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Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method
[Image: see text] 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 tr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6714528/ https://www.ncbi.nlm.nih.gov/pubmed/31497702 http://dx.doi.org/10.1021/acsomega.9b01480 |
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author | Shin, Kento Tran, Duy Phuoc Takemura, Kazuhiro Kitao, Akio Terayama, Kei Tsuda, Koji |
author_facet | Shin, Kento Tran, Duy Phuoc Takemura, Kazuhiro Kitao, Akio Terayama, Kei Tsuda, Koji |
author_sort | Shin, Kento |
collection | PubMed |
description | [Image: see text] 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. |
format | Online Article Text |
id | pubmed-6714528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-67145282019-09-06 Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method Shin, Kento Tran, Duy Phuoc Takemura, Kazuhiro Kitao, Akio Terayama, Kei Tsuda, Koji ACS Omega [Image: see text] 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. American Chemical Society 2019-08-19 /pmc/articles/PMC6714528/ /pubmed/31497702 http://dx.doi.org/10.1021/acsomega.9b01480 Text en Copyright © 2019 American Chemical Society This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License (http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) , which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes. |
spellingShingle | Shin, Kento Tran, Duy Phuoc Takemura, Kazuhiro Kitao, Akio Terayama, Kei Tsuda, Koji Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method |
title | Enhancing Biomolecular Sampling
with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation
Method |
title_full | Enhancing Biomolecular Sampling
with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation
Method |
title_fullStr | Enhancing Biomolecular Sampling
with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation
Method |
title_full_unstemmed | Enhancing Biomolecular Sampling
with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation
Method |
title_short | Enhancing Biomolecular Sampling
with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation
Method |
title_sort | enhancing biomolecular sampling
with reinforcement learning: a tree search molecular dynamics simulation
method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6714528/ https://www.ncbi.nlm.nih.gov/pubmed/31497702 http://dx.doi.org/10.1021/acsomega.9b01480 |
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