机器学习增强的量子-经典结合自由能计算
Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies
AI总结：
本文提出自动化QM/MM采样与机器学习势相结合的工作流，通过适配QM/MM数据的元素包容型原子中心对称函数描述符，实现含过渡金属药物的高效炼金术结合自由能计算。
AI中文摘要：
结合自由能是理解和预测蛋白质-药物相互作用强度的关键要素。尽管经典自由能模拟对许多纯有机配体能给出良好结果，但含有过渡金属原子的药物通常需要量子化学方法才能准确描述。我们提出一种通用且自动化的工作流：通过杂化量子力学/分子力学（QM/MM）计算对势能面进行采样，并基于QM能量和力训练机器学习（ML）势，以实现高效的炼金术自由能模拟。为高效表示包含多种不同化学元素的体系，并兼顾QM原子与MM原子描述方式的差异，我们提出将包容元素的原子中心对称函数扩展为适用于QM/MM数据的ML描述符。该ML势方法考虑了静电嵌入和长程静电作用。我们在已被充分研究的髓系细胞白血病1蛋白与抑制剂19G的蛋白质-配体复合物，以及作用于葡萄糖调节蛋白78的抗癌药物NKP1339上，证明了该工作流的适用性。
英文摘要：
Binding free energies are a key element in understanding and predicting the strength of protein--drug interactions. While classical free energy simulations yield good results for many purely organic ligands, drugs including transition metal atoms often require quantum chemical methods for an accurate description. We propose a general and automated workflow that samples the potential energy surface with hybrid quantum mechanics/molecular mechanics (QM/MM) calculations and trains a machine learning (ML) potential on the QM energies and forces to enable efficient alchemical free energy simulations. To represent systems including many different chemical elements efficiently and to account for the different description of QM and MM atoms, we propose an extension of element-embracing atom-centered symmetry functions for QM/MM data as an ML descriptor. The ML potential approach takes electrostatic embedding and long-range electrostatics into account. We demonstrate the applicability of the workflow on the well-studied protein--ligand complex of myeloid cell leukemia 1 and the inhibitor 19G and on the anti-cancer drug NKP1339 acting on the glucose-regulated protein 78.
