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Download fileEnhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method
journal contribution
posted on 2019-08-19, 13:43 authored by Kento Shin, Duy Phuoc Tran, Kazuhiro Takemura, Akio Kitao, Kei Terayama, Koji TsudaThis paper proposes
a novel molecular simulation method, called tree search molecular
dynamics (TS-MD), to accelerate the sampling of conformational transition
pathways, which require considerable computation. In TS-MD, a tree
search algorithm, called upper confidence bounds for trees, which
is a type of reinforcement learning algorithm, is applied to sample
the transition pathway. By learning from the results of the previous
simulations, TS-MD efficiently searches conformational space and avoids
being trapped in local stable structures. TS-MD exhibits better performance
than parallel cascade selection molecular dynamics, which is one of
the state-of-the-art methods, for the folding of miniproteins, Chignolin
and Trp-cage, in explicit water.