摘要
分子动力学模拟是解析复杂分子体系结构、热力学和动力学性质的重要工具,但其应用受限于计算效率,模拟和真实过程的时间尺度往往存在落差.随着深度学习技术的飞速发展,利用人工智能加速分子动力学模拟成为可能.本文探讨了人工智能在不同层面上实现加速的潜力:时域加速突破步长限制,序列加速捕捉构象跃迁,空间采样优化构象遍历.我们也讨论了现有方法在物理一致性与跨尺度适应性方面的诸多困难与挑战.未来,多尺度方法与物理约束框架的有机融合,有望协同提升精度、效率与可解释性,推动分子模拟智能化发展,并进一步拓展其在机制解析与筛选设计上的应用潜力.
Molecular dynamics(MD)simulation is a powerful computational tool for investigating the structural,thermodynamic and dynamic properties of complex molecular systems.However,its practical application is limited by computational inefficiencies,particularly the gap between the time scales of chemical processes and those accessible through simulations.With the advent of deep learning,artificial intelligence(AI)has emerged as a promising solution for accelerating MD simulations.This review examines AI-driven acceleration approaches at various levels:temporal acceleration to overcome step-size limitations,sequential acceleration to capture conformational transitions,and spatial sampling for optimized conformation exploration.We also discuss the challenges of maintaining physical consistency and multi-scale adaptability in current methods.Future advancements integrating multi-scale approaches with physical constraints are expected to enhance the accuracy,efficiency,and interpretability,thereby expanding the utility of MD simulations in mechanistic analysis and molecular design.
作者
钱润彤
杨世悦
宋子林
黄晶
Runtong Qian;Shiyue Yang;Zilin Song;Jing Huang(Westlake AI Therapeutics Lab,Westlake Laboratory of Life Sciences and Biomedicine,Hangzhou 310024,China;School of Life Sciences,Westlake University,Hangzhou 310024,China)
出处
《中国科学:化学》
北大核心
2025年第6期1688-1703,共16页
SCIENTIA SINICA Chimica
基金
国家自然科学基金(编号:32171247)
浙江省“尖兵”“领雁”研发攻关计划(编号:2023C03109,2024SSYS0036)
博士后科学基金(编号:2023M743138)资助项目。
关键词
分子动力学模拟
人工智能
构象系综
构象动力学
深度学习
人工智能驱动的科学研究
molecular dynamics simulation
artificial intelligence
conformational ensemble
conformational dynamics
deep learning
AI for science
作者简介
通讯作者:黄晶,E-mail:huangjing@westlake.edu.cn。
