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 co-crystal 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.

翻译：配体应变能，即配体在结合态与非结合态构象之间的能量差，是基于结构的小分子药物设计中的重要组成部分。在蛋白质-小分子共晶结构中观察到的大多数配体均以低应变构象结合，这使得应变能成为基于结构的药物设计中有用的筛选指标。本研究提出了一种高精度计算配体应变的工具。StrainRelief采用基于大规模密度泛函理论（DFT）计算数据库训练的MACE神经网络势能（NNP），以量子精度估算中性分子的配体应变。我们证明该工具估算的应变能差与DFT结果的偏差在1.4 kcal/mol以内，其精度优于其他神经网络势能方法。这些结果凸显了神经网络势能在药物发现中的应用价值，并为药物研发团队提供了实用工具。