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--- |
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license: other |
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license_name: license.txt |
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license_link: LICENSE |
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tags: |
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- chemistry |
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- molecular simulations |
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- machine learning potentials |
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- neural network potentials |
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- drug discovery |
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--- |
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# AceFF 1.0 |
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**Organization(s):** Acellera Therapeutics, inc |
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**Contact:** [email protected] |
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**License:** Refer to the accompanying license file for usage rights. |
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## Overview |
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AceFF 1.0 is a next-generation **Neural Network Potential (NNP)** designed for **Relative Binding Free Energy (RBFE)** calculations in drug discovery. |
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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. |
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The model leverages the TensorNet architecture[1] and the NNP software library TorchMD-Net [2] to provide accurate predictions for diverse drug-like compounds, supporting all key chemical elements and charged molecules. |
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AceFF 1.0 improves the stability of molecular dynamics simulations, supports 2 fs timesteps, and achieves state-of-the-art accuracy with fewer outliers in RBFE predictions. |
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## Description |
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AceFF 1.0 is the first version of a new family of potentials released by [Acellera](https://www.acellera.com). |
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It uses [TensorNet](https://proceedings.neurips.cc/paper_files/paper/2023/file/75c2ec5f98d7b2f50ad68033d2c07086-Paper-Conference.pdf) 1-layer trained |
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on Acellera's internal proprietary dataset of molecular forces and energies using the wB97M-V/def2-tzvppd level of theory and VV10 dispersion corrections. |
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The training set was built on [PubChem](https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF). |
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We extracted the SMILES and generated molecules filtering out molecules larger than 20 atoms. |
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We kept only molecules with the elements H, B, C, N, O, F, Si, P, S, Cl, Br, and I, and a formal charge of -1,0,1. |
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Energy are in eV. Forces are in eV/A. |
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## Key Features |
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- **Broad Applicability:** Supports diverse drug-like molecules, including charged species and rare chemical groups. |
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- **High Accuracy:** Benchmark-tested on the JACS dataset, demonstrating performance comparable to or better than MM-based methods (e.g., GAFF2, FEP+). |
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- **Improved Stability:** Enables a 2 fs timestep for NNP/MM simulations, significantly reducing computational costs. |
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- **Integration-Friendly:** Available for RBFE calculations via [HTMD](https://github.com/acellera/htmd). |
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- **Open Science:** The model and all benchmarking data are accessible on GitHub for not-for-profit usage. |
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- **Error Metrics:** AceFF 1.0 exhibits lower RMSE and higher Kendall tau correlations compared to traditional MM force fields across various protein targets.[3] |
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## Usage |
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AceFF 1.0 is designed for use alone or in a NNP/MM approach, where the ligand is treated with the neural network potential and the environment with molecular mechanics. |
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1. Run ML potential molecular simulations of a small molecule using ACEMD with this [tutorial](https://software.acellera.com/acemd/nnp.html) , e.g. to minimize. |
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1. For a tutorial on running mixed protein-ligand simulations, refer to [NNP/MM in ACEMD](https://software.acellera.com/acemd/nnpmm.html). |
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## Applications |
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- **Drug Discovery:** Optimizing lead compounds in hit-to-lead and lead optimization stages using free energy methods. |
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- **Binding Free Energy Calculations:** Accurate and efficient RBFE predictions for diverse molecular systems. |
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- **Molecular dynamics:** Capturing higher-body terms than traditional MM force fields, AceFF can be used for structure minimization and dynamics of small molecules. |
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## Limitations |
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- **Small molecules only**: AceFF 1.0 is trained on specifically curated and extended PubChem data. However, proteins, water, etc are not part of the dataset. AceFF 2.0 will be capable of simulations of proteins. |
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- **Time step**: Use time steps of 2fs to run dynamics with hydrogen mass repartitioning. |
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- **Only -1,0,1 charges**: For simplicity we have trained only on these type of charged molecules; do not use it on +2,-2, etc. AceFF 1.1 will fix this. |
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## References |
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[1] 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 |
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[2] 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 |
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[3] 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). |
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