Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation.
Bai XueQingyi YangQiaochu ZhangXiao WanDong FangXiaolu LinGuangxu SunGianpaolo GobboFenglei CaoAlan M MathiowetzBenjamin J BurkeRobert A KumpfBrajesh K RaiGeoffrey P F WoodFrank C PickardJunmei WangPeiyu ZhangJian MaYide Alan JiangShuhao WenXinjun HouJunjie ZouMingjun YangPublished in: Journal of chemical theory and computation (2023)
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 ΔΔ G and 0.92 kcal/mol for Δ G 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.
Keyphrases
- small molecule
- molecular dynamics
- monte carlo
- drug discovery
- density functional theory
- electronic health record
- healthcare
- protein protein
- single molecule
- big data
- affordable care act
- mental health
- molecular dynamics simulations
- minimally invasive
- binding protein
- clinical practice
- data analysis
- machine learning
- transcription factor
- deep learning
- virtual reality
- quantum dots