
a results of all NN2 files.
"""
# Unpackage vars
receptor = vars["filename_of_receptor"] + "qt"
nn2_executable = vars["nn2_script"]
docking_executable = vars["docking_executable"]
if vina_output_file is None:
return None
nn2_output = str(vina_output_file) + ".nn2"
lig = vina_output_file.replace(".vina", "")
torun = (
sys.executable
+ " "
+ nn2_executable
+ " -receptor "
+ receptor
+ " -ligand "
+ lig
+ " -vina_executable "
+ docking_executable
+ " > "
+ str(nn2_output)
)
# A list containing the file name as item 1 and whether it passed as item
# 2
results = execute_nn_scoring(torun, nn2_output)
# Will be None if it passed. A list containing the file name as item 1 and
# whether it passed as item 2. [PATH, True] means it passed. [PATH, False]
# means it failed.
return results
def execute_nn_scoring(command, file_path):
"""
Run an individual NN scoring function.
returns None if it worked. If it failes to rescore it returns the NN2
output file which failed to be produced.
Inputs:
:param str command: the rescoring bash style command to execute
:param str file_path: Path to a vina output file to be rescored
Returns:
:returns: list results of the rescoring function: [file_path,
it_rescored]. [PATH, True] means it passed. [PATH, False] means it failed
a results of all NN2 files.
"""
try:
os.system(command)
it_rescored = confirm_file_has_scoring(file_path)
except:
return [file_path, False]
return [file_path, it_rescored]
def confirm_file_has_scoring(file_path):
"""
Check the file has a rescore value
Inputs:
:param str file_path: Path to a vina output file to be rescored
Returns:
:returns: bol has_scoring: True if has score;
False if no score found
"""
if os.path.exists(file_path) is False:
return False
with open(file_path, "r") as f:
has_scoring = False
for line in f.readlines():
if "Best Score:" in line or "Best score:":
has_scoring = True
return has_scoring
return has_scoring
| 9,200 | 29.366337 | 89 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/scoring_classes/scoring_functions/__init__.py |
"""
This imports all of the filters within the filter_children_classes file.
This is very important as the filters will not work if this doesn't exist.
This is a dynamic and modular way of doing these imports.
Code is taken from:
https://stackoverflow.com/questions/1057431/how-to-load-all-modules-in-a-folder
"""
from os.path import dirname, basename, isfile
import glob
modules = glob.glob(dirname(__file__)+"/*.py")
__all__ = [basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]
from . import *
| 535 | 25.8 | 92 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/scoring_classes/scoring_functions/vina.py | """
This script contains the class VINA.
This is used to score Vina type docking such as QuickVina2 and Vina
"""
import __future__
import glob
import os
from autogrow.docking.scoring.scoring_classes.parent_scoring_class import ParentScoring
class VINA(ParentScoring):
"""
This will Score a given ligand for its binding affinity based on VINA or
QuickVina02 type docking.
Inputs:
:param class ParentFilter: a parent class to initialize off of.
"""
def __init__(self, vars=None, smiles_dict=None, test_boot=True):
"""
This will take vars and a list of smiles.
Inputs:
:param dict vars: Dictionary of User variables
:param dict smiles_dict: a dict of ligand info of SMILES, IDS, and
short ID
:param bool test_boot: used to initialize class without objects for
testing purpose
"""
if test_boot is False:
self.vars = vars
self.smiles_dict = smiles_dict
#######################
# Executed by the Execute_Scoring.py script
#######################
def find_files_to_score(self, file_path):
"""
Find all files of the appropriate file format within the dir. For this
class its .pdbqt.vina files.
ALL SCORING FUNCTIONS MUST HAVE THIS FUNCTION.
Inputs:
:param str file_path: the path to the file to be scored
Returns:
:returns: list list_of_files: list of all files to be scored within
the dir
"""
self.file_path = file_path
list_of_files = []
list_of_files = glob.glob(file_path + "*.pdbqt.vina")
return list_of_files
def run_rescoring(self, vina_output_file):
"""
This is not applicable but is kept because the other rescoring
functions require this function.
Inputs:
:param str vina_output_file: Path to a vina output file to be rescored
Returns:
:returns: str "Not Applicable": Because this doesn't need to rescore
the docking results
"""
return "Not Applicable"
def run_scoring(self, file_path):
"""
Get all relevant scoring info and return as a list
This is required for all Scoring Functions. Additional manipulations
may go here but there are none for this script..
Inputs:
:param str file_path: the path to the file to be scored
Returns:
:returns: list list_of_lig_data: information about the scored ligand.
Score is last index (ie. [lig_id_shortname, any_details,
fitness_score_to_use] )
"""
lig_info = self.get_score_from_a_file(file_path)
return lig_info
def get_score_from_a_file(self, file_path):
"""
Make a list of a ligands information including its docking score.
Inputs:
:param str file_path: the path to the file to be scored
Returns:
:returns: list lig_info: a list containing all info from
self.smiles_dict for a given ligand and the ligands short_id_name and
the docking score from the best pose.
"""
# grab the index of the ligand for the score
basefile = os.path.basename(file_path)
basefile_strip = basefile.replace(".pdbqt.vina", "")
basefile_split = basefile.split("__")
ligand_short_name = basefile_split[0]
affinity = None
with open(file_path, "r") as f:
for line in f.readlines():
if "REMARK VINA" in line:
line_stripped = line.replace("REMARK VINA RESULT:", "").replace(
"\n", ""
)
line_split = line_stripped.split()
if affinity is None:
affinity = float(line_split[0])
else:
if affinity > float(line_split[0]):
affinity = float(line_split[0])
if affinity is None:
# This file lacks a pose to use
return None
# Obtain additional file
lig_info = [ligand_short_name, basefile_strip, affinity]
lig_info = self.merge_smile_info_w_affinity_info(lig_info)
if lig_info is None:
return None
lig_info = [str(x) for x in lig_info]
return lig_info
def merge_smile_info_w_affinity_info(self, lig_info):
"""
From the info in self.smiles_dict get that info and merge that with
the affinity info
This will also replace the original SMILES string with that of the
SMILES string in the PDB which conservers stereoChem
Inputs:
:param list lig_info: list containing [ligand_short_name, affinity]
Returns:
:returns: list ligand_full_info: a list containing all info from
self.smiles_dict for a given ligand and the ligands short_id_name and
the docking score from the best pose. returns None if
ligand_short_name isn't in the self.smiles_dict which should never
happen
"""
ligand_short_name = lig_info[0]
# Get SMILES String of PDB
pdb_path = self.file_path + lig_info[1] + ".pdb"
if os.path.exists(pdb_path):
new_smiles_string = None
with open(pdb_path, "r") as f:
for line in f.readlines():
if "REMARK Final SMILES string: " in line:
new_smiles_string = line.replace(
"REMARK Final SMILES string: ", ""
).replace("\n", "")
break
if new_smiles_string is None:
# If the REMARK SECTION IS NOT THERE raise except. Avoid this
# if possible as rdkit can missinterpret bonds because pdbs
# dont specify bond types
raise Exception(
"Could not get SMILES string from PDB file: " + pdb_path
)
else:
return None
if ligand_short_name in list(self.smiles_dict.keys()):
ligand_full_info = [
new_smiles_string,
self.smiles_dict[ligand_short_name][1],
]
ligand_full_info.extend(lig_info)
# Convert all to strings
ligand_full_info = [str(x) for x in ligand_full_info]
return ligand_full_info
# Return None because lig name isn't in dictionary.
# This is precautionary to prevent key errors later.
# This should not occur
return None
| 6,699 | 31.682927 | 87 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/scoring_classes/scoring_functions/lig_efficiency.py | """
This script contains the class LigEfficiency.
This is used to rescore a fitness metric by the number of non-hydrogen atoms.
"""
import __future__
import rdkit
import rdkit.Chem as Chem
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
from autogrow.docking.scoring.scoring_classes.parent_scoring_class import ParentScoring
from autogrow.docking.scoring.scoring_classes.scoring_functions.vina import VINA
class LigEfficiency(VINA):
"""
This will Score a given ligand for its binding affinity based on VINA or
QuickVina02 type docking.
This inherits many functions from the vina scoring function. The only
difference is that this scoring function uses the ligand efficiency
instead of docking score. ligand efficiency is the docking score divided
by the number of heavy atoms (non-hydrogens)
Inputs:
:param class VINA: the VINA scoring function which this class inherits
from.
"""
def __init__(self, vars=None, smiles_dict=None, test_boot=True):
"""
This will take vars and a list of smiles.
Inputs:
:param dict vars: Dictionary of User variables
:param dict smiles_dict: a dict of ligand info of SMILES, IDS, and
short ID
:param bool test_boot: used to initialize class without objects for
testing purpose
"""
if test_boot is False:
self.vars = vars
self.smiles_dict = smiles_dict
def get_lig_efficiency_rescore_from_a_file(self, file_path, lig_info):
"""
This function will simply add a ligand efficiency score to the end of
the lig_info list and return said list.
The last value of the lig_info list must be a float.
Inputs:
:param str file_path: the path to the file to be scored
:param list lig_info: a list of the ligands short_id_name and
the fitness score from the best pose.
This would have been generated by the primary method of
scoring/rescoring
Returns:
:returns: list lig_info: a list of the ligands short_id_name and
the docking score from the best pose.
"""
# For saftey remove Nones and empty lists
if type(lig_info) is not list:
return None
if len(lig_info) == 0:
return None
lig_info = append_lig_effeciency(lig_info)
if lig_info is None:
return None
lig_info = [str(x) for x in lig_info]
return lig_info
# These Functions are placed outside the class for multithreading reasons.
# Multithreading doesn't like being executed within the class.
def get_number_heavy_atoms(smiles_str):
"""
Get the number of non Hydrogens in a SMILE
Inputs:
:param str smiles_str: a str representing a molecule
Returns:
:returns: int num_heavy_atoms: a int of the count of heavy atoms
"""
if smiles_str is None:
return None
# easiest nearly everything should get through
try:
mol = Chem.MolFromSmiles(smiles_str, sanitize=False)
except:
mol = None
if mol is None:
return None
atom_list = mol.GetAtoms()
num_heavy_atoms = 0
for atom in atom_list:
if atom.GetAtomicNum() != 1:
num_heavy_atoms = num_heavy_atoms + 1
return num_heavy_atoms
def append_lig_effeciency(list_of_lig_info):
"""
Determine the ligand efficiency and append it to the end of a list which
has the ligand information.
Inputs:
:param list list_of_lig_info: a list containing ligand informations with
idx=0 as the SMILES str and idx=-1 is the docking score
Returns:
:returns: list list_of_lig_info: the same list as list_of_lig_info, but
with each sublist now having the ligand efficiency score appended to the
end.
"""
if type(list_of_lig_info) is None:
return None
elif type(list_of_lig_info) == list:
if None in list_of_lig_info:
return None
# Unpack ligand info
lig_smiles_str = str(list_of_lig_info[0])
affinity = float(list_of_lig_info[-1])
# Get num of heavy atoms
heavy_atom_count = get_number_heavy_atoms(lig_smiles_str)
if heavy_atom_count is None or heavy_atom_count == 0:
return None
# Convert to Lig efficiency (aka affinity/heavy_atom_count )
lig_efficieny = float(affinity) / float(heavy_atom_count)
# Append lig_efficiency to list_of_lig_info
list_of_lig_info.append(str(lig_efficieny))
return list_of_lig_info
| 4,609 | 28.741935 | 87 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/nn_score_exe/__init__.py | 2 | 0.5 | 1 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/nn_score_exe/nnscore1/NNScore.py | # If you use NNScore in your research, please cite the following reference:
# NNScore: A Neural-Network-Based Scoring Function for the Characterization of
# Protein-Ligand Complexes. Jacob D. Durrant, J. Andrew McCammon. Journal of
# Chemical Information and Modeling, 2010, 50 (10), pp 1865-1871.
import __future__
import sys
import math
import time
import os
import random
######################################## Variables to Modify if AutoDock Output Files are to be Used ##############################################################################
mglenv = '' # If you need to use source to set environmental variables, put the filename of the file to source here.
prepare_ligand4_location = '/net/linux/pkg/autodocktools-1.5.1/MGLTools-1.5.0/MGLToolsPckgs/AutoDockTools/Utilities24/prepare_ligand4.py' # the location of prepare_ligand4.py
###################################################################################################################################################################################
program_info = "NNScore is based in part on ffnet, coded by Marek Wojciechowski. \nIt is distributed under the GNU General Public License, version 3. \nSee gpl-3.0.txt for more details."
version = '1.1'
'''Basic Feed Forward Neural Network functionality.'''
class FFNet:
'''* Network weights '''
weights = []
'''* Connection array '''
conec = [] # [][]
'''* List of input nodes '''
inno = []
'''* List of output nodes '''
outno = []
'''* Input normalization parameters '''
eni = [] # [][]
'''* Output denormalization parameters '''
deo = [] # [][]
'''* Network size '''
unitCount = 0
def findMax2d(self, table):
max = -sys.maxsize - 1
for row in table:
for value in row:
if value > max:
max = value
return max
'''Sigmoid transfer function'''
def sigmoid(self, x):
return 1.0 / (1.0 + math.exp(-x));
def file_to_double_array(self, filename):
weights = []
conec = []
inno = []
outno = []
eni = []
deo = []
text_file = open(filename, "r")
lines = text_file.readlines()
for line in lines:
if line[:8] == "WEIGHTS:": weights.append(float(line[8:]))
if line[:6] == "CONEC:":
pair = line[6:]
pair = pair.split(" ")
pair[0] = int(pair[0])
pair[1] = int(pair[1])
conec.append(pair)
if line[:5] == "INNO:": inno.append(int(line[5:]))
if line[:6] == "OUTNO:": outno.append(int(line[6:]))
if line[:4] == "ENI:":
pair = line[4:]
pair = pair.split(" ")
pair[0] = float(pair[0])
pair[1] = float(pair[1])
eni.append(pair)
if line[:4] == "DEO:":
pair = line[4:]
pair = pair.split(" ")
pair[0] = float(pair[0])
pair[1] = float(pair[1])
deo.append(pair)
text_file.close()
output = [weights, conec, inno, outno, eni, deo]
return output
'''def file_to_int_array(self, filename):
ints = []
text_file = open(filename, "r")
lines = text_file.readlines()
for line in lines:
ints.append(int(line))
text_file.close()
return ints'''
# def __init__(self, weights, conec, inno, outno, eni, deo):
def __init__(self, filename):
# first, load the weights into a double array
arrays = self.file_to_double_array(filename)
weights = arrays[0]
conec = arrays[1]
inno = arrays[2]
outno = arrays[3]
eni = arrays[4]
deo = arrays[5]
#
prev_target = conec[0][1]
# Check that connections are properly ordered
for connection in conec:
target = connection[1]
prev_target = target
self.weights = weights
self.conec = conec
self.inno = inno
self.outno = outno
self.eni = eni
self.deo = deo
self.unitCount = self.findMax2d(conec) + 1
'''*
* Normalizes inputs and sets network status for propagation.
*
* @param input
* @return units - network state before propagation
'''
def setInput(self, input):
# double[] units = new double[unitCount];
units = [0 for i in range(self.unitCount)]
k = 0
while k < len(self.inno):
units[self.inno[k]] = self.eni[k][0] * input[k] + self.eni[k][1]
k = k + 1
return units
'''*
* Gets network state with input already set and calculates all activations.
* Identity input and sigmoid activation function for other units is assumed.
'''
def prop(self, units):
''' Connections are arranged so, that inputs to one node are computed together '''
ctrg = self.conec[0][1]
i = 0
while i < len(self.conec):
src = self.conec[i][0]
trg = self.conec[i][1]
# If next target, apply sigmoid
if trg != ctrg:
units[ctrg] = self.sigmoid(units[ctrg])
ctrg = trg
units[ctrg] = 0
if src == -1: # bias
units[ctrg] = units[ctrg] + self.weights[i]
else:
units[ctrg] = units[ctrg] + units[src] * self.weights[i]
i = i + 1
units[ctrg] = self.sigmoid(units[ctrg])
'''*
* Gets output from network state and denormalizes it
'''
def getOutput(self, units):
# double[] output = new double[outno.length];
output = [0 for i in range(len(self.outno))]
k = 0
while k < len(self.outno):
output[k] = self.deo[k][0] * units[self.outno[k]] + self.deo[k][1];
k = k + 1
return output;
'''*
* Calls the network
*
* @param input Input vector
* @return Output vector
'''
def call(self, input):
units = self.setInput(input);
self.prop(units)
return self.getOutput(units)
start_time = time.time()
def therange(start, end, step):
arange = []
delta = end - start
numsteps = delta / step + 1
for i in range(0, int(numsteps)):
if start + i * step<= end:
arange.append(start + i * step)
return arange
def format_num(num):
astr = "%.3f" % num
if len(astr) < 7:
astr = ' ' + astr
return astr
def convert_atomname_to_element(atomname):
element = atomname
element = element.replace("0","")
element = element.replace("1","")
element = element.replace("2","")
element = element.replace("3","")
element = element.replace("4","")
element = element.replace("5","")
element = element.replace("6","")
element = element.replace("7","")
element = element.replace("8","")
element = element.replace("9","")
element = element[:1]
return element
def get_vdw(element):
ans = 1.0 # the default
if element == "H":
ans = 1.2
if element == "C":
ans = 1.7
if element == "N":
ans = 1.55
if element == "O":
ans = 1.52
if element == "F":
ans = 1.47
if element == "P":
ans = 1.8
if element == "S":
ans = 1.8
return ans
class point:
x = 99999.0
y = 99999.0
z = 99999.0
def __init__ (self, x, y ,z):
self.x = x
self.y = y
self.z = z
def print_coors(self):
print(str(self.x)+"\t"+str(self.y)+"\t"+str(self.z))
def snap(self,reso): # snap the point to a grid
self.x = round(self.x / reso) * reso
self.y = round(self.y / reso) * reso
self.z = round(self.z / reso) * reso
def dist_to(self,apoint):
return math.sqrt(math.pow(self.x - apoint.x,2) + math.pow(self.y - apoint.y,2) + math.pow(self.z - apoint.z,2))
def description(self):
return str(self.x) + " " + str(self.y) + " " + str(self.z)
class region:
def __init__(self):
self.center = point(9999.9, 9999.9, 9999.9)
self.radius = 9999.9 # in case the region is a sphere
self.box_dimen = point(9999.9, 9999.9, 9999.9) # in case the region is a box
self.region_type = "SPHERE" # could also be BOX
def volume(self):
vol = 0.0
if self.region_type == "SPHERE":
vol = (4.0 / 3.0) * math.pi * math.pow(self.radius,3)
elif self.region_type == "BOX":
vol = self.box_dimen.x * self.box_dimen.y * self.box_dimen.z
return vol
def point_in_region(self, point, padding):
response = False
if self.region_type == "SPHERE":
dist = math.sqrt(math.pow(point.x-self.center.x,2) + math.pow(point.y-self.center.y,2) + math.pow(point.z-self.center.z,2))
if dist<= self.radius + padding:
response = True
elif self.region_type == "BOX":
x_min = self.center.x - self.box_dimen.x/2 - padding
x_max = self.center.x + self.box_dimen.x/2 + padding
y_min = self.center.y - self.box_dimen.y/2 - padding
y_max = self.center.y + self.box_dimen.y/2 + padding
z_min = self.center.z - self.box_dimen.z/2 - padding
z_max = self.center.z + self.box_dimen.z/2 + padding
if point.x>= x_min and point.x<= x_max and point.y>= y_min and point.y<= y_max and point.z>= z_min and point.z<= z_max:
response = True
return response
def print_out(self):
self.center.print_coors()
print(self.radius)
self.box_dimen.print_coors()
print(self.region_type)
def points_set(self, reso):
points = []
if self.region_type == "BOX":
# self.center = point(9999.9, 9999.9, 9999.9)
# self.box_dimen
for x in therange(self.center.x - self.box_dimen.x / 2,self.center.x + self.box_dimen.x / 2, reso):
for y in therange(self.center.y - self.box_dimen.y / 2, self.center.y + self.box_dimen.y / 2, reso):
for z in therange(self.center.z - self.box_dimen.z / 2,self.center.z + self.box_dimen.z / 2, reso):
thepoint = point(x,y,z)
thepoint.snap(reso)
# thepoint.print_coors()
points.append(thepoint)
elif self.region_type == "SPHERE":
for x in therange(self.center.x - self.radius,self.center.x + self.radius, reso):
for y in therange(self.center.y - self.radius, self.center.y + self.radius, reso):
for z in therange(self.center.z - self.radius,self.center.z + self.radius, reso):
thepoint = point(x,y,z)
thepoint.snap(reso)
if self.point_in_region(thepoint,0): # padding always 0 for inclusion objects
points.append(thepoint)
return points
class atom:
def __init__ (self):
self.atomname = ""
self.residue = ""
self.coordinates = point(99999, 99999, 99999)
self.undo_coordinates = point(99999, 99999, 99999)
self.element = ""
self.PDBIndex = ""
self.line = ""
self.atomtype = ""
self.IndeciesOfAtomsConnecting = []
self.charge = 0
def ReadPDBLine(self, Line):
self.line = Line
self.atomname = Line[11:16].strip()
if len(self.atomname) == 1:
self.atomname = self.atomname + " "
elif len(self.atomname) == 2:
self.atomname = self.atomname + " "
elif len(self.atomname) == 3:
self.atomname = self.atomname + " " # This line is necessary to work, though many PDBs in the PDB would have this line commented out
self.coordinates = point(float(Line[30:38]), float(Line[38:46]), float(Line[46:54]))
# now atom type (for pdbqt)
self.atomtype = Line[77:79].strip().upper()
self.charge = float(Line[69:76])
# if len(Line)>= 79:
# self.element = Line[76:79].strip().upper() # element specified explicitly at end of life
# el
if self.element == "": # try to guess at element from name
two_letters = self.atomname[0:2].strip().upper()
if two_letters == 'BR':
self.element = 'BR'
elif two_letters == 'CL':
self.element = 'CL'
elif two_letters == 'BI':
self.element = 'BI'
elif two_letters == 'AS':
self.element = 'AS'
elif two_letters == 'AG':
self.element = 'AG'
elif two_letters == 'LI':
self.element = 'LI'
# elif two_letters == 'HG':
# self.element = 'HG'
elif two_letters == 'MG':
self.element = 'MG'
elif two_letters == 'MN':
self.element = 'MN'
elif two_letters == 'RH':
self.element = 'RH'
elif two_letters == 'ZN':
self.element = 'ZN'
elif two_letters == 'FE':
self.element = 'FE'
else: # So, just assume it's the first letter.
# Any number needs to be removed from the element name
self.element = self.atomname
self.element = self.element.replace('0','')
self.element = self.element.replace('1','')
self.element = self.element.replace('2','')
self.element = self.element.replace('3','')
self.element = self.element.replace('4','')
self.element = self.element.replace('5','')
self.element = self.element.replace('6','')
self.element = self.element.replace('7','')
self.element = self.element.replace('8','')
self.element = self.element.replace('9','')
self.element = self.element.replace('@','')
self.element = self.element[0:1].strip().upper()
self.PDBIndex = Line[6:12].strip()
self.residue = Line[16:20]
if self.residue.strip() == "": self.residue = " MOL"
def CreatePDBLine(self):
# if len(self.atomname) > 1: self.atomname = self.atomname[:1].upper() + self.atomname[1:].lower()
output = "ATOM "
# output = output + str(index).rjust(6) + self.atomname.rjust(5) + self.residue.rjust(4)
output = output + self.PDBIndex.rjust(6) + self.atomname.rjust(5) + self.residue.rjust(4)
output = output + ("%.3f" % self.coordinates.x).rjust(18)
output = output + ("%.3f" % self.coordinates.y).rjust(8)
output = output + ("%.3f" % self.coordinates.z).rjust(8)
output = output + self.element.rjust(24) # + " " + str(uniqueID) # This last part must be removed
return output
class PDB:
def __init__ (self):
self.AllAtoms = {}
def LoadPDB(self, FileName):
self.__init__()
# Now load the file into a list
file = open(FileName,"r")
lines = file.readlines()
file.close()
self.LoadPDB_from_list(lines)
def print_out_info(self):
for index in self.AllAtoms:
print(self.AllAtoms[index].CreatePDBLine())
def LoadPDB_from_list(self, lines):
autoindex = 1
self.entropy_count = 0
'''for line in file.readlines():
if "between atoms" in line and " A " in line:
entropy_count = entropy_count + 1
file.close()'''
for t in range(0, len(lines)):
line = lines[t]
if len(line)>= 7:
if "between atoms" in line and " A " in line:
self.entropy_count = self.entropy_count + 1
if line[0:5] == "ATOM " or line[0:7] == "HETATM ": # Load atom data (coordinates, etc.)
