物理学报2023，Vol.72Issue(24)：236-247,12.DOI:10.7498/aps.72.20231319
生物大分子过渡态搜索算法及其中的机器学习
Transition state searching for complex biomolecules:Algorithms and machine learning
摘要
Abstract
Transition state is a key concept for chemists to understand and fine-tune the conformational changes of large biomolecules.Due to its short residence time,it is difficult to capture a transition state via experimental techniques.Characterizing transition states for a conformational change therefore is only achievable via physics-driven molecular dynamics simulations.However,unlike chemical reactions which involve only a small number of atoms,conformational changes of biomolecules depend on numerous atoms and therefore the number of their coordinates in our 3D space.The searching for their transition states will inevitably encounter the curse of dimensionality,i.e.the reaction coordinate problem,which invokes the invention of various algorithms for solution.Recent years,new machine learning techniques and the incorporation of some of them into the transition state searching methods emerged.Here,we first review the design principle of representative transition state searching algorithms,including the collective-variable(CV)-dependent gentlest ascent dynamics,finite temperature string,fast tomographic,travelling-salesman based automated path searching,and the CV-independent transition path sampling.Then,we focus on the new version of TPS that incorporates reinforcement learning for efficient sampling,and we also clarify the suitable situation for its application.Finally,we propose a new paradigm for transition state searching,a new dimensionality reduction technique that preserves transition state information and combines gentlest ascent dynamics.关键词
过渡态搜索/温和爬升动力学/路径算法/强化学习/生成模型Key words
transition state/gentlest ascent dynamics/path methods/reinforcement learning/generative models引用本文复制引用
杨建宇,席昆,竺立哲..生物大分子过渡态搜索算法及其中的机器学习[J].物理学报,2023,72(24):236-247,12.基金项目
国家自然科学基金(批准号:31971179)和深圳市科技创新委员会(批准号:JCYJ20200109150003938,RCYX20200714114645019)资助的课题.Project supported by the National Natural Science Foundation of China(Grant No.31971179)and the Science Technology and Innovation Commission of Shenzhen Municipality,China(Grant Nos.JCYJ20200109150003938,RCYX20200714114645019). （批准号:31971179）
