分子体系自由能地貌图的变分分析及AI算法实现

Variational analysis and AI algorithm implementation of free energy landscapes of molecular system

精确描述复杂分子体系的自由能地貌图是理解和操控其行为,并进一步实现分子设计制造工业化的重要基础.刻画高维空间自由能地貌图的主要挑战是其往往在不同时空间尺度上具有多个层次,每个层次都可能有不止一个亚稳态被相应的自由能垒分开,且跨越路径有可能不止一条.另外很多体系涉及非线性行为,这使得理论解析和直接使用分子模拟都有很大困难.针对这些挑战,多年来研究者们发展了多种多样的增强采样方法,但往往需要很多经验选择和操作,从而一方面使得研究进程较为缓慢,另一方面也让误差控制成为困难.变分虽然在物理、统计和工程中已经被广泛应用并取得巨大成功,但在复杂分子体系中的应用却随着神经网络的发展刚刚开始.本文将对这些探索性工作的主要方向、进展和局限进行简要总结,也对将来的可能发展给出展望,希望能够激发更多对基于变分的分子体系自由能地貌图人工智能算法的关注和努力,促进大分子药物、分子生物机器等实践应用的发展.

Accurate description of the free energy landscape(FES)is the basis for understanding complex molecular systems,and for further realizing molecular design,manufacture and industrialization.Major challenges include multiple metastable states,which usually are separated by high potential barriers and are not linearly separable,and may exist at multiple levels of time and spatial scales.Consequently FES is not suitable for analytical analysis and brute force simulation.To address these challenges,many enhanced sampling methods have been developed.However,utility of them usually involves many empirical choices,which hinders research advancement,and also makes error control very unimportant.Although variational calculus has been widely applied and achieved great success in physics,engineering and statistics,its application in complex molecular systems has just begun with the development of neural networks.This brief review is to summarize the background,major developments,current limitations,and prospects of applying variation in this field.It is hoped to facilitate the AI algorithm development for complex molecular systems in general,and to promote the further methodological development in this line of research in particular.