Papers › Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical...

Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage

23 Aug 2024arXiv:2408.12817archive 2025-07-28

Tianze Zheng, Ailun Wang, Xu Han, Yu Xia, Xingyuan Xu, Jiawei Zhan, Yu Liu, Yang Chen, Zhi Wang, Xiaojie Wu, Sheng Gong, Wen Yan

A force field is a critical component in molecular dynamics simulations for computational drug discovery. It must achieve high accuracy within the constraints of molecular mechanics' (MM) limited functional forms, which offers high computational efficiency. With the rapid expansion of synthetically accessible chemical space, traditional look-up table approaches face significant challenges. In this study, we address this issue using a modern data-driven approach, developing ByteFF, an Amber-compatible force field for drug-like molecules. To create ByteFF, we generated an expansive and highly diverse molecular dataset at the B3LYP-D3(BJ)/DZVP level of theory. This dataset includes 2.4 million optimized molecular fragment geometries with analytical Hessian matrices, along with 3.2 million torsion profiles. We then trained an edge-augmented, symmetry-preserving molecular graph neural network (GNN) on this dataset, employing a carefully optimized training strategy. Our model predicts all bonded and non-bonded MM force field parameters for drug-like molecules simultaneously across a broad chemical space. ByteFF demonstrates state-of-the-art performance on various benchmark datasets, excelling in predicting relaxed geometries, torsional energy profiles, and conformational energies and forces. Its exceptional accuracy and expansive chemical space coverage make ByteFF a valuable tool for multiple stages of computational drug discovery.

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get_distance_vec bytedance/byteff/byteff/forcefield/ff_kernels.py official repository ran Apache-2.0 (permissive) · 44ca3b369b0c705e · report
get_distance_vec_0 bytedance/byteff/byteff/forcefield/ff_kernels.py official repository ran fingerprinted Apache-2.0 (permissive) · 32d80b66b49ecf0a · report
is_charge_key bytedance/byteff/byteff/mol/conformer.py official repository ran Apache-2.0 (permissive) · 332da5d5ea75d06a · report
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is_force_key bytedance/byteff/byteff/mol/conformer.py official repository ran Apache-2.0 (permissive) · 129f70ca7b3403f4 · report
reduce_batch_energy bytedance/byteff/byteff/forcefield/ff_kernels.py official repository ran Apache-2.0 (permissive) · 2fa746bfb2cfdc66 · report
set_grad_max bytedance/byteff/byteff/train/loss.py official repository ran fingerprinted Apache-2.0 (permissive) · 130b426ef2bba92e · report
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to_global_edge_idx bytedance/byteff/byteff/model/layers.py official repository unverified Apache-2.0 (permissive) · ce337e49ce3628a2 · report
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Computational EfficiencyDrug DiscoveryGraph Neural Network

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Graph Neural Network

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