| --- |
| license: cc-by-nc-nd-4.0 |
| datasets: |
| - compsciencelab/mdCATH |
| tags: |
| - neural-network-potential |
| - coarse-grained |
| - molecular-dynamics |
| - protein-dynamics |
| - computational-biophysics |
| - force-field |
| - tensornet |
| --- |
| # AMARO: All Heavy-Atom Transferable Neural Network Potential |
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| This repository hosts the trained checkpoint for **AMARO v1.0**. |
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| All-atom molecular simulations provide detailed insight into macromolecular phenomena, but their computational cost limits the exploration of complex biological processes. |
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| **AMARO**, **Advanced Machine-learning Atomic Representation Omni-force-field**, is a neural network potential that combines the O(3)-equivariant message-passing architecture **TensorNet** with a coarse-graining map that excludes hydrogen atoms. |
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| AMARO demonstrates that coarse-grained neural network potentials can be trained without explicit prior-energy terms while retaining stable protein dynamics, scalability, and transferability. |
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| ## Representation |
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| AMARO uses a **no-hydrogen, no-water mapping**. Each retained bead corresponds to one protein heavy atom. |
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| For each retained heavy atom, the reference force is constructed as the sum of: |
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| 1. the force acting on the heavy atom; and |
| 2. the forces acting on hydrogen atoms constrained to that heavy atom. |
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| This mapping reduces the number of degrees of freedom while retaining a detailed representation of protein geometry. |
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| ## Important: `z` Values Are AMARO Bead Types |
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| The checkpoint does not interpret `z` as conventional atomic numbers alone. |
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| Each bead is assigned one of **12 learned embedding types**, determined by: |
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| * the identity of the heavy atom; and |
| * the number of hydrogen atoms aggregated to it. |
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| This representation distinguishes chemically different environments and electronic hybridizations that would otherwise share the same element. |
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| Inputs must therefore be prepared using the same AMARO mapping and bead-type assignment used during training. Passing ordinary atomic numbers without applying the AMARO remapping will produce invalid predictions. |
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| ## Model Inputs and Outputs |
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| ### Inputs |
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| * `z`: one-dimensional tensor containing AMARO bead-type, with shape `(N,)` |
| * `pos`: Cartesian coordinates in Å, with shape `(N, 3)` |
| * `batch`: optional system-assignment tensor with shape `(N,)` |
| * `box`: optional periodic box vectors, where supported by the installed TorchMD-Net version |
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| ### Outputs |
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| * `energy`: learned effective potential for each system |
| * `forces`: negative gradient of the effective potential with respect to bead positions |
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| The model uses units of: |
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| ```text |
| kcal/mol/Å for forces and Å for coordinates |
| ``` |
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| Because the model was trained using force labels without energy labels, its absolute energy reference is not physically calibrated. |
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| ## Citation |
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| Please cite the following publication when using this checkpoint: |
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| ```bibtex |
| @article{mirarchi2024amaro, |
| title = {AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics}, |
| author = {Mirarchi, Antonio and Pel{\'a}ez, Ra{\'u}l P. and Simeon, Guillem and De Fabritiis, Gianni}, |
| journal = {Journal of Chemical Theory and Computation}, |
| volume = {20}, |
| number = {22}, |
| pages = {9871--9878}, |
| year = {2024}, |
| publisher = {ACS Publications} |
| } |
| ``` |
