拉伤
扭转
配体(生物化学)
量子
数学
计算机科学
物理
量子力学
生物
几何学
遗传学
解剖
受体
作者
Ewan R. S. Wallace,Nathan C. Frey,Joshua A. Rackers
标识
DOI:10.1021/acs.jcim.5c00586
摘要
Ligand strain energy, the energy difference between the bound and unbound conformations of a ligand, is an important component of structure-based small molecule drug design. A large majority of observed ligands in protein-small molecule cocrystal structures bind in low-strain conformations, making strain energy a useful filter for structure-based drug design. In this work we present a tool for calculating ligand strain with a high accuracy. StrainRelief uses a MACE neural network potential (NNP), trained on a large database of density functional theory (DFT) calculations to estimate ligand strain of neutral molecules with quantum accuracy. We show that this tool estimates strain energy differences relative to DFT to within 1.4 kcal/mol, more accurately than alternative NNPs. These results highlight the utility of NNPs in drug discovery, and provide a useful tool for drug discovery teams.
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