TempAtom = atom()
TempAtom.ReadPDBLine(line)
self.AllAtoms[autoindex] = TempAtom # because points files have no indecies
autoindex = autoindex + 1
class Complex:
def not_trained(self,type, key, ligand_atom,receptor_atom, ligand_filename):
toreturn = "The program can't deal with the " + type + " calculation between ligand,receptor atom types: " + key + ", ligand = " + ligand_filename + "\n"
toreturn = toreturn + "LIGAND: " + ligand_atom.line.strip() + "\n"
toreturn = toreturn + "RECEPTOR: " + receptor_atom.line.strip()
return toreturn
# sys.exit(0)
def make_key(self,string1, string2):
# compare 2 strings cmp(string1, string2)
# if string_1 < string_2 then return -1
# elif string_1 == string_2 return zero
# elif string_1 > string_2 retrun +1
# Example "OB" > "OA" > "NA" > "MA"
# This is to replace the cmp() function which was removed in python 3
# replace cmp() with (a > b) - (a < b) as an equivalent substitution.
compare_value = (string1 > string2) - (string1 < string2)
if compare_value == -1:
key = string1 + "_" + string2
else:
key = string2 + "_" + string1
return key
def __init__ (self, ligand, receptor): # ligand_name, receptor_name):
# self.IC50 = IC50 * math.pow(10,15) # so IC fifty is given in M, converted to fM.
'''if IC50<= math.pow(10,-8): # less than 10 nM
self.IC50 = [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
if IC50<= math.pow(10,-7): # less than 100 nM
self.IC50 = [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]
elif IC50<= math.pow(10,-6): # less than 1 uM
self.IC50 = [0.0, 0.0, 1.0, 0.0, 0.0, 0.0]
elif IC50<= math.pow(10,-5): # less than 10 uM
self.IC50 = [0.0, 0.0, 0.0, 1.0, 0.0, 0.0]
elif IC50<= math.pow(10,-4): # less than 100 uM
self.IC50 = [0.0, 0.0, 0.0, 0.0, 1.0, 0.0]
else: # > 100 uM, doesn't bind
self.IC50 = [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]'''
# self.IC50_orig = IC50
# if IC50<= 2.5 * math.pow(10,-5): # less than 25 uM
# self.IC50 = [1.0, 0.0]
# else:
# self.IC50 = [0.0, 1.0]
# picklename = "./pickles/"+os.path.basename(ligand_name)+"_"+os.path.basename(receptor_name)+".pickle"
# else:
# First, load in the ligand
# ligand = PDB()
# ligand.LoadPDB(ligand_name)
# receptor = PDB()
# receptor.LoadPDB(receptor_name)
# get info
# all atom types, too big
# charge_type_combos = ["FE_N", "A_BR", "MG_NA", "CL_SA", "A_A", "A_C", "A_CL", "A_F", "A_FE", "A_HD", "A_I", "A_N", "A_NA", "A_OA", "A_P", "A_S", "A_SA", "A_ZN", "BR_C", "BR_HD", "BR_N", "BR_OA", "C_C", "C_CL", "C_F", "C_FE", "C_HD", "C_I", "CL_HD", "CL_N", "CL_OA", "C_MG", "C_MN", "C_N", "C_NA", "C_OA", "C_P", "C_S", "C_SA", "C_ZN", "FE_HD", "FE_OA", "F_HD", "F_N", "F_OA", "F_SA", "HD_HD", "HD_I", "HD_MG", "HD_MN", "HD_N", "HD_NA", "HD_OA", "HD_P", "HD_S", "HD_SA", "HD_ZN", "I_N", "I_OA", "MG_OA", "MG_P", "MN_N", "MN_OA", "MN_P", "NA_OA", "NA_SA", "NA_ZN", "N_N", "N_NA", "N_OA", "N_P", "N_S", "N_SA", "N_ZN", "OA_OA", "OA_P", "OA_S", "OA_SA", "OA_ZN", "P_ZN", "SA_SA", "SA_ZN", "S_ZN"]
charge_type_combos = ["A_A", "A_BR", "A_C", "A_CL", "A_F", "A_FE", "A_HD", "A_I", "A_N", "A_NA", "A_OA", "A_P", "A_S", "A_SA", "A_ZN", "BR_C", "BR_HD", "BR_N", "BR_OA", "C_C", "C_CL", "C_F", "C_FE", "C_HD", "C_I", "CL_HD", "CL_N", "CL_OA", "CL_SA", "C_MG", "C_MN", "C_N", "C_NA", "C_OA", "C_P", "C_S", "C_SA", "C_ZN", "FE_HD", "FE_N", "FE_OA", "F_HD", "F_N", "F_OA", "F_SA", "HD_HD", "HD_I", "HD_MG", "HD_MN", "HD_N", "HD_NA", "HD_OA", "HD_P", "HD_S", "HD_SA", "HD_ZN", "I_N", "I_OA", "MG_NA", "MG_OA", "MG_P", "MN_N", "MN_OA", "MN_P", "NA_OA", "NA_SA", "NA_ZN", "N_N", "N_NA", "N_OA", "N_P", "N_S", "N_SA", "N_ZN", "OA_OA", "OA_P", "OA_S", "OA_SA", "OA_ZN", "P_ZN", "SA_SA", "SA_ZN", "S_ZN"]
# proximity_2_type_combos = ["FE_HD", "A_HD", "C_HD", "C_OA", "C_SA", "HD_HD", "HD_MG", "HD_N", "HD_NA", "HD_OA", "HD_ZN", "MG_OA", "NA_ZN", "OA_ZN"]
proximity_2_type_combos = ["A_HD", "C_HD", "C_OA", "C_SA", "FE_HD", "HD_HD", "HD_MG", "HD_N", "HD_NA", "HD_OA", "HD_ZN", "MG_OA", "NA_ZN", "OA_ZN"]
# proximity_4_type_combos = ["FE_N", "A_BR", "MG_NA", "CL_SA", "A_A", "A_C", "A_CL", "A_F", "A_FE", "A_HD", "A_I", "A_N", "A_NA", "A_OA", "A_P", "A_S", "A_SA", "A_ZN", "BR_C", "BR_HD", "BR_N", "BR_OA", "C_C", "C_CL", "C_F", "C_FE", "C_HD", "C_I", "CL_HD", "CL_N", "CL_OA", "C_MG", "C_MN", "C_N", "C_NA", "C_OA", "C_P", "C_S", "C_SA", "C_ZN", "FE_HD", "FE_OA", "F_HD", "F_N", "F_OA", "F_SA", "HD_HD", "HD_I", "HD_MG", "HD_MN", "HD_N", "HD_NA", "HD_OA", "HD_P", "HD_S", "HD_SA", "HD_ZN", "I_N", "I_OA", "MG_OA", "MG_P", "MN_N", "MN_OA", "MN_P", "NA_OA", "NA_SA", "NA_ZN", "N_N", "N_NA", "N_OA", "N_P", "N_S", "N_SA", "N_ZN", "OA_OA", "OA_P", "OA_S", "OA_SA", "OA_ZN", "P_ZN", "SA_SA", "SA_ZN", "S_ZN"]
proximity_4_type_combos = ["A_A", "A_BR", "A_C", "A_CL", "A_F", "A_FE", "A_HD", "A_I", "A_N", "A_NA", "A_OA", "A_P", "A_S", "A_SA", "A_ZN", "BR_C", "BR_HD", "BR_N", "BR_OA", "C_C", "C_CL", "C_F", "C_FE", "C_HD", "C_I", "CL_HD", "CL_N", "CL_OA", "CL_SA", "C_MG", "C_MN", "C_N", "C_NA", "C_OA", "C_P", "C_S", "C_SA", "C_ZN", "FE_HD", "FE_N", "FE_OA", "F_HD", "F_N", "F_OA", "F_SA", "HD_HD", "HD_I", "HD_MG", "HD_MN", "HD_N", "HD_NA", "HD_OA", "HD_P", "HD_S", "HD_SA", "HD_ZN", "I_N", "I_OA", "MG_NA", "MG_OA", "MG_P", "MN_N", "MN_OA", "MN_P", "NA_OA", "NA_SA", "NA_ZN", "N_N", "N_NA", "N_OA", "N_P", "N_S", "N_SA", "N_ZN", "OA_OA", "OA_P", "OA_S", "OA_SA", "OA_ZN", "P_ZN", "SA_SA", "SA_ZN", "S_ZN"]
lig_types_combos = ["A", "BR", "C", "CL", "F", "HD", "I", "N", "NA", "OA", "P", "S", "SA"]
# autodock_atom_types = ["HD", "HS", "C", "A", "N", "NA", "OA", "F", "P", "SA", "S", "CL", "BR", "I"]
# autodock_atom_types = ["H", "C", "N", "O", "P", "S", "CL"]
# Autogenerate keys
# ligand_atom_types = ["H", "C", "N", "O", "F", "P", "S", "CL", "BR", "I"]
# receptors_atom_types = ["C", "FE", "H", "MG", "MN", "N", "O", "P", "S", "ZN"]
# ligand_atom_types = ["H", "HD", "HS", "C", "A", "N", "NA", "NS", "OA", "OS", "F", "MG", "P", "SA", "S", "CL", "CA", "MN", "FE", "ZN", "BR", "I"]
# receptors_atom_types = ["H", "HD", "HS", "C", "A", "N", "NA", "NS", "OA", "OS", "F", "MG", "P", "SA", "S", "CL", "CA", "MN", "FE", "ZN", "BR", "I"]
# type_combos = []
# for lig_type in ligand_atom_types:
# for recep_type in receptors_atom_types:
# type_combos.append(self.make_key(lig_type, recep_type))
# charge_type_combos = type_combos[:]
# proximity_2_type_combos = type_combos[:]
# proximity_4_type_combos = type_combos[:]
# Enhancement: sort alphabetically. You actually have double the needed here.
# charge_type_combos = ["BR_C", "BR_H", "BR_N", "BR_O", "C_C", "C_CL", "C_F", "C_FE", "C_H", "C_I", "CL_H", "CL_N", "CL_O", "C_MG", "C_MN", "C_N", "C_O", "C_P", "C_S", "C_ZN", "FE_H", "FE_O", "FE_S", "F_H", "F_N", "F_O", "F_S", "F_ZN", "H_H", "H_I", "H_MG", "H_MN", "H_N", "H_O", "H_P", "H_S", "H_ZN", "I_N", "I_O", "MG_N", "MG_O", "MG_P", "MN_N", "MN_O", "MN_P", "N_N", "N_O", "N_P", "N_S", "N_ZN", "O_O", "O_P", "O_S", "O_ZN", "P_S", "P_ZN", "S_S", "S_ZN"]
# proximity_2_type_combos = ["C_H", "C_N", "C_O", "C_S", "F_H", "H_H", "H_MG", "H_N", "H_O", "H_ZN", "MG_O", "MN_O", "N_ZN", "O_O", "O_ZN"]
# proximity_4_type_combos = ["BR_C", "BR_H", "BR_N", "BR_O", "C_C", "C_CL", "C_F", "C_FE", "C_H", "C_I", "CL_H", "CL_N", "CL_O", "C_MG", "C_MN", "C_N", "C_O", "C_P", "C_S", "C_ZN", "FE_H", "FE_O", "FE_S", "F_H", "F_N", "F_O", "F_S", "F_ZN", "H_H", "H_I", "H_MG", "H_MN", "H_N", "H_O", "H_P", "H_S", "H_ZN", "I_N", "I_O", "MG_N", "MG_O", "MG_P", "MN_N", "MN_O", "MN_P", "N_N", "N_O", "N_P", "N_S", "N_ZN", "O_O", "O_P", "O_S", "O_ZN", "P_S", "P_ZN", "S_S", "S_ZN"]
self.coulomb_energy = {} # where to store energy info
self.proximity_2 = {} # where to store proximity info (within 2 A)
self.proximity_4 = {} # where to store proximity info (within 3.5 A)
self.lig_types = {}
for key in charge_type_combos:
self.coulomb_energy[key] = 0
for key in proximity_2_type_combos:
self.proximity_2[key] = 0
for key in proximity_4_type_combos:
self.proximity_4[key] = 0
for key in lig_types_combos:
self.lig_types[key] = 0
# Here you could remove receptor atoms that are far from the ligand... not currently implemented, but would speed things up a bit.
self.bad_training = ""
for ligand_index in ligand.AllAtoms:
ligand_atom = ligand.AllAtoms[ligand_index]
# also keep track of the different ligand types
lig_type = ligand_atom.atomtype
if lig_type not in self.lig_types:
self.bad_training = self.bad_training + "\n\n" + "The program can't deal with ligands that have the atom type: " + lig_type
else:
self.lig_types[lig_type] = self.lig_types[lig_type] + 1
for receptor_index in receptor.AllAtoms:
receptor_atom = receptor.AllAtoms[receptor_index]
dist = ligand_atom.coordinates.dist_to(receptor_atom.coordinates)
if dist < 0.5: print("There may be steric clashes between " +ligand_name + ", " + receptor_name)
if dist < 4.0:
# get the key
# lig_type = ligand_atom.element
# recep_type = receptor_atom.element
recep_type = receptor_atom.atomtype
key = self.make_key(lig_type, recep_type)
key_ordered = lig_type + " " + recep_type
if key in charge_type_combos:
# calculate charges
lig_charge = ligand_atom.charge
recep_charge = receptor_atom.charge
coulomb = lig_charge * recep_charge / dist # ignore all constants. Just let the neural net take care of that
self.coulomb_energy[key] = self.coulomb_energy[key] + coulomb
else:
self.bad_training = self.bad_training + "\n\n" + self.not_trained("CHARGE", key_ordered, ligand_atom,receptor_atom,ligand_name)
if dist < 2.0: # so it's a close contact
if key in proximity_2_type_combos:
self.proximity_2[key] = self.proximity_2[key] + 1
else:
self.bad_training = self.bad_training + "\n\n" + self.not_trained("PROXIMITY_2", key_ordered, ligand_atom,receptor_atom,ligand_name)
else: # so it's between 2.0 and 4.0
if key in proximity_4_type_combos:
self.proximity_4[key] = self.proximity_4[key] + 1
else:
self.bad_training = self.bad_training + "\n\n" + self.not_trained("PROXIMITY_4", key_ordered, ligand_atom,receptor_atom, ligand_name)
# now we need to find out how many active torsions there are in the ligand (so the network can account for some entropy effects).
entropy_count = ligand.entropy_count
'''file = open(ligand_name, "r")
entropy_count = 0
for line in file.readlines():
if "between atoms" in line and " A " in line:
entropy_count = entropy_count + 1
file.close()'''
self.nn_input = []
for key in charge_type_combos:
self.nn_input.append(self.coulomb_energy[key])
for key in proximity_2_type_combos:
self.nn_input.append(self.proximity_2[key])
for key in proximity_4_type_combos:
self.nn_input.append(self.proximity_4[key])
for key in lig_types_combos:
self.nn_input.append(self.lig_types[key])
self.nn_input.append(entropy_count)
networks = []
receptor_name = ""
ligand_name = ""
vina_output = ""
autodock_output = ""
for index in range(1,len(sys.argv)):
var = sys.argv[index].strip()
if var.upper() == "-RECEPTOR": receptor_name = sys.argv[index+1]
if var.upper() == "-LIGAND": ligand_name = sys.argv[index+1]
if var.upper() == "-VINA_OUTPUT": vina_output = sys.argv[index+1]
if var.upper() == "-AUTODOCK_OUTPUT": autodock_output = sys.argv[index+1] ###### ADD TO USER MANUAL #####
if var.upper() == "-NETWORK": networks.append(sys.argv[index+1])
if var.upper() == "-NETWORKS_DIR": # a directory containing only networks
sys.argv[index+1] = sys.argv[index+1].replace("\\","/")
if sys.argv[index+1][-1:] != "/": sys.argv[index+1] = sys.argv[index+1] + "/"
for filename in os.listdir(sys.argv[index+1]):
if os.path.isfile(sys.argv[index+1] + filename): networks.append(sys.argv[index+1] + filename)
print("")
print("NNScore " + version)
print("")
print(program_info)
print("")
print("If you use NNScore in your research, please cite the following reference:")
print(" NNScore: A Neural-Network-Based Scoring Function for the Characterization")
print(" of Protein-Ligand Complexes. Jacob D. Durrant, J. Andrew McCammon. Journal")
print(" of Chemical Information and Modeling, 2010, 50 (10), pp 1865-1871.")
print("")
error = False
if receptor_name == "":
error = True
extra_message = "Error! Required parameters were not passed to NNScore!"
if len(networks) == 0:
error = True
extra_message = "Error! Required parameters were not passed to NNScore!"
if ligand_name == "" and vina_output == "" and autodock_output == "":
error = True
extra_message = "Error! Required parameters were not passed to NNScore!"
if ligand_name != "" and vina_output != "":
error = True
extra_message = "Error! You cannot use both -ligand and -vina_output!"
if ligand_name != "" and autodock_output != "":
error = True
extra_message = "Error! You cannot use both -ligand and -autodock_output!"
if autodock_output != "" and vina_output != "":
error = True
extra_message = "Error! You cannot use both -autodock_output and -vina_output!"
if error is True:
print(extra_message)
print("\nParameters are:")
print("\t-receptor <pdbqt filename>")
print("\t-ligand <pdbqt filename>")
print("\t-vina_output <vina output filename>")
print("\t-autodock_output <autodock output filename>")
print("\t-network <network filename>")
print("\t-networks_dir <directory>")
print("")
print("Note: It is best to use multiple neural networks to judge ligand binding by")
print("consensus. Commandline parameters can be used to add neural-network files")
print("to the list of those that will be used. To add a single neural network to")
print("the list, use the -network parameter to specify a single network file. To")
print("add mutliple networks to the list, create a directory containing only")
print("network files and specify the path to that directory using the -networks_dir")
print("parameter.")
print("")
print("Note: Only pdbqt files of the receptor and ligand are accepted. Scripts to")
print("convert from pdb to pdbqt are included in the AutoDockTools package:")
print("http://autodock.scripps.edu/resources/adt")
print("")
print("Note: If an AutoDock Vina or AutoDock output file is specified, NNScore will")
print("evaluate all docked poses and return the best NNScore calculated. To score an")
print("AutoDock output file, modify the mglenv and prepare_ligand4_location variables")
print("at the begining of this python file.")
print("")
print("Examples:")
print("\t python NNScore.py -receptor neuraminidase.pdbqt -ligand oseltamivir.pdbqt -network ./networks/top_3_networks/12.net")
print("\t python NNScore.py -receptor integrase.pdbqt -ligand raltegravir.pdbqt -networks_dir ./networks/top_3_networks/")
print("\t python NNScore.py -receptor integrase.pdbqt -vina_output docked_poses.vina.out -networks_dir ./networks/top_3_networks/")
print("\t python NNScore.py -receptor integrase.pdbqt -autodock_output docked_poses.autodock.out -networks_dir ./networks/top_3_networks/")
print("\t python NNScore.py -receptor protease.pdbqt -ligand tipranavir.pdbqt -networks_dir ./networks/top_24_networks/ -network ./networks/top_3_networks/16.net")
print("")
sys.exit()
print("Receptor: "+ receptor_name)
print("")
def process_ligand(ligand_name, ligand, receptor):
global networks
print("Ligand: "+ ligand_name)
print(" = " * len("Ligand: "+ ligand_name))
if len(networks) == 1:
print("\tNetwork: ", networks[0])
else:
print("\tNetworks: ")
for net in networks:
print("\t\t", net)
print("")
acomplex = Complex(ligand, receptor)
scores = []
# describe binding
print("\tAtom types (one ligand, one receptor) within 2 angstroms of each other:")
first = 1
something = 1
for item in acomplex.proximity_2:
value = acomplex.proximity_2[item]
if value != 0:
something = 0
addin = ""
if first != 1: addin = "; "
first = 0
print(addin + "(" + str(item.replace("_",', ')) + "), " + str(value))
if value == 1:
sys.stdout.write(" time")
else:
sys.stdout.write(" times")
if something == 1:
print("(None)")
print("")
print("")
print("\tAtom types (one ligand, one receptor) within 4 angstroms of each other:")
first = 1
something = 1
for item in acomplex.proximity_4:
value = acomplex.proximity_4[item]
if value != 0:
something = 0
addin = ""
if first != 1: addin = "; "
first = 0
print(addin + "(" + str(item.replace("_",', ')) + "), " + str(value))
if value == 1:
sys.stdout.write(" time")
else:
sys.stdout.write(" times")
if something == 1: print("(None)")
print("")
print("")
print("\tRelative coulombic energy between atom types (one ligand, one receptor) within 4 angtroms of each other:")
first = 1
something = 1
for item in acomplex.coulomb_energy:
value = round(acomplex.coulomb_energy[item],3)
if value != 0:
something = 0
addin = ""
if first != 1: addin = "; "
first = 0
print(addin + "(" + str(item.replace("_",', ')) + "), " + str(value))
if value == 1:
sys.stdout.write(" unit")
else:
sys.stdout.write(" units")
if something == 1: print("(None)")
print("")
print("")
print("\tAtom types in the ligand:")
first = 1
something = 1
for item in acomplex.lig_types:
value = acomplex.lig_types[item]
if value != 0:
something = 0
addin = ""
if first != 1: addin = "; "
first = 0
print(addin + str(item.replace("_",', ')) + ", " + str(value))
if value == 1:
sys.stdout.write(" time")
else:
sys.stdout.write(" times")
if something == 1: print("(None)")
print("")
print("")
# self.coulomb_energy = {} # where to store energy info
# self.proximity_2 = {} # where to store proximity info (within 2 A)
# self.proximity_4 = {} # where to store proximity info (within 3.5 A)
# self.lig_types = {}
for net in networks:
print("\tUsing network " + net + " to predict binding: ")
net_name = net
net = FFNet(net_name)
result = net.call(acomplex.nn_input)
score = result[0] - result[1]
print("\t", score)
scores.append(score)
if score < 0:
print("(bad binder)")
else:
print("(good binder)")
# compute average score
total = 0
for score in scores: total = total + score
average_score = total/len(scores)
if acomplex.bad_training != "":
print(acomplex.bad_training)
average_score = -999999.9
return average_score
if ligand_name != "": # so a single pdbqt ligand was provided
ligand = PDB()
ligand.LoadPDB(ligand_name)
receptor = PDB()
receptor.LoadPDB(receptor_name)
average_score = process_ligand(ligand_name, ligand, receptor)
print("")
print("Average score: ", average_score)
if average_score < 0:
print("(bad binder)")
else:
print("(good binder)")
print("")
elif vina_output != "": # so a vina output file has been passed
receptor = PDB()
receptor.LoadPDB(receptor_name)
file = open(vina_output,"r")
lines = file.readlines()
file.close()
thelines = []
label = ""
best_binder = -10000000.0
best_binder_name = ""
for line in lines:
if "MODEL " in line:
if len(thelines) != 0:
# so create a complex of this frame and the receptor, get the score
ligand = PDB()
ligand.LoadPDB_from_list(thelines)
average_score = process_ligand(vina_output + ", " +label, ligand, receptor)
if best_binder < average_score:
best_binder = average_score
best_binder_name = vina_output + ", " +label
print("\n\tAverage score: ", average_score)
if average_score < 0: print("(bad binder)")
else: print("(good binder)")
print("")
thelines = [line.strip()]
else: thelines = [line.strip()]
label = line.strip()
else: thelines.append(line.strip())
# so create a complex of this frame and the receptor, get the score
ligand = PDB()
ligand.LoadPDB_from_list(thelines)
average_score = process_ligand(vina_output + ", " +label, ligand, receptor)
if best_binder < average_score:
best_binder = average_score
best_binder_name = vina_output + ", " +label
print("\n\tAverage score: ", average_score)
if average_score < 0: print("(bad binder)")
else: print("(good binder)")
print("")
print("Best score:", best_binder,"(" + best_binder_name + ")")
print("")
elif autodock_output != "":
receptor = PDB()
receptor.LoadPDB(receptor_name)
file = open(autodock_output,"r")
lines = file.readlines()
file.close()
for t in range(len(lines)):
if "Keeping original residue number (specified in the input PDBQ file) for outputting." in lines[t]: break
pdbs = []
thelines = []
for i in range(t+2,len(lines)):
line = lines[i]
if " l " in line:
print("Atom name \"l\" being interpreted as \"Cl.\"")
line = line.replace(' l ','Cl ')
if " r " in line:
print("Atom name \"r\" being interpreted as \"Br.\"")
line = line.replace(' r ','Br ')
if " A " in line:
print("Atom name \"A\" being interpreted as \"C\" (i.e. an aromatic carbon).")
line = line.replace(' A ',' C ')
if "ENDMDL" in line:
thelines.append(line)
pdbs.append(thelines)
thelines = []
else: thelines.append(line)
# now, printOut PBD files in temp directory
if not os.path.exists(sys.path[0] + os.sep + "tmp"):
os.mkdir(sys.path[0] + os.sep + "tmp")
print("Created directory " + sys.path[0] + os.sep + 'tmp' + os.sep)
# now, pick a random id number
id = random.randrange(0, 1000000)
filenames = []
for pdb in pdbs:
name = pdb[0]
name = name.replace("\t"," ")
while " " in name: name = name.replace(" "," ")
name = name.replace(" ", "_").strip()
filename = sys.path[0] + os.sep + 'tmp' + os.sep + name +"."+ str(id) + ".pdb"
filenames.append(filename)
f = open(filename,'w')
f.writelines(pdb)
f.close()
# now convert the pdbs into pdbqts
runstring = ''
if mglenv.strip() != "": runstring = 'source ' + mglenv + '; '
runstring = runstring + prepare_ligand4_location
print('Converting frames from AutoDock output file into individual pdbqt files...')
for filename in filenames:
torun = runstring + " -l " + filename + " -o " + filename + "qt"
print("\t"+torun)
os.system(torun)
best_binder = -10000000.0
best_binder_name = ""
for filename in filenames:
filename = filename+"qt"
ligand = PDB()
ligand.LoadPDB(filename)
tmp = os.path.basename(filename)
tmp = tmp.split(".")
tmp = tmp[0]
ligand_name = autodock_output + ", " + tmp.replace("_",' ')
average_score = process_ligand(ligand_name, ligand, receptor)
if best_binder < average_score:
best_binder = average_score
best_binder_name = ligand_name
print("\n\tAverage score: ", average_score)
if average_score < 0: print("(bad binder)")
else: print("(good binder)")
print("")
print("Best score:", best_binder,"(" + best_binder_name + ")")
print("")
# now delete files
for filename in filenames:
os.remove(filename)
os.remove(filename + "qt")
| 42,353 | 39.530144 | 707 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/nn_score_exe/nnscore1/__init__.py | 2 | 0.5 | 1 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/nn_score_exe/nnscore2/__init__.py | 2 | 0.5 | 1 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/scoring/nn_score_exe/nnscore2/NNScore2.py | # NNScore 2.02 is released under the GNU General Public License (see
# http://www.gnu.org/licenses/gpl.html). If you have any questions, comments,
# or suggestions, please don't hesitate to contact me, Jacob Durrant, at
# durrantj [at] pitt [dot] edu. If you use NNScore 2.02 in your work, please
# cite [REFERENCE HERE].
import __future__
import textwrap
import math
import os
import sys
import glob
import pickle
################################## MODIFY THIS VARIABLE TO POINT TO THE AUTODOCK VINA EXECUTABLE ##################################
vina_executable = "/PATH/TO/VINA_1_1_2/vina"
# Example: vina_executable = "/gpfs/autodock_vina_1_1_2_linux_x86/bin/vina"
###################################################################################################################################
# The ffnet class was derived from the ffnet python package developed by Marek Wojciechowski (http://ffnet.sourceforge.net/).
# As Mr. Wojciechowski's program was released under the GPL license, NNScore is likewise GPL licensed.
class ffnet:
def load(self, net_array):
self.outno = net_array['outno']
self.eni = net_array['eni']
self.weights = net_array['weights']
self.conec = net_array['conec']
self.deo = net_array['deo']
self.inno = net_array['inno']
self.units = {}
self.output = {}
def normcall(self, input):
#Takes single input pattern and returns network output
#This have the same functionality as recall but now input and
#output are normalized inside the function.
#eni = [ai, bi], eno = [ao, bo] - parameters of linear mapping
self.input = input
#set input units
self.setin()
#propagate signals
self.prop()
#get output
self.getout()
return self.output[1]
def setin(self):
#normalize and set input units
for k in range(1,len(self.inno) + 1):
self.units[self.inno[k]] = self.eni[k][1] * self.input[k-1] + self.eni[k][2] # because self.input is a python list, and the others were inputted from a fortran-format file
def prop(self):
#Gets conec and units with input already set
#and calculates all activations.
