AceFF-2.0

Acellera AceFF 2.0

Organization(s): Acellera Therapeutics, inc
Contact: [email protected]
License: apache 2.0

This model was presented in the paper AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules.

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Overview

Acellera AceFF-2.0 is a next-generation machine learning interatomic potential (MLIP) designed for small molecules. It addresses key limitations of traditional molecular mechanics (MM) force fields and earlier NNP models, including restricted atom types, limited charge support, and computational inefficiencies.

The model leverages the TensorNet v2 architecture [Farr2026], an evolution of TensorNet [Simeon2024]. It is implemented ubn the MLIP software library TorchMD-Net [Pelaez2024, Farr2026] to provide accurate predictions for diverse drug-like compounds, supporting all key chemical elements and charged molecules. Acellera AceFF 2.0 is the second version of a new family of potentials released by Acellera trained on Acellera's internal proprietary dataset of molecular forces and energies using the wB97M-V/def2-tzvppd level of theory and VV10 dispersion corrections. The training set was built on PubChem. We extracted the SMILES and generated molecules, filtering out molecules larger than 30 atoms. We kept only molecules with the elements H, B, C, N, O, F, Si, P, S, Cl, Br, and I.

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Bechmarks

Wiggle150

The table shows the results on the Wiggle150 benchmark. We include AIMNet2 and ANI-2x for comparison.

Performance of MLIPs on Wiggle150 benchmark

Many more benchmarks are reported in the paper, including torsion scans and speed.

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Key Features

• Broad Applicability: Supports diverse drug-like molecules, including charged species and rare chemical groups.
• High Accuracy: Benchmarked on a wide array of test cases.
• Improved Stability: Enables a 2 fs timestep for NNP/MM simulations, significantly reducing computational costs.
• Integration-Friendly: Available for RBFE calculations via HTMD.
• Open Science: The model is available with an Apache 2.0 license.

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Usage

1. Example notebooks are available in Google Colab, demonstrating the use of Acellera AceFF with OpenMM and ASE.

• Single point energy calculator with ASE.
• Batch minimization and molecular dynamics with PyTorch.
• Running MD for a small molecule in vacuum with OpenMM.
• Running MD for a protein ligand complex using MM/ML mechanical embedding scheme with OpenMM.

1. Run ML potential molecular simulations of a small molecule using ACEMD with this tutorial, e.g., to minimize.
2. For a tutorial on running mixed protein-ligand simulations, refer to NNP/MM in ACEMD.

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Applications

• Drug Discovery: Optimizing lead compounds in hit-to-lead and lead optimization stages using free energy methods.
• Binding Free Energy Calculations: Accurate and efficient RBFE predictions for diverse molecular systems[Zariquiey2025].
• Molecular dynamics: Capturing higher-body terms than traditional MM force fields, Acellera AceFF can be used for structure minimization and dynamics of small molecules.

Limitations

• Small molecules only: Acellera AceFF-2.0 is trained on specifically curated and extended PubChem data. However, proteins, water, etc are not part of the dataset right now.
• Time step: Use time steps of 2fs to run dynamics with hydrogen mass repartitioning.
• Only -2,-1,0,1,2 charges: For simplicity, we have trained only on these types of charged molecules.

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References

[Simeon2024] Simeon, Guillem, and Gianni De Fabritiis, Tensornet: Cartesian tensor representations for efficient learning of molecular potentials, Advances in Neural Information Processing Systems 36 (2024), https://arxiv.org/abs/2306.06482

[Pelaez2024] Raul P. Pelaez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni De Fabritiis, TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations, J. Chem. Theory Comput. 2024, 20, 10, 4076–4087, https://arxiv.org/abs/2402.17660

[Zariquiey2025] Francesc Sabanés Zariquiey, Stephen E. Farr, Stefan Doerr, Gianni De Fabritiis, QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials, https://arxiv.org/abs/2501.01811 (2025).

[Farr2026] Stephen E. Farr, Stefan Doerr, Antonio Mirarchi, Francesc Sabanes Zariquiey, Gianni De Fabritiis, AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules, https://arxiv.org/abs/2601.00581