期刊：Journal of Chemical Theory and Computation 2024, 20, 2, 799–818

作者：Bai Xue, Qingyi Yang, Qiaochu Zhang, Xiao Wan, Dong Fang, Xiaolu Lin, Guangxu Sun, Gianpaolo Gobbo, Fenglei Cao, Alan M. Mathiowetz, Benjamin J. Burke, Robert A. Kumpf, Brajesh K. Rai, Geoffrey P. F. Wood, Frank C. Pickard IV, Junmei Wang, Peiyu Zhang, Jian Ma, Yide Alan Jiang, Shuhao Wen, Xinjun Hou*, Junjie Zou*, Mingjun Yang*

时间：2023.12.29

Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein–ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG and 0.92 kcal/mol for ΔG on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.