Acellera AceFF 2-RESP-1
Acellera Therapeutics, inc · info@acellera.com · Apache 2.0
A RESP-charge variant of AceFF-2: predicts energy, forces, and per-atom RESP partial charges for electrostatic embedding in NNP/MM (e.g. RBFE). A modified TensorNet-2 model [Farr2026] with an added charge head, trained on a subset of the AceFF-2 dataset (the Schrödinger dataset, as it is referred to in the AceFF-2 paper). Paper: AceFF.
Benchmarks
RESP charges
Predicted vs reference RESP charges (650 conformers, 30,278 atoms, Q −2…+2).
Overall MAE 0.033 e, RMSE 0.049 e.
| Q | Conformers | Atoms | Charge MAE (e) | Charge RMSE (e) | Force MAE (eV/Å) | Energy MAE (eV) |
|---|---|---|---|---|---|---|
| −2 | 23 | 1048 | 0.0381 | 0.0490 | 0.0762 | 0.0605 |
| −1 | 80 | 3559 | 0.0298 | 0.0406 | 0.0458 | 0.0858 |
| +0 | 422 | 19425 | 0.0341 | 0.0509 | 0.0462 | 0.0790 |
| +1 | 122 | 6078 | 0.0302 | 0.0450 | 0.0533 | 0.0876 |
| +2 | 3 | 168 | 0.0355 | 0.0571 | 0.0543 | 0.2897 |
| All | 650 | 30278 | 0.0330 | 0.0486 | 0.0486 | 0.0818 |
Usage
Currently requires two branches (not yet merged): the torchmd-net tensornet2_resp
branch torchmd/torchmd-net@resp_model
for loading/inference, and the atm electrostatic-embedding branch
Acellera/atm@electrostatic_embedding
for RBFE. Both will be upstreamed.
import torch
from torchmdnet.models.model import load_model
model = load_model("aceff-2-resp-1.ckpt", model="tensornet2_resp", derivative=True).eval()
z = torch.tensor([8, 1, 1]) # water
pos = torch.tensor([[0.,0.,0.], [0.757,0.586,0.], [-0.757,0.586,0.]])
batch = torch.zeros(3, dtype=torch.long); q = torch.zeros(1) # total charge
energy, forces, charges = model(z=z, pos=pos, batch=batch, q=q)
print(charges.reshape(-1)) # per-atom RESP charges
Limitations
- Small molecules only (elements H, B, C, N, O, F, Si, P, S, Cl, Br, I).
- 2 fs timestep with hydrogen mass repartitioning.
- Molecular charge states −2…+2 only.
Citation
If you use AceFF-2-RESP-1, please cite our preprint: Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations, arXiv:2608.13355, DOI: 10.48550/arXiv.2608.13355.
References
[Simeon2024] Simeon, De Fabritiis. TensorNet. NeurIPS 36 (2024). https://arxiv.org/abs/2306.06482
[Pelaez2024] Pelaez et al. TorchMD-Net 2.0. J. Chem. Theory Comput. 2024, 20, 4076. https://arxiv.org/abs/2402.17660
[Zariquiey2025] Sabanés Zariquiey et al. QuantumBind-RBFE. https://arxiv.org/abs/2501.01811
[Farr2026] Farr et al. AceFF. https://arxiv.org/abs/2601.00581