#Identity input and sigmoid activation function for other units
#is assumed
#propagate signals with sigmoid activation function
if len(self.conec) > 0:
ctrg = self.conec[1][2]
self.units[ctrg] = 0.0
for xn in range(1,len(self.conec) + 1):
src = self.conec[xn][1]
trg = self.conec[xn][2]
# if next unit
if trg != ctrg:
self.units[ctrg] = 1.0/(1.0+math.exp(-self.units[ctrg]))
ctrg = trg
if src == 0: # handle bias
self.units[ctrg] = self.weights[xn]
else:
self.units[ctrg] = self.units[src] * self.weights[xn]
else:
if src == 0: # handle bias
self.units[ctrg] = self.units[ctrg] + self.weights[xn]
else:
self.units[ctrg] = self.units[ctrg] + self.units[src] * self.weights[xn]
self.units[ctrg] = 1.0/(1.0+math.exp(-self.units[ctrg])) # for last unit
def getout(self):
#get and denormalize output units
for k in range(1,len(self.outno)+1):
self.output[k] = self.deo[k][1] * self.units[self.outno[k]] + self.deo[k][2]
class point:
x=99999.0
y=99999.0
z=99999.0
def __init__ (self, x, y ,z):
self.x = x
self.y = y
self.z = z
def copy_of(self):
return point(self.x, self.y, self.z)
def dist_to(self,apoint):
return math.sqrt(math.pow(self.x - apoint.x,2) + math.pow(self.y - apoint.y,2) + math.pow(self.z - apoint.z,2))
def Magnitude(self):
return self.dist_to(point(0,0,0))
def CreatePDBLine(self, index):
output = "ATOM "
output = output + str(index).rjust(6) + "X".rjust(5) + "XXX".rjust(4)
output = output + ("%.3f" % self.x).rjust(18)
output = output + ("%.3f" % self.y).rjust(8)
output = output + ("%.3f" % self.z).rjust(8)
output = output + "X".rjust(24)
return output
class atom:
def __init__ (self):
self.atomname = ""
self.residue = ""
self.coordinates = point(99999, 99999, 99999)
self.element = ""
self.PDBIndex = ""
self.line=""
self.atomtype=""
self.IndeciesOfAtomsConnecting=[]
self.charge = 0
self.resid = 0
self.chain = ""
self.structure = ""
self.comment = ""
def copy_of(self):
theatom = atom()
theatom.atomname = self.atomname
theatom.residue = self.residue
theatom.coordinates = self.coordinates.copy_of()
theatom.element = self.element
theatom.PDBIndex = self.PDBIndex
theatom.line= self.line
theatom.atomtype= self.atomtype
theatom.IndeciesOfAtomsConnecting = self.IndeciesOfAtomsConnecting[:]
theatom.charge = self.charge
theatom.resid = self.resid
theatom.chain = self.chain
theatom.structure = self.structure
theatom.comment = self.comment
return theatom
def CreatePDBLine(self, index):
output = "ATOM "
output = output + str(index).rjust(6) + self.atomname.rjust(5) + self.residue.rjust(4)
output = output + ("%.3f" % self.coordinates.x).rjust(18)
output = output + ("%.3f" % self.coordinates.y).rjust(8)
output = output + ("%.3f" % self.coordinates.z).rjust(8)
output = output + self.element.rjust(24)
return output
def NumberOfNeighbors(self):
return len(self.IndeciesOfAtomsConnecting)
def AddNeighborAtomIndex(self, index):
if not (index in self.IndeciesOfAtomsConnecting):
self.IndeciesOfAtomsConnecting.append(index)
def SideChainOrBackBone(self): # only really applies to proteins, assuming standard atom names
if self.atomname.strip() == "CA" or self.atomname.strip() == "C" or self.atomname.strip() == "O" or self.atomname.strip() == "N":
return "BACKBONE"
else:
return "SIDECHAIN"
def ReadPDBLine(self, Line):
self.line = Line
self.atomname = Line[11:16].strip()
if len(self.atomname)==1:
self.atomname = self.atomname + " "
elif len(self.atomname)==2:
self.atomname = self.atomname + " "
elif len(self.atomname)==3:
self.atomname = self.atomname + " " # This line is necessary to work, though many PDBs in the PDB would have this line commented out
self.coordinates = point(float(Line[30:38]), float(Line[38:46]), float(Line[46:54]))
# now atom type (for pdbqt)
self.atomtype = Line[77:79].strip().upper()
if Line[69:76].strip() != "":
self.charge = float(Line[69:76])
else:
self.charge = 0.0
if self.element == "": # try to guess at element from name
two_letters = self.atomname[0:2].strip().upper()
if two_letters=='BR':
self.element='BR'
elif two_letters=='CL':
self.element='CL'
elif two_letters=='BI':
self.element='BI'
elif two_letters=='AS':
self.element='AS'
elif two_letters=='AG':
self.element='AG'
elif two_letters=='LI':
self.element='LI'
#elif two_letters=='HG':
# self.element='HG'
elif two_letters=='MG':
self.element='MG'
elif two_letters=='MN':
self.element='MN'
elif two_letters=='RH':
self.element='RH'
elif two_letters=='ZN':
self.element='ZN'
elif two_letters=='FE':
self.element='FE'
else: #So, just assume it's the first letter.
# Any number needs to be removed from the element name
self.element = self.atomname
self.element = self.element.replace('0','')
self.element = self.element.replace('1','')
self.element = self.element.replace('2','')
self.element = self.element.replace('3','')
self.element = self.element.replace('4','')
self.element = self.element.replace('5','')
self.element = self.element.replace('6','')
self.element = self.element.replace('7','')
self.element = self.element.replace('8','')
self.element = self.element.replace('9','')
self.element = self.element.replace('@','')
self.element = self.element[0:1].strip().upper()
self.PDBIndex = Line[6:12].strip()
self.residue = Line[16:20]
self.residue = " " + self.residue[-3:] # this only uses the rightmost three characters, essentially removing unique rotamer identification
if Line[23:26].strip() != "": self.resid = int(Line[23:26])
else: self.resid = 1
self.chain = Line[21:22]
if self.residue.strip() == "": self.residue = " MOL"
class PDB:
def __init__ (self):
self.AllAtoms={}
self.NonProteinAtoms = {}
self.max_x = -9999.99
self.min_x = 9999.99
self.max_y = -9999.99
self.min_y = 9999.99
self.max_z = -9999.99
self.min_z = 9999.99
self.rotateable_bonds_count = 0
self.functions = MathFunctions()
self.protein_resnames = ["ALA", "ARG", "ASN", "ASP", "ASH", "ASX", "CYS", "CYM", "CYX", "GLN", "GLU", "GLH", "GLX", "GLY", "HIS", "HID", "HIE", "HIP", "ILE", "LEU", "LYS", "LYN", "MET", "PHE", "PRO", "SER", "THR", "TRP", "TYR", "VAL"]
self.aromatic_rings = []
self.charges = [] # a list of points
self.OrigFileName = ""
def LoadPDB_from_file(self, FileName, line_header=""):
self.line_header=line_header
# Now load the file into a list
file = open(FileName,"r")
lines = file.readlines()
file.close()
self.LoadPDB_from_list(lines, self.line_header)
def LoadPDB_from_list(self, lines, line_header=""):
self.line_header=line_header
autoindex = 1
self.__init__()
atom_already_loaded = [] # going to keep track of atomname_resid_chain pairs, to make sure redundants aren't loaded. This basically
# gets rid of rotomers, I think.
for t in range(0, len(lines)):
line=lines[t]
if "between atoms" in line and " A " in line:
self.rotateable_bonds_count = self.rotateable_bonds_count + 1
if len(line) >= 7:
if line[0:4]=="ATOM" or line[0:6]=="HETATM": # Load atom data (coordinates, etc.)
TempAtom = atom()
TempAtom.ReadPDBLine(line)
key = TempAtom.atomname.strip() + "_" + str(TempAtom.resid) + "_" + TempAtom.residue.strip() + "_" + TempAtom.chain.strip() # this string unique identifies each atom
if key in atom_already_loaded and TempAtom.residue.strip() in self.protein_resnames: # so this is a receptor atom that has already been loaded once
print((self.line_header + "WARNING: Duplicate receptor atom detected: \"" + TempAtom.line.strip()+ "\". Not loading this duplicate."))
if not key in atom_already_loaded or not TempAtom.residue.strip() in self.protein_resnames: # so either the atom hasn't been loaded, or else it's a non-receptor atom
# so note that non-receptor atoms can have redundant names, but receptor atoms cannot.
# This is because protein residues often contain rotamers
atom_already_loaded.append(key) # so each atom can only be loaded once. No rotamers.
self.AllAtoms[autoindex] = TempAtom # So you're actually reindexing everything here.
if not TempAtom.residue[-3:] in self.protein_resnames: self.NonProteinAtoms[autoindex] = TempAtom
autoindex = autoindex + 1
self.CheckProteinFormat()
self.CreateBondsByDistance() # only for the ligand, because bonds can be inferred based on atomnames from PDB
self.assign_aromatic_rings()
self.assign_charges()
def printout(self, thestring):
lines = textwrap.wrap(thestring, 80)
for line in lines:
print(line)
def SavePDB(self, filename):
f = open(filename, "w")
towrite = self.SavePDBString()
if towrite.strip() == "": towrite = "ATOM 1 X XXX 0.000 0.000 0.000 X" # just so no PDB is empty, VMD will load them all
f.write(towrite)
f.close()
def SavePDBString(self):
ToOutput = ""
# write coordinates
for atomindex in self.AllAtoms:
ToOutput = ToOutput + self.AllAtoms[atomindex].CreatePDBLine(atomindex) + "\n"
return ToOutput
def AddNewAtom(self, atom):
# first get available index
t = 1
while t in list(self.AllAtoms.keys()):
t = t + 1
# now add atom
self.AllAtoms[t] = atom
def connected_atoms_of_given_element(self, index, connected_atom_element):
atom = self.AllAtoms[index]
connected_atoms = []
for index2 in atom.IndeciesOfAtomsConnecting:
atom2 = self.AllAtoms[index2]
if atom2.element == connected_atom_element:
connected_atoms.append(index2)
return connected_atoms
def connected_heavy_atoms(self, index):
atom = self.AllAtoms[index]
connected_atoms = []
for index2 in atom.IndeciesOfAtomsConnecting:
atom2 = self.AllAtoms[index2]
if atom2.element != "H": connected_atoms.append(index2)
return connected_atoms
def CheckProteinFormat(self):
curr_res = ""
first = True
residue = []
for atom_index in self.AllAtoms:
atom = self.AllAtoms[atom_index]
key = atom.residue + "_" + str(atom.resid) + "_" + atom.chain
if first is True:
curr_res = key
first = False
if key != curr_res:
self.CheckProteinFormat_process_residue(residue, last_key)
residue = []
curr_res = key
residue.append(atom.atomname.strip())
last_key = key
self.CheckProteinFormat_process_residue(residue, last_key)
def CheckProteinFormat_process_residue(self, residue, last_key):
temp = last_key.strip().split("_")
resname = temp[0]
real_resname = resname[-3:]
resid = temp[1]
chain = temp[2]
if real_resname in self.protein_resnames: # so it's a protein residue
if not "N" in residue:
self.printout('WARNING: There is no atom named "N" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine secondary structure. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "C" in residue:
self.printout('WARNING: There is no atom named "C" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine secondary structure. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CA" in residue:
self.printout('WARNING: There is no atom named "CA" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine secondary structure. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if real_resname == "GLU" or real_resname == "GLH" or real_resname == "GLX":
if not "OE1" in residue:
self.printout('WARNING: There is no atom named "OE1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine salt-bridge interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "OE2" in residue:
self.printout('WARNING: There is no atom named "OE2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine salt-bridge interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if real_resname == "ASP" or real_resname == "ASH" or real_resname == "ASX":
if not "OD1" in residue:
self.printout('WARNING: There is no atom named "OD1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine salt-bridge interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "OD2" in residue:
self.printout('WARNING: There is no atom named "OD2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine salt-bridge interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if real_resname == "LYS" or real_resname == "LYN":
if not "NZ" in residue:
self.printout('WARNING: There is no atom named "NZ" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-cation and salt-bridge interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if real_resname == "ARG":
if not "NH1" in residue:
self.printout('WARNING: There is no atom named "NH1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-cation and salt-bridge interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "NH2" in residue:
self.printout('WARNING: There is no atom named "NH2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-cation and salt-bridge interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if real_resname == "HIS" or real_resname == "HID" or real_resname == "HIE" or real_resname == "HIP":
if not "NE2" in residue:
self.printout('WARNING: There is no atom named "NE2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-cation and salt-bridge interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "ND1" in residue:
self.printout('WARNING: There is no atom named "ND1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-cation and salt-bridge interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if real_resname == "PHE":
if not "CG" in residue:
self.printout('WARNING: There is no atom named "CG" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CD1" in residue:
self.printout('WARNING: There is no atom named "CD1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CD2" in residue:
self.printout('WARNING: There is no atom named "CD2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CE1" in residue:
self.printout('WARNING: There is no atom named "CE1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CE2" in residue:
self.printout('WARNING: There is no atom named "CE2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CZ" in residue:
self.printout('WARNING: There is no atom named "CZ" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if real_resname == "TYR":
if not "CG" in residue:
self.printout('WARNING: There is no atom named "CG" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CD1" in residue:
self.printout('WARNING: There is no atom named "CD1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CD2" in residue:
self.printout('WARNING: There is no atom named "CD2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CE1" in residue:
self.printout('WARNING: There is no atom named "CE1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CE2" in residue:
self.printout('WARNING: There is no atom named "CE2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CZ" in residue:
self.printout('WARNING: There is no atom named "CZ" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if real_resname == "TRP":
if not "CG" in residue:
self.printout('WARNING: There is no atom named "CG" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CD1" in residue:
self.printout('WARNING: There is no atom named "CD1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CD2" in residue:
self.printout('WARNING: There is no atom named "CD2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "NE1" in residue:
self.printout('WARNING: There is no atom named "NE1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CE2" in residue:
self.printout('WARNING: There is no atom named "CE2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CE3" in residue:
self.printout('WARNING: There is no atom named "CE3" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CZ2" in residue:
self.printout('WARNING: There is no atom named "CZ2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CZ3" in residue:
self.printout('WARNING: There is no atom named "CZ3" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CH2" in residue:
self.printout('WARNING: There is no atom named "CH2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if real_resname == "HIS" or real_resname == "HID" or real_resname == "HIE" or real_resname == "HIP":
if not "CG" in residue:
self.printout('WARNING: There is no atom named "CG" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "ND1" in residue:
self.printout('WARNING: There is no atom named "ND1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CD2" in residue:
self.printout('WARNING: There is no atom named "CD2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "CE1" in residue:
self.printout('WARNING: There is no atom named "CE1" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
if not "NE2" in residue:
self.printout('WARNING: There is no atom named "NE2" in the protein residue ' + last_key + '. Please use standard naming conventions for all protein residues. This atom is needed to determine pi-pi and pi-cation interactions. If this residue is far from the active site, this warning may not affect the NNScore.')
print("")
# Functions to determine the bond connectivity based on distance
# ==============================================================
def CreateBondsByDistance(self):
for AtomIndex1 in self.NonProteinAtoms:
atom1 = self.NonProteinAtoms[AtomIndex1]
if not atom1.residue[-3:] in self.protein_resnames: # so it's not a protein residue
for AtomIndex2 in self.NonProteinAtoms:
if AtomIndex1 != AtomIndex2:
atom2 = self.NonProteinAtoms[AtomIndex2]
if not atom2.residue[-3:] in self.protein_resnames: # so it's not a protein residue
dist = self.functions.distance(atom1.coordinates, atom2.coordinates)
if (dist < self.BondLength(atom1.element, atom2.element) * 1.2):
atom1.AddNeighborAtomIndex(AtomIndex2)
atom2.AddNeighborAtomIndex(AtomIndex1)
def BondLength(self, element1, element2):
'''Bond lengths taken from Handbook of Chemistry and Physics. The information provided there was very specific,
so I tried to pick representative examples and used the bond lengths from those. Sitautions could arise where these
lengths would be incorrect, probably slight errors (<0.06) in the hundreds.'''
distance = 0.0
if element1 == "C" and element2 == "C": distance = 1.53
if element1 == "N" and element2 == "N": distance = 1.425
if element1 == "O" and element2 == "O": distance = 1.469
if element1 == "S" and element2 == "S": distance = 2.048
if (element1 == "C" and element2 == "H") or (element1 == "H" and element2 == "C"): distance = 1.059
if (element1 == "C" and element2 == "N") or (element1 == "N" and element2 == "C"): distance = 1.469
if (element1 == "C" and element2 == "O") or (element1 == "O" and element2 == "C"): distance = 1.413
if (element1 == "C" and element2 == "S") or (element1 == "S" and element2 == "C"): distance = 1.819
if (element1 == "N" and element2 == "H") or (element1 == "H" and element2 == "N"): distance = 1.009
if (element1 == "N" and element2 == "O") or (element1 == "O" and element2 == "N"): distance = 1.463
if (element1 == "O" and element2 == "S") or (element1 == "S" and element2 == "O"): distance = 1.577
if (element1 == "O" and element2 == "H") or (element1 == "H" and element2 == "O"): distance = 0.967
if (element1 == "S" and element2 == "H") or (element1 == "H" and element2 == "S"): distance = 2.025/1.5 # This one not from source sited above. Not sure where it's from, but it wouldn't ever be used in the current context ("AutoGrow")
if (element1 == "S" and element2 == "N") or (element1 == "N" and element2 == "S"): distance = 1.633
if (element1 == "C" and element2 == "F") or (element1 == "F" and element2 == "C"): distance = 1.399
if (element1 == "C" and element2 == "CL") or (element1 == "CL" and element2 == "C"): distance = 1.790
if (element1 == "C" and element2 == "BR") or (element1 == "BR" and element2 == "C"): distance = 1.910
if (element1 == "C" and element2 == "I") or (element1 == "I" and element2 == "C"): distance=2.162
if (element1 == "S" and element2 == "BR") or (element1 == "BR" and element2 == "S"): distance = 2.321
if (element1 == "S" and element2 == "CL") or (element1 == "CL" and element2 == "S"): distance = 2.283
if (element1 == "S" and element2 == "F") or (element1 == "F" and element2 == "S"): distance = 1.640
if (element1 == "S" and element2 == "I") or (element1 == "I" and element2 == "S"): distance= 2.687
if (element1 == "P" and element2 == "BR") or (element1 == "BR" and element2 == "P"): distance = 2.366
if (element1 == "P" and element2 == "CL") or (element1 == "CL" and element2 == "P"): distance = 2.008
if (element1 == "P" and element2 == "F") or (element1 == "F" and element2 == "P"): distance = 1.495
if (element1 == "P" and element2 == "I") or (element1 == "I" and element2 == "P"): distance= 2.490
if (element1 == "P" and element2 == "O") or (element1 == "O" and element2 == "P"): distance= 1.6 # estimate based on eye balling Handbook of Chemistry and Physics
if (element1 == "N" and element2 == "BR") or (element1 == "BR" and element2 == "N"): distance = 1.843
if (element1 == "N" and element2 == "CL") or (element1 == "CL" and element2 == "N"): distance = 1.743
if (element1 == "N" and element2 == "F") or (element1 == "F" and element2 == "N"): distance = 1.406
if (element1 == "N" and element2 == "I") or (element1 == "I" and element2 == "N"): distance= 2.2
if (element1 == "SI" and element2 == "BR") or (element1 == "BR" and element2 == "SI"): distance = 2.284
if (element1 == "SI" and element2 == "CL") or (element1 == "CL" and element2 == "SI"): distance = 2.072
if (element1 == "SI" and element2 == "F") or (element1 == "F" and element2 == "SI"): distance = 1.636
if (element1 == "SI" and element2 == "P") or (element1 == "P" and element2 == "SI"): distance= 2.264
if (element1 == "SI" and element2 == "S") or (element1 == "S" and element2 == "SI"): distance= 2.145
if (element1 == "SI" and element2 == "SI") or (element1 == "SI" and element2 == "SI"): distance= 2.359
if (element1 == "SI" and element2 == "C") or (element1 == "C" and element2 == "SI"): distance= 1.888
if (element1 == "SI" and element2 == "N") or (element1 == "N" and element2 == "SI"): distance= 1.743
if (element1 == "SI" and element2 == "O") or (element1 == "O" and element2 == "SI"): distance= 1.631
return distance;
# Functions to identify positive charges
# ======================================
def assign_charges(self):
# Get all the quartinary amines on non-protein residues (these are the only non-protein groups that will be identified as positively charged)
AllCharged = []
for atom_index in self.NonProteinAtoms:
atom = self.NonProteinAtoms[atom_index]
if atom.element == "MG" or atom.element == "MN" or atom.element == "RH" or atom.element == "ZN" or atom.element == "FE" or atom.element == "BI" or atom.element == "AS" or atom.element == "AG":
chrg = self.charged(atom.coordinates, [atom_index], True)
self.charges.append(chrg)
if atom.element == "N":
if atom.NumberOfNeighbors() == 4: # a quartinary amine, so it's easy
indexes = [atom_index]
indexes.extend(atom.IndeciesOfAtomsConnecting)
chrg = self.charged(atom.coordinates, indexes, True) # so the indicies stored is just the index of the nitrogen and any attached atoms
self.charges.append(chrg)
elif atom.NumberOfNeighbors() == 3: # maybe you only have two hydrogen's added, by they're sp3 hybridized. Just count this as a quartinary amine, since I think the positive charge would be stabalized.
nitrogen = atom
atom1 = self.AllAtoms[atom.IndeciesOfAtomsConnecting[0]]
atom2 = self.AllAtoms[atom.IndeciesOfAtomsConnecting[1]]
atom3 = self.AllAtoms[atom.IndeciesOfAtomsConnecting[2]]
angle1 = self.functions.angle_between_three_points(atom1.coordinates, nitrogen.coordinates, atom2.coordinates) * 180.0 / math.pi
angle2 = self.functions.angle_between_three_points(atom1.coordinates, nitrogen.coordinates, atom3.coordinates) * 180.0 / math.pi
angle3 = self.functions.angle_between_three_points(atom2.coordinates, nitrogen.coordinates, atom3.coordinates) * 180.0 / math.pi
average_angle = (angle1 + angle2 + angle3) / 3
if math.fabs(average_angle - 109.0) < 5.0:
indexes = [atom_index]
indexes.extend(atom.IndeciesOfAtomsConnecting)
chrg = self.charged(nitrogen.coordinates, indexes, True) # so indexes added are the nitrogen and any attached atoms.
self.charges.append(chrg)
if atom.element == "C": # let's check for guanidino-like groups (actually H2N-C-NH2, where not CN3.)
if atom.NumberOfNeighbors() == 3: # the carbon has only three atoms connected to it
nitrogens = self.connected_atoms_of_given_element(atom_index,"N")
if len(nitrogens) >= 2: # so carbon is connected to at least two nitrogens
# now we need to count the number of nitrogens that are only connected to one heavy atom (the carbon)
nitrogens_to_use = []
all_connected = atom.IndeciesOfAtomsConnecting[:]
not_isolated = -1
for atmindex in nitrogens:
if len(self.connected_heavy_atoms(atmindex)) == 1:
nitrogens_to_use.append(atmindex)
all_connected.remove(atmindex)
if len(all_connected) > 0: not_isolated = all_connected[0] # get the atom that connects this charged group to the rest of the molecule, ultimately to make sure it's sp3 hybridized
if len(nitrogens_to_use) == 2 and not_isolated != -1: # so there are at two nitrogens that are only connected to the carbon (and probably some hydrogens)
# now you need to make sure not_isolated atom is sp3 hybridized
not_isolated_atom = self.AllAtoms[not_isolated]
if (not_isolated_atom.element == "C" and not_isolated_atom.NumberOfNeighbors()==4) or (not_isolated_atom.element == "O" and not_isolated_atom.NumberOfNeighbors()==2) or not_isolated_atom.element == "N" or not_isolated_atom.element == "S" or not_isolated_atom.element == "P":
pt = self.AllAtoms[nitrogens_to_use[0]].coordinates.copy_of()
pt.x = pt.x + self.AllAtoms[nitrogens_to_use[1]].coordinates.x
pt.y = pt.y + self.AllAtoms[nitrogens_to_use[1]].coordinates.y
pt.z = pt.z + self.AllAtoms[nitrogens_to_use[1]].coordinates.z
pt.x = pt.x / 2.0
pt.y = pt.y / 2.0
pt.z = pt.z / 2.0
indexes = [atom_index]
indexes.extend(nitrogens_to_use)
indexes.extend(self.connected_atoms_of_given_element(nitrogens_to_use[0],"H"))
indexes.extend(self.connected_atoms_of_given_element(nitrogens_to_use[1],"H"))
chrg = self.charged(pt, indexes, True) # True because it's positive
self.charges.append(chrg)
if atom.element == "C": # let's check for a carboxylate
if atom.NumberOfNeighbors() == 3: # a carboxylate carbon will have three items connected to it.
oxygens = self.connected_atoms_of_given_element(atom_index,"O")
if len(oxygens) == 2: # a carboxylate will have two oxygens connected to it.
