最大值和最小值
强化学习
计算机科学
自适应采样
采样(信号处理)
折叠(DSP实现)
集合(抽象数据类型)
化学空间
人工智能
数学
工程类
药物发现
生物信息学
生物
电气工程
滤波器(信号处理)
计算机视觉
数学分析
统计
蒙特卡罗方法
程序设计语言
作者
Wen‐Hui Shen,Kaiwei Wan,Dechang Li,Huajian Gao,Xinghua Shi
标识
DOI:10.1073/pnas.2414205121
摘要
Enhanced sampling techniques have traditionally encountered two significant challenges: identifying suitable reaction coordinates and addressing the exploration–exploitation dilemma, particularly the difficulty of escaping local energy minima. Here, we introduce Adaptive CVgen, a universal adaptive sampling framework designed to tackle these issues. Our approach utilizes a set of collective variables (CVs) to comprehensively cover the system’s potential evolutionary phase space, generating diverse reaction coordinates to address the first challenge. Moreover, we integrate reinforcement learning strategies to dynamically adjust the generated reaction coordinates, thereby effectively balancing the exploration-exploitation dilemma. We apply this framework to sample the conformational space of six proteins transitioning from completely disordered states to folded states, as well as to model the chemical synthesis process of C60, achieving conformations that perfectly match the standard C60 structure. The results demonstrate Adaptive CVgen’s effectiveness in exploring new conformations and escaping local minima, achieving both sampling efficiency and exploration accuracy. This framework holds potential for extending to various related challenges, including protein folding dynamics, drug targeting, and complex chemical reactions, thereby opening promising avenues for application in these fields.
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