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This release bundles no third-party model weights and no third-party source
code. The components below are **required at runtime** and must be obtained by
the user under their own licenses. See README.md §4.
## ESM2-650M — `facebook/esm2_t33_650M_UR50D`
Used frozen (never fine-tuned) as the sequence-context model: it provides the
reference-process peptide prior and the plan head's anchor/local-context
features.
- Publisher: Meta AI (Fundamental AI Research Protein Team)
- Weights: not redistributed here; download from Hugging Face.
- License: the ESM2 model license from Meta. Review it before redistributing
weights or derivatives.
- Reference: Lin et al., "Evolutionary-scale prediction of atomic-level protein
structure with a language model", *Science* 379 (2023).
## PeptiVerse — peptide property oracles
Supplies the permeability-penetrance predictor that defines the main training
objective, plus the monitored toxicity / hemolysis / half-life predictors.
- Weights and source: not redistributed here; obtain the PeptiVerse
distribution separately.
- License: as specified by the PeptiVerse authors.
## PeptideCLM-23M — `aaronfeller/PeptideCLM-23M-all`
Required. Supplies SMILES embeddings for several PeptiVerse predictors selected
by the official `basic_models.txt` manifest (including the half-life and
nonfouling models).
- Weights: not redistributed here; download from Hugging Face.
- License: as published with the model.
## ChemBERTa-77M — `DeepChem/ChemBERTa-77M-MLM`
Required. Supplies SMILES embeddings for the permeability-penetrance predictor
that defines the main training objective, plus the toxicity, PAMPA and Caco-2
models.
- Weights: not redistributed here; download from Hugging Face.
- License: as published by DeepChem.
- Reference: Chithrananda et al., "ChemBERTa: Large-Scale Self-Supervised
Pretraining for Molecular Property Prediction" (2020).
## Python dependencies
Declared in `requirements.txt` and installed from PyPI, each under its own
license:
| Package | License |
| --- | --- |
| PyTorch | BSD-3-Clause |
| NumPy | BSD-3-Clause |
| PyYAML | MIT |
| RDKit | BSD-3-Clause |
| transformers (Hugging Face) | Apache-2.0 |
The PeptiVerse distribution brings its own further dependencies (scikit-learn,
XGBoost, MAPIE, pandas, joblib and others); those are governed by their
respective licenses and are not declared by this package.
## Data
No dataset is included in this release. The processed training and validation
splits are handled separately; nothing here downloads, reconstructs or
redistributes data. See `data/README.md`.
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