# now, each of the oxygens should be connected to only one heavy atom (so if it's connected to a hydrogen, that's okay)
if len(self.connected_heavy_atoms(oxygens[0])) == 1 and len(self.connected_heavy_atoms(oxygens[1])) == 1:
# so it's a carboxylate! Add a negative charge.
pt = self.AllAtoms[oxygens[0]].coordinates.copy_of()
pt.x = pt.x + self.AllAtoms[oxygens[1]].coordinates.x
pt.y = pt.y + self.AllAtoms[oxygens[1]].coordinates.y
pt.z = pt.z + self.AllAtoms[oxygens[1]].coordinates.z
pt.x = pt.x / 2.0
pt.y = pt.y / 2.0
pt.z = pt.z / 2.0
chrg = self.charged(pt, [oxygens[0], atom_index, oxygens[1]], False)
self.charges.append(chrg)
if atom.element == "P": # let's check for a phosphate or anything where a phosphorus is bound to two oxygens where both oxygens are bound to only one heavy atom (the phosphorus). I think this will get several phosphorus substances.
oxygens = self.connected_atoms_of_given_element(atom_index,"O")
if len(oxygens) >=2: # the phosphorus is bound to at least two oxygens
# now count the number of oxygens that are only bound to the phosphorus
count = 0
for oxygen_index in oxygens:
if len(self.connected_heavy_atoms(oxygen_index)) == 1: count = count + 1
if count >=2: # so there are at least two oxygens that are only bound to the phosphorus
indexes = [atom_index]
indexes.extend(oxygens)
chrg = self.charged(atom.coordinates, indexes, False)
self.charges.append(chrg)
if atom.element == "S": # let's check for a sulfonate or anything where a sulfur is bound to at least three oxygens and at least three are bound to only the sulfur (or the sulfur and a hydrogen).
oxygens = self.connected_atoms_of_given_element(atom_index,"O")
if len(oxygens) >=3: # the sulfur is bound to at least three oxygens
# now count the number of oxygens that are only bound to the sulfur
count = 0
for oxygen_index in oxygens:
if len(self.connected_heavy_atoms(oxygen_index)) == 1: count = count + 1
if count >=3: # so there are at least three oxygens that are only bound to the sulfur
indexes = [atom_index]
indexes.extend(oxygens)
chrg = self.charged(atom.coordinates, indexes, False)
self.charges.append(chrg)
# Now that you've found all the positive charges in non-protein residues, it's time to look for aromatic rings in protein residues
curr_res = ""
first = True
residue = []
for atom_index in self.AllAtoms:
atom = self.AllAtoms[atom_index]
key = atom.residue + "_" + str(atom.resid) + "_" + atom.chain
if first is True:
curr_res = key
first = False
if key != curr_res:
self.assign_charged_from_protein_process_residue(residue, last_key)
residue = []
curr_res = key
residue.append(atom_index)
last_key = key
self.assign_charged_from_protein_process_residue(residue, last_key)
def assign_charged_from_protein_process_residue(self, residue, last_key):
temp = last_key.strip().split("_")
resname = temp[0]
real_resname = resname[-3:]
resid = temp[1]
chain = temp[2]
if real_resname == "LYS" or real_resname == "LYN": # regardless of protonation state, assume it's charged.
for index in residue:
atom = self.AllAtoms[index]
if atom.atomname.strip() == "NZ":
# quickly go through the residue and get the hydrogens attached to this nitrogen to include in the index list
indexes = [index]
for index2 in residue:
atom2 = self.AllAtoms[index2]
if atom2.atomname.strip() == "HZ1": indexes.append(index2)
if atom2.atomname.strip() == "HZ2": indexes.append(index2)
if atom2.atomname.strip() == "HZ3": indexes.append(index2)
chrg = self.charged(atom.coordinates, indexes, True)
self.charges.append(chrg)
break
if real_resname == "ARG":
charge_pt = point(0.0,0.0,0.0)
count = 0.0
indices = []
for index in residue:
atom = self.AllAtoms[index]
if atom.atomname.strip() == "NH1":
charge_pt.x = charge_pt.x + atom.coordinates.x
charge_pt.y = charge_pt.y + atom.coordinates.y
charge_pt.z = charge_pt.z + atom.coordinates.z
indices.append(index)
count = count + 1.0
if atom.atomname.strip() == "NH2":
charge_pt.x = charge_pt.x + atom.coordinates.x
charge_pt.y = charge_pt.y + atom.coordinates.y
charge_pt.z = charge_pt.z + atom.coordinates.z
indices.append(index)
count = count + 1.0
if atom.atomname.strip() == "2HH2": indices.append(index)
if atom.atomname.strip() == "1HH2": indices.append(index)
if atom.atomname.strip() == "CZ": indices.append(index)
if atom.atomname.strip() == "2HH1": indices.append(index)
if atom.atomname.strip() == "1HH1": indices.append(index)
if count != 0.0:
charge_pt.x = charge_pt.x / count
charge_pt.y = charge_pt.y / count
charge_pt.z = charge_pt.z / count
if charge_pt.x != 0.0 or charge_pt.y != 0.0 or charge_pt.z != 0.0:
chrg = self.charged(charge_pt, indices, True)
self.charges.append(chrg)
if real_resname == "HIS" or real_resname == "HID" or real_resname == "HIE" or real_resname == "HIP": # regardless of protonation state, assume it's charged. This based on "The Cation-Pi Interaction," which suggests protonated state would be stabalized. But let's not consider HIS when doing salt bridges.
charge_pt = point(0.0,0.0,0.0)
count = 0.0
indices = []
for index in residue:
atom = self.AllAtoms[index]
if atom.atomname.strip() == "NE2":
charge_pt.x = charge_pt.x + atom.coordinates.x
charge_pt.y = charge_pt.y + atom.coordinates.y
charge_pt.z = charge_pt.z + atom.coordinates.z
indices.append(index)
count = count + 1.0
if atom.atomname.strip() == "ND1":
charge_pt.x = charge_pt.x + atom.coordinates.x
charge_pt.y = charge_pt.y + atom.coordinates.y
charge_pt.z = charge_pt.z + atom.coordinates.z
indices.append(index)
count = count + 1.0
if atom.atomname.strip() == "HE2": indices.append(index)
if atom.atomname.strip() == "HD1": indices.append(index)
if atom.atomname.strip() == "CE1": indices.append(index)
if atom.atomname.strip() == "CD2": indices.append(index)
if atom.atomname.strip() == "CG": indices.append(index)
if count != 0.0:
charge_pt.x = charge_pt.x / count
charge_pt.y = charge_pt.y / count
charge_pt.z = charge_pt.z / count
if charge_pt.x != 0.0 or charge_pt.y != 0.0 or charge_pt.z != 0.0:
chrg = self.charged(charge_pt, indices, True)
self.charges.append(chrg)
if real_resname == "GLU" or real_resname == "GLH" or real_resname == "GLX": # regardless of protonation state, assume it's charged. This based on "The Cation-Pi Interaction," which suggests protonated state would be stabalized.
charge_pt = point(0.0,0.0,0.0)
count = 0.0
indices = []
for index in residue:
atom = self.AllAtoms[index]
if atom.atomname.strip() == "OE1":
charge_pt.x = charge_pt.x + atom.coordinates.x
charge_pt.y = charge_pt.y + atom.coordinates.y
charge_pt.z = charge_pt.z + atom.coordinates.z
indices.append(index)
count = count + 1.0
if atom.atomname.strip() == "OE2":
charge_pt.x = charge_pt.x + atom.coordinates.x
charge_pt.y = charge_pt.y + atom.coordinates.y
charge_pt.z = charge_pt.z + atom.coordinates.z
indices.append(index)
count = count + 1.0
if atom.atomname.strip() == "CD": indices.append(index)
if count != 0.0:
charge_pt.x = charge_pt.x / count
charge_pt.y = charge_pt.y / count
charge_pt.z = charge_pt.z / count
if charge_pt.x != 0.0 or charge_pt.y != 0.0 or charge_pt.z != 0.0:
chrg = self.charged(charge_pt, indices, False) # False because it's a negative charge
self.charges.append(chrg)
if real_resname == "ASP" or real_resname == "ASH" or real_resname == "ASX": # regardless of protonation state, assume it's charged. This based on "The Cation-Pi Interaction," which suggests protonated state would be stabalized.
charge_pt = point(0.0,0.0,0.0)
count = 0.0
indices = []
for index in residue:
atom = self.AllAtoms[index]
if atom.atomname.strip() == "OD1":
charge_pt.x = charge_pt.x + atom.coordinates.x
charge_pt.y = charge_pt.y + atom.coordinates.y
charge_pt.z = charge_pt.z + atom.coordinates.z
indices.append(index)
count = count + 1.0
if atom.atomname.strip() == "OD2":
charge_pt.x = charge_pt.x + atom.coordinates.x
charge_pt.y = charge_pt.y + atom.coordinates.y
charge_pt.z = charge_pt.z + atom.coordinates.z
indices.append(index)
count = count + 1.0
if atom.atomname.strip() == "CG": indices.append(index)
if count != 0.0:
charge_pt.x = charge_pt.x / count
charge_pt.y = charge_pt.y / count
charge_pt.z = charge_pt.z / count
if charge_pt.x != 0.0 or charge_pt.y != 0.0 or charge_pt.z != 0.0:
chrg = self.charged(charge_pt, indices, False) # False because it's a negative charge
self.charges.append(chrg)
class charged():
def __init__(self, coordinates, indices, positive):
self.coordinates = coordinates
self.indices = indices
self.positive = positive # true or false to specifiy if positive or negative charge
# Functions to identify aromatic rings
# ====================================
def add_aromatic_marker(self, indicies_of_ring):
# first identify the center point
points_list = []
total = len(indicies_of_ring)
x_sum = 0.0
y_sum = 0.0
z_sum = 0.0
for index in indicies_of_ring:
atom = self.AllAtoms[index]
points_list.append(atom.coordinates)
x_sum = x_sum + atom.coordinates.x
y_sum = y_sum + atom.coordinates.y
z_sum = z_sum + atom.coordinates.z
if total == 0: return # to prevent errors in some cases
center = point(x_sum / total, y_sum / total, z_sum / total)
# now get the radius of the aromatic ring
radius = 0.0
for index in indicies_of_ring:
atom = self.AllAtoms[index]
dist = center.dist_to(atom.coordinates)
if dist > radius: radius = dist
# now get the plane that defines this ring
if len(indicies_of_ring) < 3:
return # to prevent an error in some cases. If there aren't three point, you can't define a plane
elif len(indicies_of_ring) == 3:
A = self.AllAtoms[indicies_of_ring[0]].coordinates
B = self.AllAtoms[indicies_of_ring[1]].coordinates
C = self.AllAtoms[indicies_of_ring[2]].coordinates
elif len(indicies_of_ring) == 4:
A = self.AllAtoms[indicies_of_ring[0]].coordinates
B = self.AllAtoms[indicies_of_ring[1]].coordinates
C = self.AllAtoms[indicies_of_ring[3]].coordinates
else: # best, for 5 and 6 member rings
A = self.AllAtoms[indicies_of_ring[0]].coordinates
B = self.AllAtoms[indicies_of_ring[2]].coordinates
C = self.AllAtoms[indicies_of_ring[4]].coordinates
AB = self.functions.vector_subtraction(B,A)
AC = self.functions.vector_subtraction(C,A)
ABXAC = self.functions.CrossProduct(AB,AC)
# formula for plane will be ax + by + cz = d
x1 = self.AllAtoms[indicies_of_ring[0]].coordinates.x
y1 = self.AllAtoms[indicies_of_ring[0]].coordinates.y
z1 = self.AllAtoms[indicies_of_ring[0]].coordinates.z
a = ABXAC.x
b = ABXAC.y
c = ABXAC.z
d = a*x1 + b*y1 + c*z1
ar_ring = self.aromatic_ring(center, indicies_of_ring, [a,b,c,d], radius)
self.aromatic_rings.append(ar_ring)
class aromatic_ring():
def __init__(self, center, indices, plane_coeff, radius):
self.center = center
self.indices = indices
self.plane_coeff = plane_coeff # a*x + b*y + c*z = dI think that
self.radius = radius
def assign_aromatic_rings(self):
# Get all the rings containing each of the atoms in the ligand
AllRings = []
for atom_index in self.NonProteinAtoms:
AllRings.extend(self.all_rings_containing_atom(atom_index))
for ring_index_1 in range(len(AllRings)):
ring1 = AllRings[ring_index_1]
if len(ring1) != 0:
for ring_index_2 in range(len(AllRings)):
if ring_index_1 != ring_index_2:
ring2 = AllRings[ring_index_2]
if len(ring2) != 0:
if self.set1_is_subset_of_set2(ring1, ring2) is True: AllRings[ring_index_2] = []
while [] in AllRings: AllRings.remove([])
# Now we need to figure out which of these ligands are aromatic (planar)
for ring_index in range(len(AllRings)):
ring = AllRings[ring_index]
is_flat = True
for t in range(-3, len(ring)-3):
pt1 = self.NonProteinAtoms[ring[t]].coordinates
pt2 = self.NonProteinAtoms[ring[t+1]].coordinates
pt3 = self.NonProteinAtoms[ring[t+2]].coordinates
pt4 = self.NonProteinAtoms[ring[t+3]].coordinates
# first, let's see if the last atom in this ring is a carbon connected to four atoms. That would be a quick way of telling this is not an aromatic ring
cur_atom = self.NonProteinAtoms[ring[t+3]]
if cur_atom.element == "C" and cur_atom.NumberOfNeighbors() == 4:
is_flat = False
break
# now check the dihedral between the ring atoms to see if it's flat
angle = self.functions.dihedral(pt1, pt2, pt3, pt4) * 180 / math.pi
if (angle > -165 and angle < -15) or (angle > 15 and angle < 165): # 15 degress is the cutoff #, ring[t], ring[t+1], ring[t+2], ring[t+3] # range of this function is -pi to pi
is_flat = False
break
# now check the dihedral between the ring atoms and an atom connected to the current atom to see if that's flat too.
for substituent_atom_index in cur_atom.IndeciesOfAtomsConnecting:
pt_sub = self.NonProteinAtoms[substituent_atom_index].coordinates
angle = self.functions.dihedral(pt2, pt3, pt4, pt_sub) * 180 / math.pi
if (angle > -165 and angle < -15) or (angle > 15 and angle < 165): # 15 degress is the cutoff #, ring[t], ring[t+1], ring[t+2], ring[t+3] # range of this function is -pi to pi
is_flat = False
break
if is_flat is False: AllRings[ring_index] = []
if len(ring) < 5: AllRings[ring_index] = [] # While I'm at it, three and four member rings are not aromatic
if len(ring) > 6: AllRings[ring_index] = [] # While I'm at it, if the ring has more than 6, also throw it out. So only 5 and 6 member rings are allowed.
while [] in AllRings: AllRings.remove([])
for ring in AllRings:
self.add_aromatic_marker(ring)
# Now that you've found all the rings in non-protein residues, it's time to look for aromatic rings in protein residues
curr_res = ""
first = True
residue = []
for atom_index in self.AllAtoms:
atom = self.AllAtoms[atom_index]
key = atom.residue + "_" + str(atom.resid) + "_" + atom.chain
if first is True:
curr_res = key
first = False
if key != curr_res:
self.assign_aromatic_rings_from_protein_process_residue(residue, last_key)
residue = []
curr_res = key
residue.append(atom_index)
last_key = key
self.assign_aromatic_rings_from_protein_process_residue(residue, last_key)
def assign_aromatic_rings_from_protein_process_residue(self, residue, last_key):
temp = last_key.strip().split("_")
resname = temp[0]
real_resname = resname[-3:]
resid = temp[1]
chain = temp[2]
if real_resname == "PHE":
indicies_of_ring = []
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CG": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CD1": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CE1": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CZ": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CE2": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CD2": indicies_of_ring.append(index)
self.add_aromatic_marker(indicies_of_ring)
if real_resname == "TYR":
indicies_of_ring = []
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CG": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CD1": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CE1": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CZ": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CE2": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CD2": indicies_of_ring.append(index)
self.add_aromatic_marker(indicies_of_ring)
if real_resname == "HIS" or real_resname == "HID" or real_resname == "HIE" or real_resname == "HIP":
indicies_of_ring = []
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CG": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "ND1": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CE1": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "NE2": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CD2": indicies_of_ring.append(index)
self.add_aromatic_marker(indicies_of_ring)
if real_resname == "TRP":
indicies_of_ring = []
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CG": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CD1": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "NE1": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CE2": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CD2": indicies_of_ring.append(index)
self.add_aromatic_marker(indicies_of_ring)
indicies_of_ring = []
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CE2": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CD2": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CE3": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CZ3": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CH2": indicies_of_ring.append(index)
for index in residue: # written this way because order is important
atom = self.AllAtoms[index]
if atom.atomname.strip() == "CZ2": indicies_of_ring.append(index)
self.add_aromatic_marker(indicies_of_ring)
def set1_is_subset_of_set2(self, set1, set2):
is_subset = True
for item in set1:
if not item in set2:
is_subset = False
break
return is_subset
def all_rings_containing_atom(self, index):
AllRings = []
atom = self.AllAtoms[index]
for conneceted_atom in atom.IndeciesOfAtomsConnecting:
self.ring_recursive(conneceted_atom, [index], index, AllRings)
return AllRings
def ring_recursive(self, index, AlreadyCrossed, orig_atom, AllRings):
if len(AlreadyCrossed) > 6: return # since you're only considering aromatic rings containing 5 or 6 members anyway, save yourself some time.
atom = self.AllAtoms[index]
temp = AlreadyCrossed[:]
temp.append(index)
for conneceted_atom in atom.IndeciesOfAtomsConnecting:
if not conneceted_atom in AlreadyCrossed:
self.ring_recursive(conneceted_atom, temp, orig_atom, AllRings)
if conneceted_atom == orig_atom and orig_atom != AlreadyCrossed[-1]:
AllRings.append(temp)
# Functions to assign secondary structure to protein residues
# ===========================================================
def assign_secondary_structure(self):
# first, we need to know what resid's are available
resids = []
last_key = "-99999_Z"
for atom_index in self.AllAtoms:
atom = self.AllAtoms[atom_index]
key = str(atom.resid) + "_" + atom.chain
if key != last_key:
last_key = key
resids.append(last_key)
structure = {}
for resid in resids:
structure[resid] = "OTHER"
atoms = []
for atom_index in self.AllAtoms:
atom = self.AllAtoms[atom_index]
if atom.SideChainOrBackBone() == "BACKBONE":
if len(atoms) < 8:
atoms.append(atom)
else:
atoms.pop(0)
atoms.append(atom)
# now make sure the first four all have the same resid and the last four all have the same resid
if atoms[0].resid == atoms[1].resid and atoms[0].resid == atoms[2].resid and atoms[0].resid == atoms[3].resid and atoms[0] != atoms[4].resid and atoms[4].resid == atoms[5].resid and atoms[4].resid == atoms[6].resid and atoms[4].resid == atoms[7].resid and atoms[0].resid + 1 == atoms[7].resid and atoms[0].chain == atoms[7].chain:
resid1 = atoms[0].resid
resid2 = atoms[7].resid
# Now give easier to use names to the atoms
for atom in atoms:
if atom.resid == resid1 and atom.atomname.strip() == "N": first_N = atom
if atom.resid == resid1 and atom.atomname.strip() == "C": first_C = atom
if atom.resid == resid1 and atom.atomname.strip() == "CA": first_CA = atom
if atom.resid == resid2 and atom.atomname.strip() == "N": second_N = atom
if atom.resid == resid2 and atom.atomname.strip() == "C": second_C = atom
if atom.resid == resid2 and atom.atomname.strip() == "CA": second_CA = atom
# Now compute the phi and psi dihedral angles
phi = self.functions.dihedral(first_C.coordinates, second_N.coordinates, second_CA.coordinates, second_C.coordinates) * 180.0 / math.pi
psi = self.functions.dihedral(first_N.coordinates, first_CA.coordinates, first_C.coordinates, second_N.coordinates) * 180.0 / math.pi
# Now use those angles to determine if it's alpha or beta
if phi > -145 and phi < -35 and psi > -70 and psi < 50:
key1 = str(first_C.resid) + "_" + first_C.chain
key2 = str(second_C.resid) + "_" + second_C.chain
structure[key1] = "ALPHA"
structure[key2] = "ALPHA"
if (phi >= -180 and phi < -40 and psi <= 180 and psi > 90) or (phi >= -180 and phi < -70 and psi <= -165): # beta. This gets some loops (by my eye), but it's the best I could do.
key1 = str(first_C.resid) + "_" + first_C.chain
key2 = str(second_C.resid) + "_" + second_C.chain
structure[key1] = "BETA"
structure[key2] = "BETA"
# Now update each of the atoms with this structural information
for atom_index in self.AllAtoms:
atom = self.AllAtoms[atom_index]
key = str(atom.resid) + "_" + atom.chain
atom.structure = structure[key]
# Some more post processing.
CA_list = [] # first build a list of the indices of all the alpha carbons
for atom_index in self.AllAtoms:
atom = self.AllAtoms[atom_index]
if atom.residue.strip() in self.protein_resnames and atom.atomname.strip() == "CA": CA_list.append(atom_index)
# some more post processing.
change = True
while change is True:
change = False
# A residue of index i is only going to be in an alpha helix its CA is within 6 A of the CA of the residue i + 3
for CA_atom_index in CA_list:
CA_atom = self.AllAtoms[CA_atom_index]
if CA_atom.structure == "ALPHA": # so it's in an alpha helix
another_alpha_is_close = False
for other_CA_atom_index in CA_list: # so now compare that CA to all the other CA's
other_CA_atom = self.AllAtoms[other_CA_atom_index]
if other_CA_atom.structure == "ALPHA": # so it's also in an alpha helix
if other_CA_atom.resid - 3 == CA_atom.resid or other_CA_atom.resid + 3 == CA_atom.resid: # so this CA atom is one of the ones the first atom might hydrogen bond with
if other_CA_atom.coordinates.dist_to(CA_atom.coordinates) < 6.0: # so these two CA atoms are close enough together that their residues are probably hydrogen bonded
another_alpha_is_close = True
break
if another_alpha_is_close is False:
self.set_structure_of_residue(CA_atom.chain, CA_atom.resid, "OTHER")
change = True
# Alpha helices are only alpha helices if they span at least 4 residues (to wrap around and hydrogen bond). I'm going to require them to span at least 5 residues, based on examination of many structures.
for index_in_list in range(len(CA_list)-5):
index_in_pdb1 = CA_list[index_in_list]
index_in_pdb2 = CA_list[index_in_list+1]
index_in_pdb3 = CA_list[index_in_list+2]
index_in_pdb4 = CA_list[index_in_list+3]
index_in_pdb5 = CA_list[index_in_list+4]
index_in_pdb6 = CA_list[index_in_list+5]
atom1 = self.AllAtoms[index_in_pdb1]
atom2 = self.AllAtoms[index_in_pdb2]
atom3 = self.AllAtoms[index_in_pdb3]
atom4 = self.AllAtoms[index_in_pdb4]
atom5 = self.AllAtoms[index_in_pdb5]
atom6 = self.AllAtoms[index_in_pdb6]
if atom1.resid + 1 == atom2.resid and atom2.resid + 1 == atom3.resid and atom3.resid + 1 == atom4.resid and atom4.resid + 1 == atom5.resid and atom5.resid + 1 == atom6.resid: # so they are sequential
if atom1.structure != "ALPHA" and atom2.structure == "ALPHA" and atom3.structure != "ALPHA":
self.set_structure_of_residue(atom2.chain, atom2.resid, "OTHER")
change = True
if atom2.structure != "ALPHA" and atom3.structure == "ALPHA" and atom4.structure != "ALPHA":
self.set_structure_of_residue(atom3.chain, atom3.resid, "OTHER")
change = True
if atom3.structure != "ALPHA" and atom4.structure == "ALPHA" and atom5.structure != "ALPHA":
self.set_structure_of_residue(atom4.chain, atom4.resid, "OTHER")
change = True
if atom4.structure != "ALPHA" and atom5.structure == "ALPHA" and atom6.structure != "ALPHA":
self.set_structure_of_residue(atom5.chain, atom5.resid, "OTHER")
change = True
if atom1.structure != "ALPHA" and atom2.structure == "ALPHA" and atom3.structure == "ALPHA" and atom4.structure != "ALPHA":
self.set_structure_of_residue(atom2.chain, atom2.resid, "OTHER")
self.set_structure_of_residue(atom3.chain, atom3.resid, "OTHER")
change = True
if atom2.structure != "ALPHA" and atom3.structure == "ALPHA" and atom4.structure == "ALPHA" and atom5.structure != "ALPHA":
self.set_structure_of_residue(atom3.chain, atom3.resid, "OTHER")
self.set_structure_of_residue(atom4.chain, atom4.resid, "OTHER")
change = True
if atom3.structure != "ALPHA" and atom4.structure == "ALPHA" and atom5.structure == "ALPHA" and atom6.structure != "ALPHA":
self.set_structure_of_residue(atom4.chain, atom4.resid, "OTHER")
self.set_structure_of_residue(atom5.chain, atom5.resid, "OTHER")
change = True
if atom1.structure != "ALPHA" and atom2.structure == "ALPHA" and atom3.structure == "ALPHA" and atom4.structure == "ALPHA" and atom5.structure != "ALPHA":
self.set_structure_of_residue(atom2.chain, atom2.resid, "OTHER")
self.set_structure_of_residue(atom3.chain, atom3.resid, "OTHER")
self.set_structure_of_residue(atom4.chain, atom4.resid, "OTHER")
change = True
if atom2.structure != "ALPHA" and atom3.structure == "ALPHA" and atom4.structure == "ALPHA" and atom5.structure == "ALPHA" and atom6.structure != "ALPHA":
self.set_structure_of_residue(atom3.chain, atom3.resid, "OTHER")
self.set_structure_of_residue(atom4.chain, atom4.resid, "OTHER")
self.set_structure_of_residue(atom5.chain, atom5.resid, "OTHER")
change = True
if atom1.structure != "ALPHA" and atom2.structure == "ALPHA" and atom3.structure == "ALPHA" and atom4.structure == "ALPHA" and atom5.structure == "ALPHA" and atom6.structure != "ALPHA":
self.set_structure_of_residue(atom2.chain, atom2.resid, "OTHER")
self.set_structure_of_residue(atom3.chain, atom3.resid, "OTHER")
self.set_structure_of_residue(atom4.chain, atom4.resid, "OTHER")
self.set_structure_of_residue(atom5.chain, atom5.resid, "OTHER")
change = True
# now go through each of the BETA CA atoms. A residue is only going to be called a beta sheet if CA atom is within 6.0 A of another CA beta, same chain, but index difference > 2.
