compsciencelab/AMARO2

AMARO v2.0

This repository, compsciencelab/AMARO2, will host all three AMARO v2 neural network potential checkpoints. Once available, use amaro_v2.ckpt for the final model. The original AMARO v1 release is hosted separately at compsciencelab/AMARO.

AMARO v2 is a coarse-grained potential that retains heavy atoms and treats solvent implicitly. Hydrogen-aware bead types and total-charge conditioning provide a shared representation for proteins, drug-like molecules, and their complexes. The model and training procedure are described in the preprint A unified coarse-grained neural network potential for the dynamics of biomolecular complexes by Antonio Mirarchi, Stephen E. Farr, Adria Perez, and Gianni De Fabritiis.

Code and installation: compsciencelab/amaro. See the system preparation guide and OpenMM simulation guide for preparing inputs and running the model.

Planned checkpoint release

The checkpoint files and v2.0 release tag are not available yet.

Revision	Checkpoints	Representation
v2.0	amaro_v2_sm.ckpt, amaro_v2_p.ckpt, amaro_v2.ckpt	Extended no-hydrogen (NOH) biomelecular systems mapping

AMARO v2 training stages

The three planned files are checkpoints from successive stages of the same training sequence. Each stage continues training from the preceding stage:

Stage	Checkpoint	Training data used at this stage	Role
1: small molecules	amaro_v2_sm.ckpt	In-house AceFF small-molecule dataset with quantum-mechanical force labels	AMARO v2.0 (SM) in the preprint
2: proteins	amaro_v2_p.ckpt	Protein conformations from mdCATH, with molecular-mechanics force labels	Intermediate checkpoint after protein training
3: protein–ligand complexes	amaro_v2.ckpt	Acellera HoloSet protein–ligand conformations, with molecular-mechanics force labels	Final AMARO v2.0 model in the preprint

Inputs and outputs

AMARO v2 uses NOH species IDs, which encode retained elements and, where applicable, the number of hydrogens mapped to each heavy atom. The preprint describes 26 solute bead types across 18 elements: the original 12 protein types plus B, F, Si, P, Cl, Br, I, Na, K, Ca, Mg, Co, Cu, and Zn. Solvent and counterions are omitted from the training representation.

Use the exact species IDs and bead ordering from the v2 preparation utilities. Do not replace species IDs with ordinary atomic numbers. The original v1 12-type mapping does not cover the full v2 solute representation. For proteins and complexes, the preprint assigns protonation states and total charge at pH 7.45; the supplied charge must match the prepared system.

The inference interface takes species IDs, Cartesian positions, and molecular charge, with optional batch assignments and periodic box vectors where supported by the runner. The model predicts an effective potential; forces are its negative position gradient. Use the AMARO v2 TorchMD-Net source and system preparation utilities from the accompanying code release. The dataset classes used during training are not required for inference.

Use positions in angstroms, effective potential values in kcal/mol, and forces in kcal/mol/angstrom, consistent with the preprint's training objective and cutoff and the OpenMM PythonForce runner. Force-only training does not calibrate an absolute energy reference. The Scalar output head does not use Coulomb cutoff or reaction-field options.

Download (coming soon)

The commands below will work after the checkpoints and v2.0 tag are published. Install the Hub client with python -m pip install huggingface_hub, then download one checkpoint into the code repository's ckpts/ directory:

from huggingface_hub import hf_hub_download

checkpoint = hf_hub_download(
    repo_id="compsciencelab/AMARO2",
    filename="amaro_v2.ckpt",
    revision="v2.0",
    local_dir="ckpts",
)

Select another v2 variant by changing filename.

To download all three ckpts training stages:

hf download compsciencelab/AMARO2 \
  amaro_v2_sm.ckpt amaro_v2_p.ckpt amaro_v2.ckpt \
  --revision v2.0 --local-dir ckpts

License

AMARO v2 checkpoint licensing to define.

Citation

For AMARO v2, cite the preprint (arXiv:2610.37606v1):

@unpublished{mirarchi2026amarov2,
  title = {A unified coarse-grained neural network potential for the dynamics of biomolecular complexes},
  author = {Mirarchi, Antonio and Farr, Stephen E. and Perez, Adria and De Fabritiis, Gianni},
  year = {2026},
  eprint = {2610.37606},
  archivePrefix = {arXiv},
  primaryClass = {q-bio.BM},
  url = {https://arxiv.org/pdf/2610.37606v1},
  note = {Version 1}
}