物理学报2023，Vol.72Issue(24)：319-331,13.DOI:10.7498/aps.72.20231624
生物分子模拟中的机器学习方法
Machine learning in molecular simulations of biomolecules
摘要
Abstract
Molecular simulation has already become a powerful tool for studying life principles at a molecular level.The past 50-year researches show that molecular simulation has been able to quantitatively characterize the kinetic and thermodynamic properties of complex molecular processes,such as protein folding and conformational changes.In recent years,the application of machine learning algorithms represented by deep learning has further promoted the development of molecular simulation.This work reviews machine learning methods in biomolecular simulation,focusing on the important progress made by machine learning algorithms in improving the accuracy of molecular force fields,the efficiency of molecular simulation conformation sampling,and also the processing of high-dimensional simulation data.The future researches to further overcome the bottleneck of accuracy and efficiency of molecular simulation,expand the scope of molecular simulation,and realize the integration of computational simulation and experimental based on machine learning technique is prospected.关键词
生物大分子/分子模拟/机器学习/增强采样/多尺度模型Key words
bio-molecules/molecular simulations/machine learning/enhanced sampling/multiscale model引用本文复制引用
管星悦,黄恒焱,彭华祺,刘彦航,李文飞,王炜..生物分子模拟中的机器学习方法[J].物理学报,2023,72(24):319-331,13.基金项目
国家自然科学基金(批准号:11974173)资助的课题.Project supported by the National Natural Science Foundation of China(Grant No.11974173). （批准号:11974173）