for CA_atom_index in CA_list:
CA_atom = self.AllAtoms[CA_atom_index]
if CA_atom.structure == "BETA": # so it's in a beta sheet
another_beta_is_close = False
for other_CA_atom_index in CA_list:
if other_CA_atom_index != CA_atom_index: # so not comparing an atom to itself
other_CA_atom = self.AllAtoms[other_CA_atom_index]
if other_CA_atom.structure == "BETA": # so you're comparing it only to other BETA-sheet atoms
if other_CA_atom.chain == CA_atom.chain: # so require them to be on the same chain. needed to indecies can be fairly compared
if math.fabs(other_CA_atom.resid - CA_atom.resid) > 2: # so the two residues are not simply adjacent to each other on the chain
if CA_atom.coordinates.dist_to(other_CA_atom.coordinates) < 6.0: # so these to atoms are close to each other
another_beta_is_close = True
break
if another_beta_is_close is False:
self.set_structure_of_residue(CA_atom.chain, CA_atom.resid, "OTHER")
change = True
# Now some more post-processing needs to be done. Do this again to clear up mess that may have just been created (single residue beta strand, for example)
# Beta sheets are usually at least 3 residues long
for index_in_list in range(len(CA_list)-3):
index_in_pdb1 = CA_list[index_in_list]
index_in_pdb2 = CA_list[index_in_list+1]
index_in_pdb3 = CA_list[index_in_list+2]
index_in_pdb4 = CA_list[index_in_list+3]
atom1 = self.AllAtoms[index_in_pdb1]
atom2 = self.AllAtoms[index_in_pdb2]
atom3 = self.AllAtoms[index_in_pdb3]
atom4 = self.AllAtoms[index_in_pdb4]
if atom1.resid + 1 == atom2.resid and atom2.resid + 1 == atom3.resid and atom3.resid + 1 == atom4.resid: # so they are sequential
if atom1.structure != "BETA" and atom2.structure == "BETA" and atom3.structure != "BETA":
self.set_structure_of_residue(atom2.chain, atom2.resid, "OTHER")
change = True
if atom2.structure != "BETA" and atom3.structure == "BETA" and atom4.structure != "BETA":
self.set_structure_of_residue(atom3.chain, atom3.resid, "OTHER")
change = True
if atom1.structure != "BETA" and atom2.structure == "BETA" and atom3.structure == "BETA" and atom4.structure != "BETA":
self.set_structure_of_residue(atom2.chain, atom2.resid, "OTHER")
self.set_structure_of_residue(atom3.chain, atom3.resid, "OTHER")
change = True
def set_structure_of_residue(self, chain, resid, structure):
for atom_index in self.AllAtoms:
atom = self.AllAtoms[atom_index]
if atom.chain == chain and atom.resid == resid:
atom.structure = structure
class MathFunctions:
def vector_subtraction(self, vector1, vector2): # vector1 - vector2
return point(vector1.x - vector2.x, vector1.y - vector2.y, vector1.z - vector2.z)
def CrossProduct(self, Pt1, Pt2): # never tested
Response = point(0,0,0)
Response.x = Pt1.y * Pt2.z - Pt1.z * Pt2.y
Response.y = Pt1.z * Pt2.x - Pt1.x * Pt2.z
Response.z = Pt1.x * Pt2.y - Pt1.y * Pt2.x
return Response;
def vector_scalar_multiply(self, vector, scalar):
return point(vector.x * scalar, vector.y * scalar, vector.z * scalar)
def dot_product(self, point1, point2):
return point1.x * point2.x + point1.y * point2.y + point1.z * point2.z
def dihedral(self, point1, point2, point3, point4): # never tested
b1 = self.vector_subtraction(point2, point1)
b2 = self.vector_subtraction(point3, point2)
b3 = self.vector_subtraction(point4, point3)
b2Xb3 = self.CrossProduct(b2,b3)
b1Xb2 = self.CrossProduct(b1,b2)
b1XMagb2 = self.vector_scalar_multiply(b1,b2.Magnitude())
radians = math.atan2(self.dot_product(b1XMagb2,b2Xb3), self.dot_product(b1Xb2,b2Xb3))
return radians
def angle_between_three_points(self, point1, point2, point3): # As in three connected atoms
vector1 = self.vector_subtraction(point1, point2)
vector2 = self.vector_subtraction(point3, point2)
return self.angle_between_points(vector1, vector2)
def angle_between_points(self, point1, point2):
new_point1 = self.return_normalized_vector(point1)
new_point2 = self.return_normalized_vector(point2)
dot_prod = self.dot_product(new_point1, new_point2)
if dot_prod > 1.0: dot_prod = 1.0 # to prevent errors that can rarely occur
if dot_prod < -1.0: dot_prod = -1.0
return math.acos(dot_prod)
def return_normalized_vector(self, vector):
dist = self.distance(point(0,0,0), vector)
return point(vector.x/dist, vector.y/dist, vector.z/dist)
def distance(self, point1, point2):
deltax = point1.x - point2.x
deltay = point1.y - point2.y
deltaz = point1.z - point2.z
return math.sqrt(math.pow(deltax,2) + math.pow(deltay,2) + math.pow(deltaz,2))
def project_point_onto_plane(self, apoint, plane_coefficients): # essentially finds the point on the plane that is closest to the specified point
# the plane_coefficients are [a,b,c,d], where the plane is ax + by + cz = d
# First, define a plane using cooeficients a, b, c, d such that ax + by + cz = d
a = plane_coefficients[0]
b = plane_coefficients[1]
c = plane_coefficients[2]
d = plane_coefficients[3]
# Now, define a point in space (s,u,v)
s = apoint.x
u = apoint.y
v = apoint.z
# the formula of a line perpendicular to the plan passing through (s,u,v) is:
#x = s + at
#y = u + bt
#z = v + ct
t = (d - a*s - b*u - c*v) / (a*a + b*b + c*c)
# here's the point closest on the plane
x = s + a*t
y = u + b*t
z = v + c*t
return point(x,y,z)
def getCommandOutput2(command):
child = os.popen(command)
data = child.read()
err = child.close()
if err:
raise RuntimeError('%s failed w/ exit code %d' % (command, err))
return data
class binana:
functions = MathFunctions()
# supporting functions
def list_alphebetize_and_combine(self, list):
list.sort()
return '_'.join(list)
def hashtable_entry_add_one(self, hashtable, key, toadd = 1): # note that dictionaries (hashtables) are passed by reference in python
if key in hashtable:
hashtable[key] = hashtable[key] + toadd
else:
hashtable[key] = toadd
def extend_list_by_dictionary(self, input_list, dictionary):
# first, sort the dictionary by the key
keys = dictionary.keys()
keys = list(keys)
keys.sort()
# now make a list of the values
vals = []
for key in keys:
vals.append(dictionary[key])
# now append vals to the list
newlist = input_list[:]
newlist.extend(vals)
# return the extended list
return newlist
def center(self, string, length):
while len(string) < length:
string = " " + string
if len(string) < length:
string = string + " "
return string
# The meat of the class
#def __init__(self, ligand_pdbqt_filename, receptor_pdbqt_filename, parameters, line_header, actual_filename_if_ligand_is_list="", actual_filename_if_receptor_is_list=""): # must be a more elegant way of doing this
def __init__(self, ligand_pdbqt_filename, receptor, parameters, line_header, actual_filename_if_ligand_is_list="", actual_filename_if_receptor_is_list=""): # must be a more elegant way of doing this
receptor_pdbqt_filename = receptor.OrigFileName
ligand = PDB()
if actual_filename_if_ligand_is_list!="": # so a list was passed as the ligand
ligand.LoadPDB_from_list(ligand_pdbqt_filename, line_header)
# now write the file so when VINA is run it has a ligand file for input
f = open(actual_filename_if_ligand_is_list,'w')
for line in ligand_pdbqt_filename:
if not "MODEL" in line and not "ENDMDL" in line: f.write(line)
f.close()
ligand_pdbqt_filename = actual_filename_if_ligand_is_list
else: # so a filename was passed as the ligand
ligand.LoadPDB_from_file(ligand_pdbqt_filename, line_header)
#receptor = PDB()
#if actual_filename_if_ligand_is_list=="": # receptor is a filename instead of a list
# receptor.LoadPDB_from_file(receptor_pdbqt_filename, line_header)
#else: # so it's a list that was passed, not a filename
# receptor.LoadPDB_from_list(receptor_pdbqt_filename, line_header)
# receptor_pdbqt_filename = actual_filename_if_receptor_is_list
receptor.assign_secondary_structure()
# Get distance measurements between protein and ligand atom types, as well as some other measurements
ligand_receptor_atom_type_pairs_less_than_two_half = {}
ligand_receptor_atom_type_pairs_less_than_four = {}
ligand_receptor_atom_type_pairs_electrostatic = {}
active_site_flexibility = {}
hbonds = {}
hydrophobics = {}
ligand.rotateable_bonds_count
functions = MathFunctions()
pdb_close_contacts = PDB()
pdb_contacts = PDB()
pdb_contacts_alpha_helix = PDB()
pdb_contacts_beta_sheet = PDB()
pdb_contacts_other_2nd_structure = PDB()
pdb_side_chain = PDB()
pdb_back_bone = PDB()
pdb_hydrophobic = PDB()
pdb_hbonds = PDB()
for ligand_atom_index in ligand.AllAtoms:
for receptor_atom_index in receptor.AllAtoms:
ligand_atom = ligand.AllAtoms[ligand_atom_index]
receptor_atom = receptor.AllAtoms[receptor_atom_index]
dist = ligand_atom.coordinates.dist_to(receptor_atom.coordinates)
if dist < 2.5: # less than 2.5 A
list = [ligand_atom.atomtype, receptor_atom.atomtype]
self.hashtable_entry_add_one(ligand_receptor_atom_type_pairs_less_than_two_half, self.list_alphebetize_and_combine(list))
pdb_close_contacts.AddNewAtom(ligand_atom.copy_of())
pdb_close_contacts.AddNewAtom(receptor_atom.copy_of())
elif dist < 4.0: # less than 4 A
list = [ligand_atom.atomtype, receptor_atom.atomtype]
self.hashtable_entry_add_one(ligand_receptor_atom_type_pairs_less_than_four, self.list_alphebetize_and_combine(list))
pdb_contacts.AddNewAtom(ligand_atom.copy_of())
pdb_contacts.AddNewAtom(receptor_atom.copy_of())
if dist < 4.0:
# calculate electrostatic energies for all less than 4 A
ligand_charge = ligand_atom.charge
receptor_charge = receptor_atom.charge
coulomb_energy = (ligand_charge * receptor_charge / dist) * 138.94238460104697e4 # to convert into J/mol # might be nice to double check this
list = [ligand_atom.atomtype, receptor_atom.atomtype]
self.hashtable_entry_add_one(ligand_receptor_atom_type_pairs_electrostatic, self.list_alphebetize_and_combine(list), coulomb_energy)
if dist < 4.0:
# Now get statistics to judge active-site flexibility
flexibility_key = receptor_atom.SideChainOrBackBone() + "_" + receptor_atom.structure # first can be sidechain or backbone, second back be alpha, beta, or other, so six catagories
if receptor_atom.structure == "ALPHA": pdb_contacts_alpha_helix.AddNewAtom(receptor_atom.copy_of())
elif receptor_atom.structure == "BETA": pdb_contacts_beta_sheet.AddNewAtom(receptor_atom.copy_of())
elif receptor_atom.structure == "OTHER": pdb_contacts_other_2nd_structure.AddNewAtom(receptor_atom.copy_of())
if receptor_atom.SideChainOrBackBone() == "BACKBONE": pdb_back_bone.AddNewAtom(receptor_atom.copy_of())
elif receptor_atom.SideChainOrBackBone() == "SIDECHAIN": pdb_side_chain.AddNewAtom(receptor_atom.copy_of())
self.hashtable_entry_add_one(active_site_flexibility, flexibility_key)
if dist < 4.0:
# Now see if there's hydrophobic contacts (C-C contacts)
if ligand_atom.element == "C" and receptor_atom.element == "C":
hydrophobic_key = receptor_atom.SideChainOrBackBone() + "_" + receptor_atom.structure
pdb_hydrophobic.AddNewAtom(ligand_atom.copy_of())
pdb_hydrophobic.AddNewAtom(receptor_atom.copy_of())
self.hashtable_entry_add_one(hydrophobics, hydrophobic_key)
if dist < 4.0:
# Now see if there's some sort of hydrogen bond between these two atoms. distance cutoff = 4, angle cutoff = 40. Note that this is liberal.
if (ligand_atom.element == "O" or ligand_atom.element == "N") and (receptor_atom.element == "O" or receptor_atom.element == "N"):
# now build a list of all the hydrogens close to these atoms
hydrogens = []
for atm_index in ligand.AllAtoms:
if ligand.AllAtoms[atm_index].element == "H": # so it's a hydrogen
if ligand.AllAtoms[atm_index].coordinates.dist_to(ligand_atom.coordinates) < 1.3: # O-H distance is 0.96 A, N-H is 1.01 A. See http://www.science.uwaterloo.ca/~cchieh/cact/c120/bondel.html
ligand.AllAtoms[atm_index].comment = "LIGAND"
hydrogens.append(ligand.AllAtoms[atm_index])
for atm_index in receptor.AllAtoms:
if receptor.AllAtoms[atm_index].element == "H": # so it's a hydrogen
if receptor.AllAtoms[atm_index].coordinates.dist_to(receptor_atom.coordinates) < 1.3: # O-H distance is 0.96 A, N-H is 1.01 A. See http://www.science.uwaterloo.ca/~cchieh/cact/c120/bondel.html
receptor.AllAtoms[atm_index].comment = "RECEPTOR"
hydrogens.append(receptor.AllAtoms[atm_index])
# now we need to check the angles
for hydrogen in hydrogens:
if math.fabs(180 - functions.angle_between_three_points(ligand_atom.coordinates, hydrogen.coordinates, receptor_atom.coordinates) * 180.0 / math.pi) <= 40.0:
hbonds_key = "HDONOR_" + hydrogen.comment + "_" + receptor_atom.SideChainOrBackBone() + "_" + receptor_atom.structure
pdb_hbonds.AddNewAtom(ligand_atom.copy_of())
pdb_hbonds.AddNewAtom(hydrogen.copy_of())
pdb_hbonds.AddNewAtom(receptor_atom.copy_of())
self.hashtable_entry_add_one(hbonds, hbonds_key)
# Get the total number of each atom type in the ligand
ligand_atom_types = {}
for ligand_atom_index in ligand.AllAtoms:
ligand_atom = ligand.AllAtoms[ligand_atom_index]
self.hashtable_entry_add_one(ligand_atom_types, ligand_atom.atomtype)
pi_padding = 0.75 # This is perhaps controversial. I noticed that often a pi-cation interaction or other pi interaction was only slightly off, but looking at the structure, it was clearly supposed to be a
# pi-cation interaction. I've decided then to artificially expand the radius of each pi ring. Think of this as adding in a VDW radius, or accounting for poor crystal-structure resolution, or whatever you want
# to justify it.
# Count pi-pi stacking and pi-T stacking interactions
PI_interactions = {}
pdb_pistack = PDB()
pdb_pi_T = PDB()
# "PI-Stacking Interactions ALIVE AND WELL IN PROTEINS" says distance of 7.5 A is good cutoff. This seems really big to me, except that pi-pi interactions (parallel) are actuall usually off centered. Interesting paper.
# Note that adenine and tryptophan count as two aromatic rings. So, for example, an interaction between these two, if positioned correctly, could count for 4 pi-pi interactions.
for aromatic1 in ligand.aromatic_rings:
for aromatic2 in receptor.aromatic_rings:
dist = aromatic1.center.dist_to(aromatic2.center)
if dist < 7.5: # so there could be some pi-pi interactions.
# first, let's check for stacking interactions. Are the two pi's roughly parallel?
aromatic1_norm_vector = point(aromatic1.plane_coeff[0], aromatic1.plane_coeff[1], aromatic1.plane_coeff[2])
aromatic2_norm_vector = point(aromatic2.plane_coeff[0], aromatic2.plane_coeff[1], aromatic2.plane_coeff[2])
angle_between_planes = self.functions.angle_between_points(aromatic1_norm_vector, aromatic2_norm_vector) * 180.0/math.pi
if math.fabs(angle_between_planes-0) < 30.0 or math.fabs(angle_between_planes-180) < 30.0: # so they're more or less parallel, it's probably pi-pi stackingoutput_dir
# now, pi-pi are not usually right on top of each other. They're often staggared. So I don't want to just look at the centers of the rings and compare. Let's look at each of the atoms.
# do atom of the atoms of one ring, when projected onto the plane of the other, fall within that other ring?
pi_pi = False # start by assuming it's not a pi-pi stacking interaction
for ligand_ring_index in aromatic1.indices:
# project the ligand atom onto the plane of the receptor ring
pt_on_receptor_plane = self.functions.project_point_onto_plane(ligand.AllAtoms[ligand_ring_index].coordinates, aromatic2.plane_coeff)
if pt_on_receptor_plane.dist_to(aromatic2.center) <= aromatic2.radius + pi_padding:
pi_pi = True
break
if pi_pi is False: # if you've already determined it's a pi-pi stacking interaction, no need to keep trying
for receptor_ring_index in aromatic2.indices:
# project the ligand atom onto the plane of the receptor ring
pt_on_ligand_plane = self.functions.project_point_onto_plane(receptor.AllAtoms[receptor_ring_index].coordinates, aromatic1.plane_coeff)
if pt_on_ligand_plane.dist_to(aromatic1.center) <= aromatic1.radius + pi_padding:
pi_pi = True
break
if pi_pi is True:
structure = receptor.AllAtoms[aromatic2.indices[0]].structure
if structure == "": structure = "OTHER" # since it could be interacting with a cofactor or something
key = "STACKING_" + structure
for index in aromatic1.indices: pdb_pistack.AddNewAtom(ligand.AllAtoms[index].copy_of())
for index in aromatic2.indices: pdb_pistack.AddNewAtom(receptor.AllAtoms[index].copy_of())
self.hashtable_entry_add_one(PI_interactions, key)
elif math.fabs(angle_between_planes-90) < 30.0 or math.fabs(angle_between_planes-270) < 30.0: # so they're more or less perpendicular, it's probably a pi-edge interaction
# having looked at many structures, I noticed the algorithm was identifying T-pi reactions when the two rings were in fact quite distant, often with other atoms
# in between. Eye-balling it, requiring that at their closest they be at least 5 A apart seems to separate the good T's from the bad
min_dist = 100.0
for ligand_ind in aromatic1.indices:
ligand_at = ligand.AllAtoms[ligand_ind]
for receptor_ind in aromatic2.indices:
receptor_at = receptor.AllAtoms[receptor_ind]
dist = ligand_at.coordinates.dist_to(receptor_at.coordinates)
if dist < min_dist: min_dist = dist
if min_dist <= 5.0: # so at their closest points, the two rings come within 5 A of each other.
# okay, is the ligand pi pointing into the receptor pi, or the other way around?
# first, project the center of the ligand pi onto the plane of the receptor pi, and vs. versa
# This could be directional somehow, like a hydrogen bond.
pt_on_receptor_plane = self.functions.project_point_onto_plane(aromatic1.center, aromatic2.plane_coeff)
pt_on_lignad_plane = self.functions.project_point_onto_plane(aromatic2.center, aromatic1.plane_coeff)
# now, if it's a true pi-T interaction, this projected point should fall within the ring whose plane it's been projected into.
if (pt_on_receptor_plane.dist_to(aromatic2.center) <= aromatic2.radius + pi_padding) or (pt_on_lignad_plane.dist_to(aromatic1.center) <= aromatic1.radius + pi_padding): # so it is in the ring on the projected plane.
structure = receptor.AllAtoms[aromatic2.indices[0]].structure
if structure == "": structure = "OTHER" # since it could be interacting with a cofactor or something
key = "T-SHAPED_" + structure
for index in aromatic1.indices: pdb_pi_T.AddNewAtom(ligand.AllAtoms[index].copy_of())
for index in aromatic2.indices: pdb_pi_T.AddNewAtom(receptor.AllAtoms[index].copy_of())
self.hashtable_entry_add_one(PI_interactions, key)
# Now identify pi-cation interactions
pdb_pi_cat = PDB()
for aromatic in receptor.aromatic_rings:
for charged in ligand.charges:
if charged.positive is True: # so only consider positive charges
if charged.coordinates.dist_to(aromatic.center) < 6.0: # distance cutoff based on "Cation-pi interactions in structural biology."
# project the charged onto the plane of the aromatic
charge_projected = self.functions.project_point_onto_plane(charged.coordinates,aromatic.plane_coeff)
if charge_projected.dist_to(aromatic.center) < aromatic.radius + pi_padding:
structure = receptor.AllAtoms[aromatic.indices[0]].structure
if structure == "": structure = "OTHER" # since it could be interacting with a cofactor or something
key = "PI-CATION_LIGAND-CHARGED_" + structure
for index in aromatic.indices: pdb_pi_cat.AddNewAtom(receptor.AllAtoms[index].copy_of())
for index in charged.indices: pdb_pi_cat.AddNewAtom(ligand.AllAtoms[index].copy_of())
self.hashtable_entry_add_one(PI_interactions, key)
for aromatic in ligand.aromatic_rings: # now it's the ligand that has the aromatic group
for charged in receptor.charges:
if charged.positive is True: # so only consider positive charges
if charged.coordinates.dist_to(aromatic.center) < 6.0: # distance cutoff based on "Cation-pi interactions in structural biology."
# project the charged onto the plane of the aromatic
charge_projected = self.functions.project_point_onto_plane(charged.coordinates,aromatic.plane_coeff)
if charge_projected.dist_to(aromatic.center) < aromatic.radius + pi_padding:
structure = receptor.AllAtoms[charged.indices[0]].structure
if structure == "": structure = "OTHER" # since it could be interacting with a cofactor or something
key = "PI-CATION_RECEPTOR-CHARGED_" + structure
for index in aromatic.indices: pdb_pi_cat.AddNewAtom(ligand.AllAtoms[index].copy_of())
for index in charged.indices: pdb_pi_cat.AddNewAtom(receptor.AllAtoms[index].copy_of())
self.hashtable_entry_add_one(PI_interactions, key)
# now count the number of salt bridges
pdb_salt_bridges = PDB()
salt_bridges = {}
for receptor_charge in receptor.charges:
for ligand_charge in ligand.charges:
if ligand_charge.positive != receptor_charge.positive: # so they have oppositve charges
if ligand_charge.coordinates.dist_to(receptor_charge.coordinates) < 5.5: # 4 is good cutoff for salt bridges according to "Close-Range Electrostatic Interactions in Proteins", but looking at complexes, I decided to go with 5.5 A
structure = receptor.AllAtoms[receptor_charge.indices[0]].structure
if structure == "": structure = "OTHER" # since it could be interacting with a cofactor or something
key = "SALT-BRIDGE_" + structure
for index in receptor_charge.indices: pdb_salt_bridges.AddNewAtom(receptor.AllAtoms[index].copy_of())
for index in ligand_charge.indices: pdb_salt_bridges.AddNewAtom(ligand.AllAtoms[index].copy_of())
self.hashtable_entry_add_one(salt_bridges, key)
# Now save the files
preface ="REMARK "
# if an output directory is specified, and it doesn't exist, create it
#if parameters.params['output_dir'] != "":
# if not os.path.exists(parameters.params['output_dir']):
# os.mkdir(parameters.params['output_dir'])
# Now get vina
vina_output = getCommandOutput2(parameters.params['vina_executable'] + ' --score_only --receptor ' + receptor_pdbqt_filename + ' --ligand ' + ligand_pdbqt_filename)
print(vina_output)
vina_output = vina_output.split("\n")
vina_affinity = 0.0
vina_gauss_1 = 0.0
vina_gauss_2 = 0.0
vina_repulsion = 0.0
vina_hydrophobic = 0.0
vina_hydrogen = 0.0
for item in vina_output:
item = item.strip()
if "Affinity" in item: vina_affinity = float(item.replace("Affinity: ","").replace(" (kcal/mol)",""))
if "gauss 1" in item: vina_gauss_1 = float(item.replace("gauss 1 : ",""))
if "gauss 2" in item: vina_gauss_2 = float(item.replace("gauss 2 : ",""))
if "repulsion" in item: vina_repulsion = float(item.replace("repulsion : ",""))
if "hydrophobic" in item: vina_hydrophobic = float(item.replace("hydrophobic : ",""))
if "Hydrogen" in item: vina_hydrogen = float(item.replace("Hydrogen : ",""))
vina_output = [vina_affinity, vina_gauss_1, vina_gauss_2, vina_repulsion, vina_hydrophobic, vina_hydrogen]
stacking = []
t_shaped = []
pi_cation = []
for key in PI_interactions:
value = PI_interactions[key]
together = key + "_" + str(value) # not that this is put together strangely!!!
if "STACKING" in together: stacking.append(together)
if "CATION" in together: pi_cation.append(together)
if "SHAPED" in together: t_shaped.append(together)
# now create a single descriptor object
data = {}
data['vina_output'] = vina_output
data['ligand_receptor_atom_type_pairs_less_than_two_half'] = ligand_receptor_atom_type_pairs_less_than_two_half
data['ligand_receptor_atom_type_pairs_less_than_four'] = ligand_receptor_atom_type_pairs_less_than_four
data['ligand_atom_types'] = ligand_atom_types
data['ligand_receptor_atom_type_pairs_electrostatic'] = ligand_receptor_atom_type_pairs_electrostatic
data['rotateable_bonds_count'] = ligand.rotateable_bonds_count
data['active_site_flexibility'] = active_site_flexibility
data['hbonds'] = hbonds
data['hydrophobics'] = hydrophobics
data['stacking'] = stacking
data['pi_cation'] = pi_cation
data['t_shaped'] = t_shaped
data['salt_bridges'] = salt_bridges
self.vina_output = data['vina_output']
self.rotateable_bonds_count = {'rot_bonds':data['rotateable_bonds_count']}
self.ligand_receptor_atom_type_pairs_less_than_two_half = {"A_A": 0, "A_C": 0, "A_CL": 0, "A_F": 0, "A_FE": 0, "A_MG": 0, "A_MN": 0, "A_NA": 0, "A_SA": 0, "BR_C": 0, "BR_OA": 0, "C_CL": 0, "CD_OA": 0, "CL_FE": 0, "CL_MG": 0, "CL_N": 0, "CL_OA": 0, "CL_ZN": 0, "C_MN": 0, "C_NA": 0, "F_N": 0, "F_SA": 0, "F_ZN": 0, "HD_MN": 0, "MN_N": 0, "NA_SA": 0, "N_SA": 0, "A_HD": 0, "A_N": 0, "A_OA": 0, "A_ZN": 0, "BR_HD": 0, "C_C": 0, "C_F": 0, "C_HD": 0, "CL_HD": 0, "C_MG": 0, "C_N": 0, "C_OA": 0, "C_SA": 0, "C_ZN": 0, "FE_HD": 0, "FE_N": 0, "FE_OA": 0, "F_HD": 0, "F_OA": 0, "HD_HD": 0, "HD_I": 0, "HD_MG": 0, "HD_N": 0, "HD_NA": 0, "HD_OA": 0, "HD_P": 0, "HD_S": 0, "HD_SA": 0, "HD_ZN": 0, "MG_NA": 0, "MG_OA": 0, "MN_OA": 0, "NA_OA": 0, "NA_ZN": 0, "N_N": 0, "N_NA": 0, "N_OA": 0, "N_ZN": 0, "OA_OA": 0, "OA_SA": 0, "OA_ZN": 0, "SA_ZN": 0, "S_ZN": 0}
for key in data['ligand_receptor_atom_type_pairs_less_than_two_half']:
if not key in self.ligand_receptor_atom_type_pairs_less_than_two_half:
print(("\tWARNING: Atoms of types " + key.replace("_"," and ") + " come within 2.5 angstroms of each other."))
print("\t The neural networks were not trained to deal with this juxtaposition,")
print("\t so it will be ignored.")
self.error = True
else:
self.ligand_receptor_atom_type_pairs_less_than_two_half[key] = data['ligand_receptor_atom_type_pairs_less_than_two_half'][key]
self.ligand_receptor_atom_type_pairs_less_than_four = {"A_CU": 0, "A_MG": 0, "A_MN": 0, "BR_SA": 0, "C_CD": 0, "CL_FE": 0, "CL_MG": 0, "CL_MN": 0, "CL_NA": 0, "CL_P": 0, "CL_S": 0, "CL_ZN": 0, "CU_HD": 0, "CU_N": 0, "FE_NA": 0, "FE_SA": 0, "MG_N": 0, "MG_S": 0, "MG_SA": 0, "MN_NA": 0, "MN_S": 0, "MN_SA": 0, "NA_P": 0, "P_S": 0, "P_SA": 0, "S_SA": 0, "A_A": 0, "A_BR": 0, "A_C": 0, "A_CL": 0, "A_F": 0, "A_FE": 0, "A_HD": 0, "A_I": 0, "A_N": 0, "A_NA": 0, "A_OA": 0, "A_P": 0, "A_S": 0, "A_SA": 0, "A_ZN": 0, "BR_C": 0, "BR_HD": 0, "BR_N": 0, "BR_OA": 0, "C_C": 0, "C_CL": 0, "C_F": 0, "C_FE": 0, "C_HD": 0, "C_I": 0, "CL_HD": 0, "CL_N": 0, "CL_OA": 0, "CL_SA": 0, "C_MG": 0, "C_MN": 0, "C_N": 0, "C_NA": 0, "C_OA": 0, "C_P": 0, "C_S": 0, "C_SA": 0, "C_ZN": 0, "FE_HD": 0, "FE_N": 0, "FE_OA": 0, "F_HD": 0, "F_N": 0, "F_OA": 0, "F_SA": 0, "HD_HD": 0, "HD_I": 0, "HD_MG": 0, "HD_MN": 0, "HD_N": 0, "HD_NA": 0, "HD_OA": 0, "HD_P": 0, "HD_S": 0, "HD_SA": 0, "HD_ZN": 0, "I_N": 0, "I_OA": 0, "MG_NA": 0, "MG_OA": 0, "MG_P": 0, "MN_N": 0, "MN_OA": 0, "MN_P": 0, "NA_OA": 0, "NA_S": 0, "NA_SA": 0, "NA_ZN": 0, "N_N": 0, "N_NA": 0, "N_OA": 0, "N_P": 0, "N_S": 0, "N_SA": 0, "N_ZN": 0, "OA_OA": 0, "OA_P": 0, "OA_S": 0, "OA_SA": 0, "OA_ZN": 0, "P_ZN": 0, "SA_SA": 0, "SA_ZN": 0, "S_ZN": 0}
for key in data['ligand_receptor_atom_type_pairs_less_than_four']:
if not key in self.ligand_receptor_atom_type_pairs_less_than_four:
print(("\tWARNING: Atoms of types " + key.replace("_"," and ") + " come within 4 angstroms of each other."))
print("\t The neural networks were not trained to deal with this juxtaposition,")
print("\t so it will be ignored.")
