用量子精度计算配体应变能，提升药物设计准确性
Strain Problems got you in a Twist? Try StrainRelief: A Quantum-Accurate Tool for Ligand Strain Calculations
- 基于MACE神经网络势，结合DFT数据训练
- 与DFT对比误差小于1.4 kcal/mol，优于其他模型
- 适合药物研发团队快速评估配体构象能量
配体应变能是基于结构的小分子药物设计中的关键参数，指配体在结合与未结合构象间的能量差。大多数蛋白质-小分子共晶结构中的配体以低应变构象结合，使应变能成为药物设计的有效筛选指标。本文提出StrainRelief工具，利用在大规模密度泛函理论（DFT）计算数据上训练的MACE神经网络势（NNP），实现中性分子配体应变能的量子级精度计算。结果表明，该工具对应变能差异的估算与DFT相比误差低于1.4 kcal/mol，优于现有其他神经网络势模型。这凸显了神经网络势在药物发现中的价值，并为研发团队提供了一项高效可靠的应变能计算工具。
原文摘要 · Abstract (English)
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.
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