Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method
| Autor: | Akio Kitao, Koji Tsuda, Kei Terayama, Kazuhiro Takemura, Duy Phuoc Tran, Kento Shin |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | ACS Omega ACS Omega, Vol 4, Iss 9, Pp 13853-13862 (2019) |
| ISSN: | 2470-1343 |
| Popis: | This 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. |
| Databáze: | OpenAIRE |
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