self.error = True
else:
self.ligand_receptor_atom_type_pairs_less_than_four[key] = data['ligand_receptor_atom_type_pairs_less_than_four'][key]
self.ligand_atom_types = {'A': 0, 'BR': 0, 'C': 0, 'CL': 0, 'F': 0, 'HD': 0, 'I': 0, 'N': 0, 'NA': 0, 'OA': 0, 'P': 0, 'S': 0, 'SA': 0}
for key in data['ligand_atom_types']:
if not key in self.ligand_atom_types:
print(("\tWARNING: The ligand contains an atoms of type " + key + ". The neural networks"))
print("\t were not trained to deal with this ligand atom type, so it will be ignored.")
self.error = True
else:
self.ligand_atom_types[key] = data['ligand_atom_types'][key]
self.ligand_receptor_atom_type_pairs_electrostatic = {"A_MG": 0, "A_MN": 0, "BR_SA": 0, "CL_FE": 0, "CL_MG": 0, "CL_MN": 0, "CL_NA": 0, "CL_P": 0, "CL_S": 0, "CL_ZN": 0, "CU_HD": 0, "CU_N": 0, "FE_NA": 0, "FE_SA": 0, "MG_N": 0, "MG_S": 0, "MG_SA": 0, "MN_NA": 0, "MN_S": 0, "MN_SA": 0, "NA_P": 0, "P_S": 0, "P_SA": 0, "S_SA": 0, "A_A": 0.0, "A_BR": 0.0, "A_C": 0.0, "A_CL": 0.0, "A_F": 0.0, "A_FE": 0.0, "A_HD": 0.0, "A_I": 0.0, "A_N": 0.0, "A_NA": 0.0, "A_OA": 0.0, "A_P": 0.0, "A_S": 0.0, "A_SA": 0.0, "A_ZN": 0.0, "BR_C": 0.0, "BR_HD": 0.0, "BR_N": 0.0, "BR_OA": 0.0, "C_C": 0.0, "C_CL": 0.0, "C_F": 0.0, "C_FE": 0.0, "C_HD": 0.0, "C_I": 0.0, "CL_HD": 0.0, "CL_N": 0.0, "CL_OA": 0.0, "CL_SA": 0.0, "C_MG": 0.0, "C_MN": 0.0, "C_N": 0.0, "C_NA": 0.0, "C_OA": 0.0, "C_P": 0.0, "C_S": 0.0, "C_SA": 0.0, "C_ZN": 0.0, "FE_HD": 0.0, "FE_N": 0.0, "FE_OA": 0.0, "F_HD": 0.0, "F_N": 0.0, "F_OA": 0.0, "F_SA": 0.0, "HD_HD": 0.0, "HD_I": 0.0, "HD_MG": 0.0, "HD_MN": 0.0, "HD_N": 0.0, "HD_NA": 0.0, "HD_OA": 0.0, "HD_P": 0.0, "HD_S": 0.0, "HD_SA": 0.0, "HD_ZN": 0.0, "I_N": 0.0, "I_OA": 0.0, "MG_NA": 0.0, "MG_OA": 0.0, "MG_P": 0.0, "MN_N": 0.0, "MN_OA": 0.0, "MN_P": 0.0, "NA_OA": 0.0, "NA_S": 0.0, "NA_SA": 0.0, "NA_ZN": 0.0, "N_N": 0.0, "N_NA": 0.0, "N_OA": 0.0, "N_P": 0.0, "N_S": 0.0, "N_SA": 0.0, "N_ZN": 0.0, "OA_OA": 0.0, "OA_P": 0.0, "OA_S": 0.0, "OA_SA": 0.0, "OA_ZN": 0.0, "P_ZN": 0.0, "SA_SA": 0.0, "SA_ZN": 0.0, "S_ZN": 0, "F_ZN": 0}
for key in data['ligand_receptor_atom_type_pairs_electrostatic']:
if not key in self.ligand_receptor_atom_type_pairs_electrostatic:
print(("\tWARNING: Atoms of types " + key.replace("_"," and ") + ", which come within 4 angstroms of each"))
print("\t other, may interact electrostatically. However, the neural networks")
print("\t were not trained to deal with electrostatic interactions between atoms")
print("\t of these types, so they will be ignored.")
self.error = True
else:
self.ligand_receptor_atom_type_pairs_electrostatic[key] = data['ligand_receptor_atom_type_pairs_electrostatic'][key]
self.active_site_flexibility = {'BACKBONE_ALPHA': 0, 'BACKBONE_BETA': 0, 'BACKBONE_OTHER': 0, 'SIDECHAIN_ALPHA': 0, 'SIDECHAIN_BETA': 0, 'SIDECHAIN_OTHER': 0}
for key in data['active_site_flexibility']:
self.active_site_flexibility[key] = data['active_site_flexibility'][key]
alpha_tmp = self.active_site_flexibility['BACKBONE_ALPHA'] + self.active_site_flexibility['SIDECHAIN_ALPHA']
beta_tmp = self.active_site_flexibility['BACKBONE_BETA'] + self.active_site_flexibility['SIDECHAIN_BETA']
other_tmp = self.active_site_flexibility['BACKBONE_OTHER'] + self.active_site_flexibility['SIDECHAIN_OTHER']
self.active_site_flexibility_by_structure = {'ALPHA':alpha_tmp, 'BETA':beta_tmp, 'OTHER':other_tmp}
backbone_tmp = self.active_site_flexibility['BACKBONE_ALPHA'] + self.active_site_flexibility['BACKBONE_BETA'] + self.active_site_flexibility['BACKBONE_OTHER']
sidechain_tmp = self.active_site_flexibility['SIDECHAIN_ALPHA'] + self.active_site_flexibility['SIDECHAIN_BETA'] + self.active_site_flexibility['SIDECHAIN_OTHER']
self.active_site_flexibility_by_backbone_or_sidechain = {'BACKBONE':backbone_tmp, 'SIDECHAIN':sidechain_tmp}
all = self.active_site_flexibility['BACKBONE_ALPHA'] + self.active_site_flexibility['BACKBONE_BETA'] + self.active_site_flexibility['BACKBONE_OTHER'] + self.active_site_flexibility['SIDECHAIN_ALPHA'] + self.active_site_flexibility['SIDECHAIN_BETA'] + self.active_site_flexibility['SIDECHAIN_OTHER']
self.active_site_flexibility_all = {'all': all}
self.hbonds = {'HDONOR-LIGAND_BACKBONE_ALPHA': 0, 'HDONOR-LIGAND_BACKBONE_BETA': 0, 'HDONOR-LIGAND_BACKBONE_OTHER': 0, 'HDONOR-LIGAND_SIDECHAIN_ALPHA': 0, 'HDONOR-LIGAND_SIDECHAIN_BETA': 0, 'HDONOR-LIGAND_SIDECHAIN_OTHER': 0, 'HDONOR-RECEPTOR_BACKBONE_ALPHA': 0, 'HDONOR-RECEPTOR_BACKBONE_BETA': 0, 'HDONOR-RECEPTOR_BACKBONE_OTHER': 0, 'HDONOR-RECEPTOR_SIDECHAIN_ALPHA': 0, 'HDONOR-RECEPTOR_SIDECHAIN_BETA': 0, 'HDONOR-RECEPTOR_SIDECHAIN_OTHER': 0}
for key in data['hbonds']:
key2 = key.replace("HDONOR_","HDONOR-")
self.hbonds[key2] = data['hbonds'][key]
hdonor_ligand = self.hbonds['HDONOR-LIGAND_BACKBONE_ALPHA'] + self.hbonds['HDONOR-LIGAND_BACKBONE_BETA'] + self.hbonds['HDONOR-LIGAND_BACKBONE_OTHER'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_ALPHA'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_BETA'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_OTHER']
hdonor_receptor = self.hbonds['HDONOR-RECEPTOR_BACKBONE_ALPHA'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_BETA'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_OTHER'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_ALPHA'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_BETA'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_OTHER']
self.hbonds_by_location_of_hdonor = {'LIGAND':hdonor_ligand, 'RECEPTOR':hdonor_receptor}
hbond_backbone = self.hbonds['HDONOR-LIGAND_BACKBONE_ALPHA'] + self.hbonds['HDONOR-LIGAND_BACKBONE_BETA'] + self.hbonds['HDONOR-LIGAND_BACKBONE_OTHER'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_ALPHA'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_BETA'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_OTHER']
hbond_sidechain = self.hbonds['HDONOR-LIGAND_SIDECHAIN_ALPHA'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_BETA'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_OTHER'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_ALPHA'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_BETA'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_OTHER']
self.hbonds_by_backbone_or_sidechain = {'BACKBONE':hbond_backbone, 'SIDECHAIN':hbond_sidechain}
hbond_alpha = self.hbonds['HDONOR-LIGAND_BACKBONE_ALPHA'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_ALPHA'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_ALPHA'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_ALPHA']
hbond_beta = self.hbonds['HDONOR-LIGAND_BACKBONE_BETA'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_BETA'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_BETA'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_BETA']
hbond_other = self.hbonds['HDONOR-LIGAND_BACKBONE_OTHER'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_OTHER'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_OTHER'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_OTHER']
self.hbonds_by_structure = {'ALPHA':hbond_alpha, 'BETA':hbond_beta, 'OTHER':hbond_other}
all = self.hbonds['HDONOR-LIGAND_BACKBONE_ALPHA'] + self.hbonds['HDONOR-LIGAND_BACKBONE_BETA'] + self.hbonds['HDONOR-LIGAND_BACKBONE_OTHER'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_ALPHA'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_BETA'] + self.hbonds['HDONOR-LIGAND_SIDECHAIN_OTHER'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_ALPHA'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_BETA'] + self.hbonds['HDONOR-RECEPTOR_BACKBONE_OTHER'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_ALPHA'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_BETA'] + self.hbonds['HDONOR-RECEPTOR_SIDECHAIN_OTHER']
self.hbonds_all = {'all':all}
self.hydrophobics = {'BACKBONE_ALPHA': 0, 'BACKBONE_BETA': 0, 'BACKBONE_OTHER': 0, 'SIDECHAIN_ALPHA': 0, 'SIDECHAIN_BETA': 0, 'SIDECHAIN_OTHER': 0}
for key in data['hydrophobics']:
self.hydrophobics[key] = data['hydrophobics'][key]
alpha_tmp = self.hydrophobics['BACKBONE_ALPHA'] + self.hydrophobics['SIDECHAIN_ALPHA']
beta_tmp = self.hydrophobics['BACKBONE_BETA'] + self.hydrophobics['SIDECHAIN_BETA']
other_tmp = self.hydrophobics['BACKBONE_OTHER'] + self.hydrophobics['SIDECHAIN_OTHER']
self.hydrophobics_by_structure = {'ALPHA':alpha_tmp, 'BETA':beta_tmp, 'OTHER':other_tmp}
backbone_tmp = self.hydrophobics['BACKBONE_ALPHA'] + self.hydrophobics['BACKBONE_BETA'] + self.hydrophobics['BACKBONE_OTHER']
sidechain_tmp = self.hydrophobics['SIDECHAIN_ALPHA'] + self.hydrophobics['SIDECHAIN_BETA'] + self.hydrophobics['SIDECHAIN_OTHER']
self.hydrophobics_by_backbone_or_sidechain = {'BACKBONE':backbone_tmp, 'SIDECHAIN':sidechain_tmp}
all = self.hydrophobics['BACKBONE_ALPHA'] + self.hydrophobics['BACKBONE_BETA'] + self.hydrophobics['BACKBONE_OTHER'] + self.hydrophobics['SIDECHAIN_ALPHA'] + self.hydrophobics['SIDECHAIN_BETA'] + self.hydrophobics['SIDECHAIN_OTHER']
self.hydrophobics_all = {'all':all}
stacking_tmp = {}
for item in data['stacking']:
item = item.split("_")
stacking_tmp[item[1]] = int(item[2])
self.stacking = {'ALPHA': 0, 'BETA': 0, 'OTHER': 0}
for key in stacking_tmp:
self.stacking[key] = stacking_tmp[key]
all = self.stacking['ALPHA'] + self.stacking['BETA'] + self.stacking['OTHER']
self.stacking_all = {'all': all}
pi_cation_tmp = {}
for item in data['pi_cation']:
item = item.split("_")
pi_cation_tmp[item[1] + "_" + item[2]] = int(item[3])
self.pi_cation = {'LIGAND-CHARGED_ALPHA': 0, 'LIGAND-CHARGED_BETA': 0, 'LIGAND-CHARGED_OTHER': 0, 'RECEPTOR-CHARGED_ALPHA': 0, 'RECEPTOR-CHARGED_BETA': 0, 'RECEPTOR-CHARGED_OTHER': 0}
for key in pi_cation_tmp:
self.pi_cation[key] = pi_cation_tmp[key]
pi_cation_ligand_charged = self.pi_cation['LIGAND-CHARGED_ALPHA'] + self.pi_cation['LIGAND-CHARGED_BETA'] + self.pi_cation['LIGAND-CHARGED_OTHER']
pi_cation_receptor_charged = self.pi_cation['RECEPTOR-CHARGED_ALPHA'] + self.pi_cation['RECEPTOR-CHARGED_BETA'] + self.pi_cation['RECEPTOR-CHARGED_OTHER']
self.pi_cation_charge_location = {'LIGAND':pi_cation_ligand_charged, 'RECEPTOR':pi_cation_receptor_charged}
pi_cation_alpha = self.pi_cation['LIGAND-CHARGED_ALPHA'] + self.pi_cation['RECEPTOR-CHARGED_ALPHA']
pi_cation_beta = self.pi_cation['LIGAND-CHARGED_BETA'] + self.pi_cation['RECEPTOR-CHARGED_BETA']
pi_cation_other = self.pi_cation['LIGAND-CHARGED_OTHER'] + self.pi_cation['RECEPTOR-CHARGED_OTHER']
self.pi_cation_by_structure = {'ALPHA':pi_cation_alpha, 'BETA':pi_cation_beta, "OTHER":pi_cation_other}
all = self.pi_cation['LIGAND-CHARGED_ALPHA'] + self.pi_cation['LIGAND-CHARGED_BETA'] + self.pi_cation['LIGAND-CHARGED_OTHER'] + self.pi_cation['RECEPTOR-CHARGED_ALPHA'] + self.pi_cation['RECEPTOR-CHARGED_BETA'] + self.pi_cation['RECEPTOR-CHARGED_OTHER']
self.pi_cation_all = {'all': all}
t_shaped_tmp = {}
for item in data['t_shaped']:
item = item.split("_")
t_shaped_tmp[item[1]] = int(item[2])
self.t_shaped = {'ALPHA': 0, 'BETA': 0, 'OTHER': 0}
for key in t_shaped_tmp:
self.t_shaped[key] = t_shaped_tmp[key]
all = self.t_shaped['ALPHA'] + self.t_shaped['BETA'] + self.t_shaped['OTHER']
self.t_shaped_all = {'all': all}
self.salt_bridges = {'ALPHA': 0, 'BETA': 0, 'OTHER': 0}
for key in data['salt_bridges']:
key2 = key.replace("SALT-BRIDGE_","")
self.salt_bridges[key2] = data['salt_bridges'][key]
all = self.salt_bridges['ALPHA'] + self.salt_bridges['BETA'] + self.salt_bridges['OTHER']
self.salt_bridges_all = {'all': all}
self.input_vector = []
self.input_vector.extend(self.vina_output) # a list
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.ligand_receptor_atom_type_pairs_less_than_four)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.ligand_receptor_atom_type_pairs_electrostatic)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.ligand_atom_types)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.ligand_receptor_atom_type_pairs_less_than_two_half)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.hbonds)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.hydrophobics)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.stacking)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.pi_cation)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.t_shaped)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.active_site_flexibility)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.salt_bridges)
self.input_vector = self.extend_list_by_dictionary(self.input_vector, self.rotateable_bonds_count)
class command_line_parameters:
params = {}
def __init__(self, parameters):
global vina_executable
# first, set defaults
self.params['receptor'] = ''
self.params['ligand'] = ''
self.params['vina_executable'] = vina_executable
self.params['check_vina_version'] = "TRUE" # TRUE by default, but setting to false will speed up execution. Good when rescoring many poses.
# now get user inputed values
for index in range(len(parameters)):
item = parameters[index]
if item[:1] == '-': # so it's a parameter key value
key = item.replace('-','').lower()
if key == "help":
print("INTRODUCTION")
print("============\n")
print((textwrap.fill("NNScore 2.02 (NNScore2s.py) is a python script for predicting the binding affinity of receptor-ligand complexes. It is particularly well suited for rescoring ligands that have been docked using AutoDock Vina.") + "\n"))
print("REQUIREMENTS")
print("============\n")
print((textwrap.fill("Python: NNScore 2.02 has been tested using Python versions 2.6.5, 2.6.1, and 2.5.2 on Ubuntu 10.04.1 LTS, Mac OS X 10.6.8, and Windows XP Professional, respectively. A copy of the Python interpreter can be downloaded from http://www.python.org/getit/.") + "\n"))
print((textwrap.fill("AutoDock Vina 1.1.2: NNScore 2.02 uses AutoDock Vina 1.1.2 to obtain some information about the receptor-ligand complex. Note that previous versions of AutoDock Vina are not suitble. AutoDock Vina 1.1.2 can be downloaded from http://vina.scripps.edu/download.html.") + "\n"))
print((textwrap.fill("MGLTools: As receptor and ligand inputs, NNScore 2.02 accepts models in the PDBQT format. Files in the more common PDB format can be converted to the PDBQT format using scripts included in MGLTools (prepare_receptor4.py and prepare_ligand4.py). MGLTools can be obtained from http://mgltools.scripps.edu/downloads.") + "\n"))
print("COMMAND-LINE PARAMETERS")
print("=======================\n")
print((textwrap.fill("-receptor: File name of the receptor PDBQT file.") + "\n"))
print((textwrap.fill("-ligand: File name of the ligand PDBQT file. AutoDock Vina output files, typically containing multiple poses, are also permitted.") + "\n"))
print((textwrap.fill("-vina_executable: The location of the AutoDock Vina 1.1.2 executable. If you don't wish to specify the location of this file every time you use NNScore 2.02, simply edit the vina_executable variable defined near the begining of the NNScore2.py script.") + "\n"))
print("PROGRAM OUTPUT")
print("==============\n")
print((textwrap.fill("NNScore 2.02 evaluates each of the ligand poses contained in the file specified by the -ligand tag using 20 distinct neural-network scoring functions. The program then seeks to identify which of the poses has the highest predicted affinity using several metrics:") + "\n"))
print((textwrap.fill("1) Each of the 20 networks are considered separately. The poses are ranked in 20 different ways by the scores assigned by each network.") + "\n"))
print((textwrap.fill("2) The poses are ranked by the best score given by any of the 20 networks.") + "\n"))
print((textwrap.fill("3) The poses are ranked by the average of the scores given by the 20 networks. This is the recommended metric.") + "\n"))
sys.exit(0)
value = parameters[index+1]
if key in list(self.params.keys()):
self.params[key] = value
parameters[index] = ""
parameters[index + 1] = ""
if self.okay_to_proceed() is True:
print("Command-line parameters used:")
print(("\tReceptor: " + self.params["receptor"]))
print(("\tLigand: " + self.params["ligand"]))
print(("\tVina executable: " + self.params["vina_executable"] + "\n"))
# make a list of all the command-line parameters not used
error = ""
for index in range(1,len(parameters)):
item = parameters[index]
if item != "": error = error + item + " "
if error != "":
print("WARNING: The following command-line parameters were not used:")
print(("\t" + error + "\n"))
# do a check to make sure the autodock vina is version 1.1.2
if self.params["check_vina_version"] == "TRUE":
vina_version_output = ""
if not os.path.exists(self.params['vina_executable']):
vina_version_output = ""
else:
try:
vina_version_output = getCommandOutput2(self.params['vina_executable'] + ' --version')
except:
vina_version_output = ""
if not " 1.1.2 " in vina_version_output:
print("ERROR: NNScore 2.02 is designed to work with AutoDock Vina 1.1.2.\nOther versions of AutoDock may not work properly. Please download\nAutoDock Vina 1.1.2 from http://vina.scripps.edu/download.html.\n")
print("Once this executable is downloaded, you can use the -vina_executable\ntag to indicate its location. Alternatively, you can simply modify\nthe vina_executable variable defined near the beginning of\nthe NNScore2.py file.\n")
sys.exit(0)
def okay_to_proceed(self):
if self.params['receptor'] != '' and self.params['ligand'] != '' and self.params['vina_executable'] != '':
return True
else: return False
def score_to_kd(score):
kd = math.pow(10,-score)
if score <= 0: return "Kd = " + str(round(kd,2)) + " M"
temp = kd * pow(10,3)
if temp >=1 and temp <1000: return "Kd = " + str(round(temp,2)) + " mM"
temp = temp * pow(10,3)
if temp >=1 and temp <1000: return "Kd = " + str(round(temp,2)) + " uM"
temp = temp * pow(10,3)
if temp >=1 and temp <1000: return "Kd = " + str(round(temp,2)) + " nM"
temp = temp * pow(10,3)
if temp >=1 and temp <1000: return "Kd = " + str(round(temp,2)) + " pM"
temp = temp * pow(10,3)
#if temp >=1 and temp <1000:
return "Kd = " + str(round(temp,2)) + " fM"
#return "Kd = " + str(kd) + " M"
def networks():
nets = []
nets.append({'outno': {1: 359}, 'eni': {1: {1: 0.0304046423545, 2: 0.651959362024}, 2: {1: 0.00261812943927, 2: 0.138937931856}, 3: {1: 0.000173034126841, 2: 0.137653838555}, 4: {1: 0.0223427311116, 2: 0.148292791916}, 5: {1: 0.00402370444764, 2: 0.15}, 6: {1: 0.0319499025528, 2: 0.15}, 7: {1: 0.00853658536585, 2: 0.15}, 8: {1: 0.07, 2: 0.15}, 9: {1: 0.00843373493976, 2: 0.15}, 10: {1: 0.04375, 2: 0.15}, 11: {1: 0.7, 2: 0.15}, 12: {1: 0.0318181818182, 2: 0.15}, 13: {1: 0.14, 2: 0.15}, 14: {1: 0.00666666666667, 2: 0.15}, 15: {1: 0.175, 2: 0.15}, 16: {1: 0.0875, 2: 0.15}, 17: {1: 0.0875, 2: 0.15}, 18: {1: 0.021875, 2: 0.15}, 19: {1: 0.025, 2: 0.15}, 20: {1: 0.0134615384615, 2: 0.15}, 21: {1: 0.14, 2: 0.15}, 22: {1: 0.1, 2: 0.15}, 23: {1: 0.07, 2: 0.15}, 24: {1: 0.0777777777778, 2: 0.15}, 25: {1: 0.07, 2: 0.15}, 26: {1: 0.14, 2: 0.15}, 27: {1: 0.14, 2: 0.15}, 28: {1: 0.175, 2: 0.15}, 29: {1: 0.35, 2: 0.15}, 30: {1: 0.7, 2: 0.15}, 31: {1: 0.0538461538462, 2: 0.15}, 32: {1: 0.7, 2: 0.15}, 33: {1: 0.7, 2: 0.15}, 34: {1: 0.0875, 2: 0.15}, 35: {1: 0.7, 2: 0.15}, 36: {1: 0.0777777777778, 2: 0.15}, 37: {1: 0.7, 2: 0.15}, 38: {1: 0.7, 2: 0.15}, 39: {1: 0.35, 2: 0.15}, 40: {1: 0.7, 2: 0.15}, 41: {1: 0.7, 2: 0.15}, 42: {1: 0.7, 2: 0.15}, 43: {1: 0.0102941176471, 2: 0.15}, 44: {1: 0.35, 2: 0.15}, 45: {1: 0.0466666666667, 2: 0.15}, 46: {1: 0.0291666666667, 2: 0.15}, 47: {1: 0.175, 2: 0.15}, 48: {1: 0.00945945945946, 2: 0.15}, 49: {1: 0.0538461538462, 2: 0.15}, 50: {1: 0.0777777777778, 2: 0.15}, 51: {1: 0.1, 2: 0.15}, 52: {1: 0.0205882352941, 2: 0.15}, 53: {1: 0.0304347826087, 2: 0.15}, 54: {1: 0.00804597701149, 2: 0.15}, 55: {1: 0.1, 2: 0.15}, 56: {1: 0.14, 2: 0.15}, 57: {1: 0.0583333333333, 2: 0.15}, 58: {1: 0.14, 2: 0.15}, 59: {1: 0.35, 2: 0.15}, 60: {1: 0.233333333333, 2: 0.15}, 61: {1: 0.233333333333, 2: 0.15}, 62: {1: 0.35, 2: 0.15}, 63: {1: 0.7, 2: 0.15}, 64: {1: 0.0583333333333, 2: 0.15}, 65: {1: 0.07, 2: 0.15}, 66: {1: 0.0636363636364, 2: 0.15}, 67: {1: 0.175, 2: 0.15}, 68: {1: 0.0259259259259, 2: 0.15}, 69: {1: 0.116666666667, 2: 0.15}, 70: {1: 0.175, 2: 0.15}, 71: {1: 0.35, 2: 0.15}, 72: {1: 0.0212121212121, 2: 0.15}, 73: {1: 0.0466666666667, 2: 0.15}, 74: {1: 0.00777777777778, 2: 0.15}, 75: {1: 0.0225806451613, 2: 0.15}, 76: {1: 0.0368421052632, 2: 0.15}, 77: {1: 0.0636363636364, 2: 0.15}, 78: {1: 0.175, 2: 0.15}, 79: {1: 0.14, 2: 0.15}, 80: {1: 0.14, 2: 0.15}, 81: {1: 0.35, 2: 0.15}, 82: {1: 0.7, 2: 0.15}, 83: {1: 0.116666666667, 2: 0.15}, 84: {1: 0.14, 2: 0.15}, 85: {1: 0.7, 2: 0.15}, 86: {1: 0.7, 2: 0.15}, 87: {1: 0.35, 2: 0.15}, 88: {1: 0.7, 2: 0.15}, 89: {1: 0.175, 2: 0.15}, 90: {1: 0.175, 2: 0.15}, 91: {1: 0.7, 2: 0.15}, 92: {1: 0.35, 2: 0.15}, 93: {1: 0.0583333333333, 2: 0.15}, 94: {1: 0.7, 2: 0.15}, 95: {1: 0.7, 2: 0.15}, 96: {1: 0.233333333333, 2: 0.15}, 97: {1: 0.233333333333, 2: 0.15}, 98: {1: 0.07, 2: 0.15}, 99: {1: 0.0583333333333, 2: 0.15}, 100: {1: 0.0179487179487, 2: 0.15}, 101: {1: 0.05, 2: 0.15}, 102: {1: 0.116666666667, 2: 0.15}, 103: {1: 0.1, 2: 0.15}, 104: {1: 0.35, 2: 0.15}, 105: {1: 0.0318181818182, 2: 0.15}, 106: {1: 0.116666666667, 2: 0.15}, 107: {1: 0.14, 2: 0.15}, 108: {1: 0.1, 2: 0.15}, 109: {1: 0.233333333333, 2: 0.15}, 110: {1: 0.7, 2: 0.15}, 111: {1: 0.7, 2: 0.15}, 112: {1: 0.233333333333, 2: 0.15}, 113: {1: 0.35, 2: 0.15}, 114: {1: 0.35, 2: 0.15}, 115: {1: 0.7, 2: 0.15}, 116: {1: 0.7, 2: 0.15}, 117: {1: 3.87374150895e-06, 2: 0.241484336616}, 118: {1: 9.1513333069e-05, 2: 0.766520574414}, 119: {1: 1.00992148454e-06, 2: 0.163933793932}, 120: {1: 2.48414365543e-05, 2: 0.789884848031}, 121: {1: 1.30975514685e-05, 2: 0.766361493713}, 122: {1: 6.59324162593e-07, 2: 0.17025699176}, 123: {1: 8.17739471218e-07, 2: 0.164133755398}, 124: {1: 0.000167593113975, 2: 0.737334549945}, 125: {1: 1.5464440133e-06, 2: 0.16902428405}, 126: {1: 1.22347176214e-06, 2: 0.15}, 127: {1: 7.30883962944e-07, 2: 0.829064171192}, 128: {1: 5.7291070724e-06, 2: 0.759815993108}, 129: {1: 7.49262071283e-07, 2: 0.831419138892}, 130: {1: 1.08182938119e-05, 2: 0.23912023872}, 131: {1: 1.15823776086e-05, 2: 0.207196926645}, 132: {1: 5.66538965622e-06, 2: 0.805092286718}, 133: {1: 7.25309313051e-07, 2: 0.169603659975}, 134: {1: 2.14071551021e-05, 2: 0.85}, 135: {1: 1.43789916949e-05, 2: 0.85}, 136: {1: 2.07879410441e-05, 2: 0.15}, 137: {1: 1.4712765834e-05, 2: 0.15}, 138: {1: 8.28474553007e-05, 2: 0.15}, 139: {1: 4.48132351188e-06, 2: 0.85}, 140: {1: 7.29335404606e-06, 2: 0.85}, 141: {1: 7.52267963728e-06, 2: 0.85}, 142: {1: 1.02205250245e-05, 2: 0.85}, 143: {1: 8.64871808319e-06, 2: 0.15}, 144: {1: 0.000105484564531, 2: 0.15}, 145: {1: 5.26783530509e-06, 2: 0.15}, 146: {1: 4.4794304865e-05, 2: 0.85}, 147: {1: 8.6071196746e-05, 2: 0.85}, 148: {1: 6.42726531599e-05, 2: 0.15}, 149: {1: 2.88337019365e-06, 2: 0.85}, 150: {1: 8.20115210765e-05, 2: 0.15}, 151: {1: 4.59702491179e-05, 2: 0.85}, 152: {1: 1.42834286818e-06, 2: 0.165217591298}, 153: {1: 1.08646375961e-05, 2: 0.85}, 154: {1: 2.67353702245e-06, 2: 0.849818558507}, 155: {1: 6.16660516646e-07, 2: 0.164406019442}, 156: {1: 6.23053341881e-07, 2: 0.155232189189}, 157: {1: 2.15322086711e-05, 2: 0.85}, 158: {1: 8.89598109933e-07, 2: 0.15}, 159: {1: 9.78923087766e-07, 2: 0.15}, 160: {1: 7.44721657761e-07, 2: 0.815304690842}, 161: {1: 2.5585212063e-06, 2: 0.714192567361}, 162: {1: 2.53560643555e-07, 2: 0.848300867199}, 163: {1: 3.03479876511e-06, 2: 0.156137054749}, 164: {1: 6.83582644776e-06, 2: 0.160017249654}, 165: {1: 7.46703541115e-06, 2: 0.772736409688}, 166: {1: 6.35602431782e-07, 2: 0.15}, 167: {1: 4.28132932403e-07, 2: 0.15}, 168: {1: 4.5619236151e-07, 2: 0.775861322815}, 169: {1: 2.26111797938e-06, 2: 0.85}, 170: {1: 3.54925419893e-07, 2: 0.85}, 171: {1: 5.28972169235e-06, 2: 0.85}, 172: {1: 3.39018182563e-06, 2: 0.85}, 173: {1: 3.51530418416e-06, 2: 0.159291858218}, 174: {1: 1.68375498935e-06, 2: 0.15}, 175: {1: 1.48535582186e-05, 2: 0.15}, 176: {1: 3.04981097616e-06, 2: 0.85}, 177: {1: 1.03224930002e-06, 2: 0.15}, 178: {1: 2.43009983658e-05, 2: 0.85}, 179: {1: 6.65575477243e-07, 2: 0.15}, 180: {1: 1.12256869854e-06, 2: 0.15}, 181: {1: 6.77344120409e-07, 2: 0.775348711427}, 182: {1: 1.91204751533e-06, 2: 0.726883199998}, 183: {1: 1.07997369738e-07, 2: 0.85}, 184: {1: 5.40332864562e-07, 2: 0.15}, 185: {1: 1.78351478793e-06, 2: 0.15}, 186: {1: 5.33651617685e-06, 2: 0.693993575673}, 187: {1: 3.75459032168e-07, 2: 0.15}, 188: {1: 3.74940334159e-05, 2: 0.15}, 189: {1: 2.43043514963e-05, 2: 0.15}, 190: {1: 9.87764471542e-07, 2: 0.72284175909}, 191: {1: 1.82151336559e-06, 2: 0.646662722318}, 192: {1: 1.33879211779e-07, 2: 0.85}, 193: {1: 4.80980529835e-07, 2: 0.15}, 194: {1: 2.92122211936e-06, 2: 0.15}, 195: {1: 6.39148628048e-06, 2: 0.85}, 196: {1: 7.10090127137e-07, 2: 0.77089749952}, 197: {1: 2.79244481728e-06, 2: 0.85}, 198: {1: 2.22838538385e-07, 2: 0.85}, 199: {1: 4.81155800939e-07, 2: 0.15}, 200: {1: 3.38210815284e-06, 2: 0.15}, 201: {1: 9.37452115359e-06, 2: 0.85}, 202: {1: 1.18318889482e-06, 2: 0.248599577106}, 203: {1: 1.74020480673e-05, 2: 0.85}, 204: {1: 2.97358178694e-05, 2: 0.85}, 205: {1: 2.04662535052e-05, 2: 0.334703335937}, 206: {1: 8.28082859147e-07, 2: 0.85}, 207: {1: 1.69218106344e-06, 2: 0.277363300137}, 208: {1: 2.09956887178e-06, 2: 0.285838747635}, 209: {1: 2.68256612731e-07, 2: 0.211890792482}, 210: {1: 1.47301043354e-06, 2: 0.802495129318}, 211: {1: 3.82691779191e-06, 2: 0.814028569912}, 212: {1: 6.71107109288e-06, 2: 0.275516715583}, 213: {1: 1.00596730095e-06, 2: 0.654788033668}, 214: {1: 3.0613085843e-07, 2: 0.15}, 215: {1: 1.66632022455e-06, 2: 0.85}, 216: {1: 3.08220797994e-06, 2: 0.85}, 217: {1: 3.33635498928e-06, 2: 0.29918211991}, 218: {1: 1.90008203665e-07, 2: 0.85}, 219: {1: 3.32049850362e-05, 2: 0.15}, 220: {1: 5.6308839472e-05, 2: 0.85}, 221: {1: 9.43695879039e-07, 2: 0.15}, 222: {1: 3.11730517483e-05, 2: 0.392700518419}, 223: {1: 1.5771216231e-06, 2: 0.85}, 224: {1: 4.19320874302e-05, 2: 0.85}, 225: {1: 1.9807710649e-06, 2: 0.15}, 226: {1: 0.0184210526316, 2: 0.15}, 227: 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238, 2: 350}, 3032: {1: 239, 2: 350}, 3033: {1: 240, 2: 350}, 3034: {1: 241, 2: 350}, 3035: {1: 242, 2: 350}, 3036: {1: 243, 2: 350}, 3037: {1: 244, 2: 350}, 3038: {1: 245, 2: 350}, 3039: {1: 246, 2: 350}, 3040: {1: 247, 2: 350}, 3041: {1: 248, 2: 350}, 3042: {1: 249, 2: 350}, 3043: {1: 250, 2: 350}, 3044: {1: 251, 2: 350}, 3045: {1: 252, 2: 350}, 3046: {1: 253, 2: 350}, 3047: {1: 254, 2: 350}, 3048: {1: 255, 2: 350}, 3049: {1: 256, 2: 350}, 3050: {1: 257, 2: 350}, 3051: {1: 258, 2: 350}, 3052: {1: 259, 2: 350}, 3053: {1: 260, 2: 350}, 3054: {1: 261, 2: 350}, 3055: {1: 262, 2: 350}, 3056: {1: 263, 2: 350}, 3057: {1: 264, 2: 350}, 3058: {1: 265, 2: 350}, 3059: {1: 266, 2: 350}, 3060: {1: 267, 2: 350}, 3061: {1: 268, 2: 350}, 3062: {1: 269, 2: 350}, 3063: {1: 270, 2: 350}, 3064: {1: 271, 2: 350}, 3065: {1: 272, 2: 350}, 3066: {1: 273, 2: 350}, 3067: {1: 274, 2: 350}, 3068: {1: 275, 2: 350}, 3069: {1: 276, 2: 350}, 3070: {1: 277, 2: 350}, 3071: {1: 278, 2: 350}, 3072: {1: 279, 2: 350}, 3073: {1: 280, 2: 350}, 3074: {1: 281, 2: 350}, 3075: {1: 282, 2: 350}, 3076: {1: 283, 2: 350}, 3077: {1: 284, 2: 350}, 3078: {1: 285, 2: 350}, 3079: {1: 286, 2: 350}, 3080: {1: 287, 2: 350}, 3081: {1: 288, 2: 350}, 3082: {1: 289, 2: 350}, 3083: {1: 290, 2: 350}, 3084: {1: 291, 2: 350}, 3085: {1: 292, 2: 350}, 3086: {1: 293, 2: 350}, 3087: {1: 294, 2: 350}, 3088: {1: 295, 2: 350}, 3089: {1: 296, 2: 350}, 3090: {1: 297, 2: 350}, 3091: {1: 298, 2: 350}, 3092: {1: 299, 2: 350}, 3093: {1: 300, 2: 350}, 3094: {1: 301, 2: 350}, 3095: {1: 302, 2: 350}, 3096: {1: 303, 2: 350}, 3097: {1: 304, 2: 350}, 3098: {1: 305, 2: 350}, 3099: {1: 306, 2: 350}, 3100: {1: 307, 2: 350}, 3101: {1: 308, 2: 350}, 3102: {1: 309, 2: 350}, 3103: {1: 310, 2: 350}, 3104: {1: 311, 2: 350}, 3105: {1: 312, 2: 350}, 3106: {1: 313, 2: 350}, 3107: {1: 314, 2: 350}, 3108: {1: 315, 2: 350}, 3109: {1: 316, 2: 350}, 3110: {1: 317, 2: 350}, 3111: {1: 318, 2: 350}, 3112: {1: 319, 2: 350}, 3113: {1: 320, 2: 350}, 3114: {1: 321, 2: 350}, 3115: {1: 322, 2: 350}, 3116: {1: 323, 2: 350}, 3117: {1: 324, 2: 350}, 3118: {1: 325, 2: 350}, 3119: {1: 326, 2: 350}, 3120: {1: 327, 2: 350}, 3121: {1: 328, 2: 350}, 3122: {1: 329, 2: 350}, 3123: {1: 330, 2: 350}, 3124: {1: 331, 2: 350}, 3125: {1: 332, 2: 350}, 3126: {1: 333, 2: 350}, 3127: {1: 334, 2: 350}, 3128: {1: 335, 2: 350}, 3129: {1: 336, 2: 350}, 3130: {1: 337, 2: 350}, 3131: {1: 338, 2: 350}, 3132: {1: 339, 2: 350}, 3133: {1: 340, 2: 350}, 3134: {1: 341, 2: 350}, 3135: {1: 342, 2: 350}, 3136: {1: 343, 2: 350}, 3137: {1: 344, 2: 350}, 3138: {1: 345, 2: 350}, 3139: {1: 346, 2: 350}, 3140: {1: 347, 2: 350}, 3141: {1: 348, 2: 350}, 3142: {1: 0, 2: 351}, 3143: {1: 1, 2: 351}, 3144: {1: 2, 2: 351}, 3145: {1: 3, 2: 351}, 3146: {1: 4, 2: 351}, 3147: {1: 5, 2: 351}, 3148: {1: 6, 2: 351}, 3149: {1: 7, 2: 351}, 3150: {1: 8, 2: 351}, 3151: {1: 9, 2: 351}, 3152: {1: 10, 2: 351}, 3153: {1: 11, 2: 351}, 3154: {1: 12, 2: 351}, 3155: {1: 13, 2: 351}, 3156: {1: 14, 2: 351}, 3157: {1: 15, 2: 351}, 3158: {1: 16, 2: 351}, 3159: {1: 17, 2: 351}, 3160: {1: 18, 2: 351}, 3161: {1: 19, 2: 351}, 3162: {1: 20, 2: 351}, 3163: {1: 21, 2: 351}, 3164: {1: 22, 2: 351}, 3165: {1: 23, 2: 351}, 3166: {1: 24, 2: 351}, 3167: {1: 25, 2: 351}, 3168: {1: 26, 2: 351}, 3169: {1: 27, 2: 351}, 3170: {1: 28, 2: 351}, 3171: {1: 29, 2: 351}, 3172: {1: 30, 2: 351}, 3173: {1: 31, 2: 351}, 3174: {1: 32, 2: 351}, 3175: {1: 33, 2: 351}, 3176: {1: 34, 2: 351}, 3177: {1: 35, 2: 351}, 3178: {1: 36, 2: 351}, 3179: {1: 37, 2: 351}, 3180: {1: 38, 2: 351}, 3181: {1: 39, 2: 351}, 3182: {1: 40, 2: 351}, 3183: {1: 41, 2: 351}, 3184: {1: 42, 2: 351}, 3185: {1: 43, 2: 351}, 3186: {1: 44, 2: 351}, 3187: {1: 45, 2: 351}, 3188: {1: 46, 2: 351}, 3189: {1: 47, 2: 351}, 3190: {1: 48, 2: 351}, 3191: {1: 49, 2: 351}, 3192: {1: 50, 2: 351}, 3193: {1: 51, 2: 351}, 3194: {1: 52, 2: 351}, 3195: {1: 53, 2: 351}, 3196: {1: 54, 2: 351}, 3197: {1: 55, 2: 351}, 3198: {1: 56, 2: 351}, 3199: {1: 57, 2: 351}, 3200: {1: 58, 2: 351}, 3201: {1: 59, 2: 351}, 3202: {1: 60, 2: 351}, 3203: {1: 61, 2: 351}, 3204: {1: 62, 2: 351}, 3205: {1: 63, 2: 351}, 3206: {1: 64, 2: 351}, 3207: {1: 65, 2: 351}, 3208: {1: 66, 2: 351}, 3209: {1: 67, 2: 351}, 3210: {1: 68, 2: 351}, 3211: {1: 69, 2: 351}, 3212: {1: 70, 2: 351}, 3213: {1: 71, 2: 351}, 3214: {1: 72, 2: 351}, 3215: {1: 73, 2: 351}, 3216: {1: 74, 2: 351}, 3217: {1: 75, 2: 351}, 3218: {1: 76, 2: 351}, 3219: {1: 77, 2: 351}, 3220: {1: 78, 2: 351}, 3221: {1: 79, 2: 351}, 3222: {1: 80, 2: 351}, 3223: {1: 81, 2: 351}, 3224: {1: 82, 2: 351}, 3225: {1: 83, 2: 351}, 3226: {1: 84, 2: 351}, 3227: {1: 85, 2: 351}, 3228: {1: 86, 2: 351}, 3229: {1: 87, 2: 351}, 3230: {1: 88, 2: 351}, 3231: {1: 89, 2: 351}, 3232: {1: 90, 2: 351}, 3233: {1: 91, 2: 351}, 3234: {1: 92, 2: 351}, 3235: {1: 93, 2: 351}, 3236: {1: 94, 2: 351}, 3237: {1: 95, 2: 351}, 3238: {1: 96, 2: 351}, 3239: {1: 97, 2: 351}, 3240: {1: 98, 2: 351}, 3241: {1: 99, 2: 351}, 3242: {1: 100, 2: 351}, 3243: {1: 101, 2: 351}, 3244: {1: 102, 2: 351}, 3245: {1: 103, 2: 351}, 3246: {1: 104, 2: 351}, 3247: {1: 105, 2: 351}, 3248: {1: 106, 2: 351}, 3249: {1: 107, 2: 351}, 3250: {1: 108, 2: 351}, 3251: {1: 109, 2: 351}, 3252: {1: 110, 2: 351}, 3253: {1: 111, 2: 351}, 3254: {1: 112, 2: 351}, 3255: {1: 113, 2: 351}, 3256: {1: 114, 2: 351}, 3257: {1: 115, 2: 351}, 3258: {1: 116, 2: 351}, 3259: {1: 117, 2: 351}, 3260: {1: 118, 2: 351}, 3261: {1: 119, 2: 351}, 3262: {1: 120, 2: 351}, 3263: {1: 121, 2: 351}, 3264: {1: 122, 2: 351}, 3265: {1: 123, 2: 351}, 3266: {1: 124, 2: 351}, 3267: {1: 125, 2: 351}, 3268: {1: 126, 2: 351}, 3269: {1: 127, 2: 351}, 3270: {1: 128, 2: 351}, 3271: {1: 129, 2: 351}, 3272: {1: 130, 2: 351}, 3273: {1: 131, 2: 351}, 3274: {1: 132, 2: 351}, 3275: {1: 133, 2: 351}, 3276: {1: 134, 2: 351}, 3277: {1: 135, 2: 351}, 3278: {1: 136, 2: 351}, 3279: {1: 137, 2: 351}, 3280: {1: 138, 2: 351}, 3281: {1: 139, 2: 351}, 3282: {1: 140, 2: 351}, 3283: {1: 141, 2: 351}, 3284: {1: 142, 2: 351}, 3285: {1: 143, 2: 351}, 3286: {1: 144, 2: 351}, 3287: {1: 145, 2: 351}, 3288: {1: 146, 2: 351}, 3289: {1: 147, 2: 351}, 3290: {1: 148, 2: 351}, 3291: {1: 149, 2: 351}, 3292: {1: 150, 2: 351}, 3293: {1: 151, 2: 351}, 3294: {1: 152, 2: 351}, 3295: {1: 153, 2: 351}, 3296: {1: 154, 2: 351}, 3297: {1: 155, 2: 351}, 3298: {1: 156, 2: 351}, 3299: {1: 157, 2: 351}, 3300: {1: 158, 2: 351}, 3301: {1: 159, 2: 351}, 3302: {1: 160, 2: 351}, 3303: {1: 161, 2: 351}, 3304: {1: 162, 2: 351}, 3305: {1: 163, 2: 351}, 3306: {1: 164, 2: 351}, 3307: {1: 165, 2: 351}, 3308: {1: 166, 2: 351}, 3309: {1: 167, 2: 351}, 3310: {1: 168, 2: 351}, 3311: {1: 169, 2: 351}, 3312: {1: 170, 2: 351}, 3313: {1: 171, 2: 351}, 3314: {1: 172, 2: 351}, 3315: {1: 173, 2: 351}, 3316: {1: 174, 2: 351}, 3317: {1: 175, 2: 351}, 3318: {1: 176, 2: 351}, 3319: {1: 177, 2: 351}, 3320: {1: 178, 2: 351}, 3321: {1: 179, 2: 351}, 3322: {1: 180, 2: 351}, 3323: {1: 181, 2: 351}, 3324: {1: 182, 2: 351}, 3325: {1: 183, 2: 351}, 3326: {1: 184, 2: 351}, 3327: {1: 185, 2: 351}, 3328: {1: 186, 2: 351}, 3329: {1: 187, 2: 351}, 3330: {1: 188, 2: 351}, 3331: {1: 189, 2: 351}, 3332: {1: 190, 2: 351}, 3333: {1: 191, 2: 351}, 3334: {1: 192, 2: 351}, 3335: {1: 193, 2: 351}, 3336: {1: 194, 2: 351}, 3337: {1: 195, 2: 351}, 3338: {1: 196, 2: 351}, 3339: {1: 197, 2: 351}, 3340: {1: 198, 2: 351}, 3341: {1: 199, 2: 351}, 3342: {1: 200, 2: 351}, 3343: {1: 201, 2: 351}, 3344: {1: 202, 2: 351}, 3345: {1: 203, 2: 351}, 3346: {1: 204, 2: 351}, 3347: {1: 205, 2: 351}, 3348: {1: 206, 2: 351}, 3349: {1: 207, 2: 351}, 3350: {1: 208, 2: 351}, 3351: {1: 209, 2: 351}, 3352: {1: 210, 2: 351}, 3353: {1: 211, 2: 351}, 3354: {1: 212, 2: 351}, 3355: {1: 213, 2: 351}, 3356: {1: 214, 2: 351}, 3357: {1: 215, 2: 351}, 3358: {1: 216, 2: 351}, 3359: {1: 217, 2: 351}, 3360: {1: 218, 2: 351}, 3361: {1: 219, 2: 351}, 3362: {1: 220, 2: 351}, 3363: {1: 221, 2: 351}, 3364: {1: 222, 2: 351}, 3365: {1: 223, 2: 351}, 3366: {1: 224, 2: 351}, 3367: {1: 225, 2: 351}, 3368: {1: 226, 2: 351}, 3369: {1: 227, 2: 351}, 3370: {1: 228, 2: 351}, 3371: {1: 229, 2: 351}, 3372: {1: 230, 2: 351}, 3373: {1: 231, 2: 351}, 3374: {1: 232, 2: 351}, 3375: {1: 233, 2: 351}, 3376: {1: 234, 2: 351}, 3377: {1: 235, 2: 351}, 3378: {1: 236, 2: 351}, 3379: {1: 237, 2: 351}, 3380: {1: 238, 2: 351}, 3381: {1: 239, 2: 351}, 3382: {1: 240, 2: 351}, 3383: {1: 241, 2: 351}, 3384: {1: 242, 2: 351}, 3385: {1: 243, 2: 351}, 3386: {1: 244, 2: 351}, 3387: {1: 245, 2: 351}, 3388: {1: 246, 2: 351}, 3389: {1: 247, 2: 351}, 3390: {1: 248, 2: 351}, 3391: {1: 249, 2: 351}, 3392: {1: 250, 2: 351}, 3393: {1: 251, 2: 351}, 3394: {1: 252, 2: 351}, 3395: {1: 253, 2: 351}, 3396: {1: 254, 2: 351}, 3397: {1: 255, 2: 351}, 3398: {1: 256, 2: 351}, 3399: {1: 257, 2: 351}, 3400: {1: 258, 2: 351}, 3401: {1: 259, 2: 351}, 3402: {1: 260, 2: 351}, 3403: {1: 261, 2: 351}, 3404: {1: 262, 2: 351}, 3405: {1: 263, 2: 351}, 3406: {1: 264, 2: 351}, 3407: {1: 265, 2: 351}, 3408: {1: 266, 2: 351}, 3409: {1: 267, 2: 351}, 3410: {1: 268, 2: 351}, 3411: {1: 269, 2: 351}, 3412: {1: 270, 2: 351}, 3413: {1: 271, 2: 351}, 3414: {1: 272, 2: 351}, 3415: {1: 273, 2: 351}, 3416: {1: 274, 2: 351}, 3417: {1: 275, 2: 351}, 3418: {1: 276, 2: 351}, 3419: {1: 277, 2: 351}, 3420: {1: 278, 2: 351}, 3421: {1: 279, 2: 351}, 3422: {1: 280, 2: 351}, 3423: {1: 281, 2: 351}, 3424: {1: 282, 2: 351}, 3425: {1: 283, 2: 351}, 3426: {1: 284, 2: 351}, 3427: {1: 285, 2: 351}, 3428: {1: 286, 2: 351}, 3429: {1: 287, 2: 351}, 3430: {1: 288, 2: 351}, 3431: {1: 289, 2: 351}, 3432: {1: 290, 2: 351}, 3433: {1: 291, 2: 351}, 3434: {1: 292, 2: 351}, 3435: {1: 293, 2: 351}, 3436: {1: 294, 2: 351}, 3437: {1: 295, 2: 351}, 3438: {1: 296, 2: 351}, 3439: {1: 297, 2: 351}, 3440: {1: 298, 2: 351}, 3441: {1: 299, 2: 351}, 3442: {1: 300, 2: 351}, 3443: {1: 301, 2: 351}, 3444: {1: 302, 2: 351}, 3445: {1: 303, 2: 351}, 3446: {1: 304, 2: 351}, 3447: {1: 305, 2: 351}, 3448: {1: 306, 2: 351}, 3449: {1: 307, 2: 351}, 3450: {1: 308, 2: 351}, 3451: {1: 309, 2: 351}, 3452: {1: 310, 2: 351}, 3453: {1: 311, 2: 351}, 3454: {1: 312, 2: 351}, 3455: {1: 313, 2: 351}, 3456: {1: 314, 2: 351}, 3457: {1: 315, 2: 351}, 3458: {1: 316, 2: 351}, 3459: {1: 317, 2: 351}, 3460: {1: 318, 2: 351}, 3461: {1: 319, 2: 351}, 3462: {1: 320, 2: 351}, 3463: {1: 321, 2: 351}, 3464: {1: 322, 2: 351}, 3465: {1: 323, 2: 351}, 3466: {1: 324, 2: 351}, 3467: {1: 325, 2: 351}, 3468: {1: 326, 2: 351}, 3469: {1: 327, 2: 351}, 3470: {1: 328, 2: 351}, 3471: {1: 329, 2: 351}, 3472: {1: 330, 2: 351}, 3473: {1: 331, 2: 351}, 3474: {1: 332, 2: 351}, 3475: {1: 333, 2: 351}, 3476: {1: 334, 2: 351}, 3477: {1: 335, 2: 351}, 3478: {1: 336, 2: 351}, 3479: {1: 337, 2: 351}, 3480: {1: 338, 2: 351}, 3481: {1: 339, 2: 351}, 3482: {1: 340, 2: 351}, 3483: {1: 341, 2: 351}, 3484: {1: 342, 2: 351}, 3485: {1: 343, 2: 351}, 3486: {1: 344, 2: 351}, 3487: {1: 345, 2: 351}, 3488: {1: 346, 2: 351}, 3489: {1: 347, 2: 351}, 3490: {1: 348, 2: 351}, 3491: {1: 352, 2: 359}, 3492: {1: 353, 2: 359}, 3493: {1: 354, 2: 359}, 3494: {1: 355, 2: 359}, 3495: {1: 356, 2: 359}, 3496: {1: 357, 2: 359}, 3497: {1: 358, 2: 359}, 3498: {1: 0, 2: 359}, 3499: {1: 349, 2: 359}, 3500: {1: 350, 2: 359}, 3501: {1: 351, 2: 359}}, 'deo': {1: {1: 21.7454982145, 2: -3.26182473217}}, 'inno': {1: 348, 2: 347, 3: 346, 4: 345, 5: 344, 6: 343, 7: 342, 8: 341, 9: 340, 10: 339, 11: 338, 12: 337, 13: 336, 14: 335, 15: 334, 16: 333, 17: 332, 18: 331, 19: 330, 20: 329, 21: 328, 22: 327, 23: 326, 24: 325, 25: 324, 26: 323, 27: 322, 28: 321, 29: 320, 30: 319, 31: 318, 32: 317, 33: 316, 34: 315, 35: 314, 36: 313, 37: 312, 38: 311, 39: 310, 40: 309, 41: 308, 42: 307, 43: 306, 44: 305, 45: 304, 46: 303, 47: 302, 48: 301, 49: 300, 50: 299, 51: 298, 52: 297, 53: 296, 54: 295, 55: 294, 56: 293, 57: 292, 58: 291, 59: 290, 60: 289, 61: 288, 62: 287, 63: 286, 64: 285, 65: 284, 66: 283, 67: 282, 68: 281, 69: 280, 70: 279, 71: 278, 72: 277, 73: 276, 74: 275, 75: 274, 76: 273, 77: 272, 78: 271, 79: 270, 80: 269, 81: 268, 82: 267, 83: 266, 84: 265, 85: 264, 86: 263, 87: 262, 88: 261, 89: 260, 90: 259, 91: 258, 92: 257, 93: 256, 94: 255, 95: 254, 96: 253, 97: 252, 98: 251, 99: 250, 100: 249, 101: 248, 102: 247, 103: 246, 104: 245, 105: 244, 106: 243, 107: 242, 108: 241, 109: 240, 110: 239, 111: 238, 112: 237, 113: 236, 114: 235, 115: 234, 116: 233, 117: 232, 118: 231, 119: 230, 120: 229, 121: 228, 122: 227, 123: 226, 124: 225, 125: 224, 126: 223, 127: 222, 128: 221, 129: 220, 130: 219, 131: 218, 132: 217, 133: 216, 134: 215, 135: 214, 136: 213, 137: 212, 138: 211, 139: 210, 140: 209, 141: 208, 142: 207, 143: 206, 144: 205, 145: 204, 146: 203, 147: 202, 148: 201, 149: 200, 150: 199, 151: 198, 152: 197, 153: 196, 154: 195, 155: 194, 156: 193, 157: 192, 158: 191, 159: 190, 160: 189, 161: 188, 162: 187, 163: 186, 164: 185, 165: 184, 166: 183, 167: 182, 168: 181, 169: 180, 170: 179, 171: 178, 172: 177, 173: 176, 174: 175, 175: 174, 176: 173, 177: 172, 178: 171, 179: 170, 180: 169, 181: 168, 182: 167, 183: 166, 184: 165, 185: 164, 186: 163, 187: 162, 188: 161, 189: 160, 190: 159, 191: 158, 192: 157, 193: 156, 194: 155, 195: 154, 196: 153, 197: 152, 198: 151, 199: 150, 200: 149, 201: 148, 202: 147, 203: 146, 204: 145, 205: 144, 206: 143, 207: 142, 208: 141, 209: 140, 210: 139, 211: 138, 212: 137, 213: 136, 214: 135, 215: 134, 216: 133, 217: 132, 218: 131, 219: 130, 220: 129, 221: 128, 222: 127, 223: 126, 224: 125, 225: 124, 226: 123, 227: 122, 228: 121, 229: 120, 230: 119, 231: 118, 232: 117, 233: 116, 234: 115, 235: 114, 236: 113, 237: 112, 238: 111, 239: 110, 240: 109, 241: 108, 242: 107, 243: 106, 244: 105, 245: 104, 246: 103, 247: 102, 248: 101, 249: 100, 250: 99, 251: 98, 252: 97, 253: 96, 254: 95, 255: 94, 256: 93, 257: 92, 258: 91, 259: 90, 260: 89, 261: 88, 262: 87, 263: 86, 264: 85, 265: 84, 266: 83, 267: 82, 268: 81, 269: 80, 270: 79, 271: 78, 272: 77, 273: 76, 274: 75, 275: 74, 276: 73, 277: 72, 278: 71, 279: 70, 280: 69, 281: 68, 282: 67, 283: 66, 284: 65, 285: 64, 286: 63, 287: 62, 288: 61, 289: 60, 290: 59, 291: 58, 292: 57, 293: 56, 294: 55, 295: 54, 296: 53, 297: 52, 298: 51, 299: 50, 300: 49, 301: 48, 302: 47, 303: 46, 304: 45, 305: 44, 306: 43, 307: 42, 308: 41, 309: 40, 310: 39, 311: 38, 312: 37, 313: 36, 314: 35, 315: 34, 316: 33, 317: 32, 318: 31, 319: 30, 320: 29, 321: 28, 322: 27, 323: 26, 324: 25, 325: 24, 326: 23, 327: 22, 328: 21, 329: 20, 330: 19, 331: 18, 332: 17, 333: 16, 334: 15, 335: 14, 336: 13, 337: 12, 338: 11, 339: 10, 340: 9, 341: 8, 342: 7, 343: 6, 344: 5, 345: 4, 346: 3, 347: 2, 348: 1}})
return nets
print("\nNNScore 2.02")
print("============\n")
print((textwrap.fill("NNScore 2.02 is released under the GNU General Public License (see http://www.gnu.org/licenses/gpl.html). If you have any questions, comments, or suggestions, please contact the author, Jacob Durrant, at durrantj [at] pitt [dot] edu. If you use NNScore 2.02 in your work, please cite NNScore 2.0: A Neural-Network Receptor-Ligand Scoring Function. Jacob D. Durrant, Andrew McCammon. Journal of Chemical Information and Modeling, 2011, 51 (11), pp 2897-2903.")))
print("")
print((textwrap.fill("NNScore 2.02 is based in part on the ffnet python package developed by Marek Wojciechowski (http://ffnet.sourceforge.net/).")))
print("")
print("Use the -help command-line parameter for extended help.\n")
print("Example: python NNScore2.py -receptor myreceptor.pdbqt -ligand myligand.pdbqt -vina_executable /PATH/TO/VINA/1.1.2/vina\n")
cmd_params = command_line_parameters(sys.argv[:])
if cmd_params.okay_to_proceed() is False:
print("ERROR: You need to specify the ligand and receptor PDBQT files, as\nwell as the full path the the AutoDock Vina 1.1.2 executable, using the\n-receptor, -ligand, and -vina_executable tags from the command line.\nThe -ligand tag can also specify an AutoDock Vina output file.\n")
sys.exit(0)
lig = cmd_params.params['ligand']
rec = cmd_params.params['receptor']
def calculate_score(lig, rec, cmd_params, actual_filename_if_lig_is_list="", actual_filename_if_rec_is_list="", line_header = ""):
d = binana(lig, rec, cmd_params, line_header, actual_filename_if_lig_is_list, actual_filename_if_rec_is_list)
# now load in neural networks
scores = []
total = 0.0
nets = networks()
for net_array in nets:
try:
net = ffnet()
net.load(net_array)
val = net.normcall(d.input_vector)
print((line_header + "Network #" + str(len(scores) + 1) + " gave a score of " + str( round(val,3)) + " (" + score_to_kd(val) + ")"))
scores.append(val)
total = total + val
except OverflowError:
print((line_header + "The output of network #" + str(len(scores) + 1) + " could not be determined because of an overflow error!"))
if len(scores) != 0:
average = total / len(scores)
best_score = 0.0
best_network = -1
count = 1
sum = 0.0
for score in scores:
if score > best_score:
best_score = score
best_network = count
sum = sum + math.pow(score - average,2)
count = count + 1
stdev = math.pow(sum / (len(scores)-1), 0.5)
print("")
print((line_header + "Best Score: ", round(best_score,3), "(" + score_to_kd(best_score) + ")"))
print((line_header + "Average Score: ", round(average,3), "(" + score_to_kd(average) + ")"))
print((line_header + "Standard Deviation: ", round(stdev,3)))
print("")
return [average, stdev, best_score, best_network, scores]
else:
print((line_header + "Could not compute the score of this receptor-ligand complex because none of the networks returned a valid score."))
return [0, 0, 0, 0, []]
# load the rec into an array so you only have to load it from the disk once
print("\nLOADING THE RECEPTOR")
print("====================\n")
#f2 = open(rec, "r")
#rec_array = f2.readlines()
#f2.close()
receptor = PDB()
receptor.LoadPDB_from_file(rec)
receptor.OrigFileName = rec
print("\nEVALUATING EACH OF THE POSES IN THE LIGAND FILE USING 20 TRAINED NEURAL NETWORKS")
print("================================================================================\n")
# determine if the ligand input file is a single pdbqt or an autodock vina output file. Both are acceptable inputs.
f = open(lig, "r")
lig_array = []
line = "NULL"
scores = []
model_id = 1
while len(line) != 0:
line = f.readline()
if line[:6] != "ENDMDL": lig_array.append(line)
if line[:6] == "ENDMDL" or len(line) == 0:
if len(lig_array) != 0 and lig_array != ['']:
temp_filename = lig + ".MODEL_" + str(model_id) + ".pdbqt"
temp_f = open(temp_filename, "w")
for ln in lig_array: temp_f.write(ln)
temp_f.close()
model_name = "MODEL " + str(model_id)
print(model_name)
score=calculate_score(lig_array, receptor, cmd_params, temp_filename, rec, "\t")
scores.append([score[0], score[1], score[2], score[3], score[4], model_name])
os.remove(temp_filename)
lig_array = []
model_id = model_id + 1
f.close()
# now find the best score across all models, for each network
print("\nRANKED POSES AND SCORES WHEN EACH OF THE 20 NETWORKS IS CONSIDERED SEPARATELY")
print("=============================================================================\n")
best_network_scores = []
for t in range(20):
net_scores = []
for score in scores:
net_scores.append((score[4][t],score[5]))
net_scores = sorted(net_scores,key=lambda net_score: net_score[0], reverse=True) # sort by score
print(("USING NETWORK #" + str(t+1)).center(42," "))
print(" Rank | Pose | Score | Predicted Kd ")
print("------+----------+-------+--------------")
count = 1
best_network_scores.append(net_scores[0])
for net_score in net_scores:
print((str(count).center(6," ") + "|" + net_score[1].center(10," ") + "|" + str(round(net_score[0],3)).center(7," ") + "|" + score_to_kd(net_score[0]).replace("Kd = ","").center(14," ")))
count = count + 1
print("")
print("\nRANKED POSES AND SCORES WHEN A CONSENSUS OF NETWORK OUTPUTS ARE CONSIDERED")
print("==========================================================================\n")
# now get the best score of all vina output poses
best_scores = sorted(scores,key=lambda score: score[2], reverse=True) # sort by score
best_best_score = best_scores[0]
print(("BEST SCORE OF ALL 20 NETWORKS, BY POSE".center(65," ")))
print(" Rank | Pose | Average Score | Predicted Kd | Network")
print("------+----------+---------------+--------------+---------")
count = 1
for score in best_scores:
print((str(count).center(6," ") + "|" + score[5].center(10," ") + "|" + str(round(score[2],3)).center(15," ") + "|" + score_to_kd(score[2]).replace("Kd = ","").center(14," ") + "|" + ("#" + str(score[3])).center(9," ")))
count = count + 1
print("")
# now get the best average score of all vina output poses
average_scores = sorted(scores,key=lambda score: score[0], reverse=True) # sort by score
best_of_average_scores = average_scores[0]
print(("AVERAGE SCORE OF ALL 20 NETWORKS, BY POSE".center(70," ")))
print(" Rank | Pose | Average Score | Standard Deviation | Predicted Kd")
print("------+----------+---------------+--------------------+--------------")
count = 1
for score in average_scores:
print((str(count).center(6," ") + "|" + score[5].center(10," ") + "|" + str(round(score[0],3)).center(15," ") + "|" + str(round(score[1],3)).center(20," ") + "|" + score_to_kd(score[0]).replace("Kd = ","").center(15," ")))
count = count + 1
print("")
print("\nSUMMARY")
print("=======\n")
count = 1
for best_network_score in best_network_scores:
print((textwrap.fill("Best pose scored by network #" + str(count) + ": " + best_network_score[1] + " (Score = " + str(round(best_network_score[0],3)) + " = " + score_to_kd(best_network_score[0]).replace("Kd = ","") + ")")))
count = count + 1
print("")
print((textwrap.fill("When the poses were ranked by the best of the 20 network scores associated with each pose, the best-scoring pose was " + best_best_score[5] + " (Score = " + str(round(best_best_score[2],3)) + " = " + score_to_kd(best_best_score[2]).replace("Kd = ","") + ")")))
print("")
print((textwrap.fill("When the poses were ranked by the average of the 20 network scores associated with each pose, the best-scoring pose was " + best_of_average_scores[5] + " (Score = " + str(round(best_of_average_scores[0],3)) + " +/- " + str(round(best_of_average_scores[1],3)) + " = " + score_to_kd(best_of_average_scores[0]).replace("Kd = ","") + "). This is the recommended ranking/scoring metric.")))
print("")
| 3,563,328 | 1,504.419941 | 171,630 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/ranking/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/ranking/ranking_mol.py | """
This script runs the ranking and selection of ligands.
"""
import __future__
import os
import random
import rdkit
import rdkit.Chem as Chem
from rdkit.Chem.rdMolDescriptors import GetMorganFingerprint
from rdkit import DataStructs
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
import autogrow.operators.convert_files.gypsum_dl.gypsum_dl.MolObjectHandling as MOH
import autogrow.docking.ranking.selecting.rank_selection as Rank_Sel
import autogrow.docking.ranking.selecting.roulette_selection as Roulette_Sel
import autogrow.docking.ranking.selecting.tournament_selection as Tournament_Sel
def create_seed_list(usable_list_of_smiles, num_seed_diversity,
num_seed_dock_fitness, selector_choice, tourn_size):
"""
this function will take ausable_list_of_smiles which can be derived from
either the previous generation or a source_compounds_file. Then it will
select a set of smiles chosen by a weighted function to their
docking-fitness (docking score) Then it will select a set of smiles chosen
by a weighted function to their diversity-fitness (docking score) Then it
will merge these sets of smiles into a single list
Using the merged list it will make a list of all the smiles in the
merged-list (chosen_mol_list) with all the other information about each of
the chosen mols from the usable_list_of_smiles
It will return this list with the complete information of each chosen mol
(weighted_order_list)
Inputs:
:param list usable_list_of_smiles: a list with SMILES strings, names, and
information about the smiles from either the previous generation or the
source compound list
:param int num_seed_diversity: the number of seed molecules which come
from diversity selection
:param int num_seed_dock_fitness: the number of seed molecules which come
from eite selection by docking score
:param int selector_choice: the choice of selector method. Choices are
Roulette_Selector, Rank_Selector, or Tournament_Selector
:param float tourn_size: percentage of the total pool of ligands to be
tested in each tournament.
Returns:
:returns: list chosen_mol_full_data_list: a list of all the smiles in a
weighted ranking ie ["CCCC" "zinc123" 1 -0.1]
"""
if selector_choice == "Roulette_Selector":
print("Roulette_Selector")
# Get seed molecules based on docking scores
docking_fitness_smiles_list = Roulette_Sel.spin_roulette_selector(
usable_list_of_smiles, num_seed_dock_fitness, "docking"
)
# Get seed molecules based on diversity scores
diversity_smile_list = Roulette_Sel.spin_roulette_selector(
usable_list_of_smiles, num_seed_diversity, "diversity"
)
elif selector_choice == "Rank_Selector":
print("Rank_Selector")
# This assumes the most negative number is the best option which is
# true for both This is true for both the diversity score and the
# docking score. This may need to be adjusted for different scoring
# functions.
# Get seed molecules based on docking scores
docking_fitness_smiles_list = Rank_Sel.run_rank_selector(
usable_list_of_smiles, num_seed_dock_fitness, -2, False
)
# Get seed molecules based on diversity scores
diversity_smile_list = Rank_Sel.run_rank_selector(
usable_list_of_smiles, num_seed_diversity, -1, False
)
elif selector_choice == "Tournament_Selector":
print("Tournament_Selector")
# This assumes the most negative number is the best option which is
# true for both This is true for both the diversity score and the
# docking score. This may need to be adjusted for different scoring
# functions.
# Get seed molecules based on docking scores
docking_fitness_smiles_list = Tournament_Sel.run_Tournament_Selector(
usable_list_of_smiles, num_seed_dock_fitness, tourn_size, -2, True
)
# Get seed molecules based on diversity scores
diversity_smile_list = Tournament_Sel.run_Tournament_Selector(
usable_list_of_smiles, num_seed_diversity, tourn_size, -1, True
)
else:
print(selector_choice)
raise Exception(
"selector_choice value is not Roulette_Selector, Rank_Selector, nor Tournament_Selector"
)
chosen_mol_list = [x for x in docking_fitness_smiles_list]
chosen_mol_list.extend(diversity_smile_list)
if selector_choice in ["Rank_Selector", "Roulette_Selector"]:
# Get all the information about the chosen molecules. chosen_mol_list
# is 1D list of all chosen ligands chosen_mol_full_data_list is a 1D
# list with each item of the list having multiple pieces of
# information such as the ligand name/id, the smiles string, the
# diversity and docking score...
chosen_mol_full_data_list = get_chosen_mol_full_data_list(
chosen_mol_list, usable_list_of_smiles
)
elif selector_choice == "Tournament_Selector":
# Tournament_Selector returns an already full list of ligands so you
# can skip the get_chosen_mol_full_data_list step
chosen_mol_full_data_list = chosen_mol_list
else:
print(selector_choice)
raise Exception(
"selector_choice value is not Roulette_Selector, Rank_Selector, nor Tournament_Selector"
)
return chosen_mol_full_data_list
def get_chosen_mol_full_data_list(chosen_mol_list, usable_list_of_smiles):
"""
This function will take a list of chosen molecules and a list of all the
SMILES which could have been chosen and all of the information about those
SMILES (ie. ligand name, SMILES string, docking score, diversity score...)
It will iterated through the list of chosen mols (chosen_mol_list), get
all the information from the usable_list_of_smiles Then it appends the
corresponding item in usable_list_of_smiles to a new list
weighted_order_list
--- an issue to be aware of is that there may be redundancies in both
chosen_mol_list and usable_list_of_smiles this causes a many-to-many
problem so if manipulating this section you need to solve for
one-to-many
---for this reason if this gets altered it will raise an
AssertionError if the one-to-many is violated.
It then shuffles the order of the list which to prevent biasing by the
order of the ligands.
It will return that list of the chosen molecules in a randomly shuffled
order.
Inputs:
:param list chosen_mol_list: a list of chosen molecules
:param list usable_list_of_smiles: List of all the possibly chosen ligs
and all the of the info about it (ie. ligand name, SMILES string, docking
score, diversity score...) ["CCCC" "zinc123" 1 -0.1 -0.1]
Returns:
:returns: list weighted_order_list: a list of all the SMILES with all of
the associated information in a random order
"""
sorted_list = sorted(usable_list_of_smiles, key=lambda x: float(x[-2]))
weighted_order_list = []
for smile in chosen_mol_list:
for smile_pair in sorted_list:
if smile == smile_pair[0]:
weighted_order_list.append(smile_pair)
break
if len(weighted_order_list) != len(chosen_mol_list):
raise AssertionError(
"weighted_order_list not the same length as the chosen_mol_list"
)
random.shuffle(weighted_order_list)
return weighted_order_list
def get_usable_format(infile):
"""
This code takes a string for an file which is formatted as an .smi file. It
opens the file and reads in the components into a usable list.
The .smi must follow the following format for each line:
MANDATORY INFO
part 1 is the SMILES string
part 2 is the SMILES name/ID
Optional info
part -1 (the last piece of info) is the SMILES diversity score
relative to its population
part -2 (the second to last piece of info) is the fitness metric
for evaluating
- For default setting this is the Docking score
- If you add a unique scoring function Docking score should be
-3 and that score function should be -2
Any other information MUST be between part 2 and part -2 (this
allows for the expansion of features without disrupting the rest of the code)
Inputs:
:param str infile: the string of the PATHname of a formatted .smi file to
be read into the program
Returns:
:returns: list usable_list_of_smiles: list of SMILES and their associated
information formatted into a list which is usable by the rest of Autogrow
"""
# IMPORT SMILES FROM THE PREVIOUS GENERATION
usable_list_of_smiles = []
if os.path.exists(infile) is False:
print("\nFile of Source compounds does not exist: {}\n".format(infile))
raise Exception("File of Source compounds does not exist")
with open(infile) as smiles_file:
for line in smiles_file:
line = line.replace("\n", "")
parts = line.split("\t") # split line into parts separated by 4-spaces
if len(parts) == 1:
parts = line.split(
" "
) # split line into parts separated by 4-spaces
choice_list = []
for i in range(0, len(parts)):
choice_list.append(parts[i])
usable_list_of_smiles.append(choice_list)
return usable_list_of_smiles
def convert_usable_list_to_lig_dict(usable_list_of_smiles):
"""
This will convert a list created by get_usable_format() to a dictionary
using the ligand smile+lig_id as the key. This makes for faster searching
in larger Autogrow runs.
Inputs:
:param list usable_list_of_smiles: list of SMILES and their associated
information formatted into a list which is usable by the rest of Autogrow
Returns:
:returns: list usable_dict_of_smiles: djct of all the ligand info with a
key containing both the SMILE string and the unique lig ID
"""
if type(usable_list_of_smiles) is not type([]):
return None
usable_dict_of_smiles = {}
for item in usable_list_of_smiles:
key = str(item[0]) + str(item[1])
if key in usable_dict_of_smiles.keys():
if usable_dict_of_smiles[key][-1] < item[-2]:
continue
usable_dict_of_smiles[key] = item
return usable_dict_of_smiles
##### Called in the docking class ######
def score_and_append_diversity_scores(molecules_list):
"""
This function will take list of molecules which makes up a population. It
will then create a diversity score for each molecules:
It creates the diversity score by determining the Morgan Fingerprint for
each molecule in the population.
It then compares the fingerprints for every molecule against every
molecule in a pairwise manner.
Based on the approach provided on
http://www.rdkit.org/docs/GettingStartedInPython.html section: "Picking
Diverse Molecules Using Fingerprints"
It determines a score of similarity using the RDKit function
DataStructs.DiceSimilarity
-The higher the similarity the higher the similarity score
-ie) if you compare two identical SMILES the similarity score
is 1.0. I.e., if you compare 4 identical SMILES the
similarity score for each is 4.0.
-ie) if you compare two completely different SMILES, the score
is 0.0
It sums the similarity score for each pairwise comparison.
-ie) if there are 15 ligands the max score is 15 the minimum is 0.0
with 15.0 if all ligands are identical
It then appends the diversity score to the molecule list which it
returns.
It can raise an AssertionError if there are ligs which fail to
sanitize or deprotanate.
-this prevents future errors from occuring in later steps and
makes this funciton usable for multiple codes
It will remove any Nones from the input list
Inputs:
:param list molecules_list: list of all molecules in the populations with
the respective info
Returns:
:returns: list molecules_list: list of all molecules in the populations
with the respective info and append diversity score
"""
mol_list = []
for pair in molecules_list:
if pair is not None:
smile = pair[0]
# name = pair[1]
try:
mol = Chem.MolFromSmiles(smile, sanitize=False)
except:
mol = None
if mol is None:
raise AssertionError("mol in list failed to sanitize. Issue in Ranking.py def score_and_append_diversity_scores")
mol = MOH.check_sanitization(mol)
if mol is None:
raise AssertionError("mol in list failed to sanitize. Issue in Ranking.py def score_and_append_diversity_scores")
mol = MOH.try_deprotanation(mol)
if mol is None:
raise AssertionError("mol in list failed to sanitize. Issue in Ranking.py def score_and_append_diversity_scores")
temp = [x for x in pair]
temp.append(mol)
if temp[-1] is None:
print(temp)
print("None in temp list, skip this one")
continue
if temp[-1] is not None:
mol_list.append(temp)
else:
print("noneitem in molecules_list in score_and_append_diversity_scores")
fps_list = []
for molecule in mol_list:
fp = GetMorganFingerprint(molecule[-1], 10, useFeatures=True)
temp = [x for x in molecule]
temp.append(fp)
fps_list.append(temp)
fps_list_w_div_score = []
for i in range(0, len(fps_list)):
diversity_score = 0
for j in range(0, len(fps_list)):
if i != j:
# if DiceSimilarity=1.0 its a perfect match, the smaller the
# number the more diverse it is. The sum of all of these gives
# the distance from the normal. The smaller the number means
# the more distant
diversity_score = diversity_score + DataStructs.DiceSimilarity(
fps_list[i][-1], fps_list[j][-1]
)
temp = [x for x in fps_list[i]]
temp.append(str(diversity_score))
fps_list_w_div_score.append(temp)
# take the diversity score and append to the last column in the original list
for i in range(0, len(molecules_list)):
if molecules_list[i][0] == fps_list_w_div_score[i][0]:
molecules_list[i].append(fps_list_w_div_score[i][-1])
return molecules_list
| 15,237 | 38.785901 | 129 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/docking/ranking/selecting/roulette_selection.py | """
This script is use to select molecules using a roulette selector
"""
import __future__
import numpy.random as rn
def spin_roulette_selector(usable_list_of_smiles, number_to_chose,
docking_or_diversity):
"""
make a list of ligands chosen by a random weighted roulette selection,
without replacement, weighted by its docking score
Inputs:
:param list usable_list_of_smiles: a list with all the information of all
the mols in the previous generation
:param int number_to_chose: the number of molecules to chose based on
docking score
:param str docking_or_diversity: an string describing either "docking" or
"diversity" this tells the function how to adjust the weighted scores
Returns:
:returns: list top_choice_smile_order: list of ligands chosen by a random
weighted selection, without replacement, -weighted by its docking score
"""
if type(usable_list_of_smiles) is not type([]):
raise Exception("usable_list_of_smiles Must be a list, wrong data type")
num_ligands = len(usable_list_of_smiles)
if num_ligands == 0:
raise Exception(
"usable_list_of_smiles is an empty list. There is nothing to chose from."
)
if number_to_chose <= 0:
top_choice_smile_order = []
return top_choice_smile_order
adjusted = adjust_scores(usable_list_of_smiles, docking_or_diversity)
total = sum(adjusted)
probability = [x / total for x in adjusted]
smiles_list = [x[0] for x in usable_list_of_smiles]
top_choice_smile_order = rn.choice(
smiles_list, size=number_to_chose, replace=False, p=probability
)
return top_choice_smile_order
def adjust_scores(usable_list_of_smiles, docking_or_diversity):
"""
This function adjusts the scores appropriately. This is where we weight
the scores so smaller differences are more pronounced and where we adjust
for the fact that docking score is better with a more negative number
while diversity score is the smallest positive number is the most unique.
Inputs:
:param list usable_list_of_smiles: a list with all the information of all
the mols in the previous generation
:param str docking_or_diversity: an string describing either "docking"
or "diversity" this tells the function how to adjust the weighted
scores
Returns:
:returns: list adjusted: list of ligand scores which have been weighted
and adjusted
"""
if docking_or_diversity == "docking":
weight_scores = [float(x[-1]) for x in usable_list_of_smiles]
# minimum is the most positive value from usable_list_of_smiles the
# more negative the docking score the better the dock
minimum = max(weight_scores) + 0.1
if minimum < 0:
minimum = 0
adjusted = [(x ** 10) + minimum for x in weight_scores]
elif docking_or_diversity == "diversity":
weight_scores = [float(x[-1]) for x in usable_list_of_smiles]
# adjust by squaring the number to make the discrpency larger and
# invert by dividing 1/x^2 (because the more diverse a mol is the
# smaller the number)
adjusted = [(x ** -2) for x in weight_scores]
else:
raise Exception("docking_or_diversity choice not an option")
return adjusted
| 3,389 | 34.3125 | 85 | py |
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