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DOI | 10.1038/s41467-018-05845-7 |
VAMPnets for deep learning of molecular kinetics | |
Mardt, Andreas; Pasquali, Luca; Wu, Hao; Noe, Frank | |
2018-01-02 | |
发表期刊 | NATURE COMMUNICATIONS |
ISSN | 2041-1723 |
出版年 | 2018 |
卷号 | 9 |
文章类型 | Article |
语种 | 英语 |
国家 | Germany |
英文摘要 | There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from highthroughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension- reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000419304800001 |
WOS关键词 | MARKOV STATE MODELS ; CONFORMATIONAL DYNAMICS ; VARIATIONAL APPROACH ; SYSTEMS ; SIMULATIONS ; REDUCTION ; NETWORKS ; MAPS |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/203958 |
专题 | 资源环境科学 |
作者单位 | Free Univ Berlin, Dept Math & Comp Sci, Arnimallee 6, D-14195 Berlin, Germany |
推荐引用方式 GB/T 7714 | Mardt, Andreas,Pasquali, Luca,Wu, Hao,et al. VAMPnets for deep learning of molecular kinetics[J]. NATURE COMMUNICATIONS,2018,9. |
APA | Mardt, Andreas,Pasquali, Luca,Wu, Hao,&Noe, Frank.(2018).VAMPnets for deep learning of molecular kinetics.NATURE COMMUNICATIONS,9. |
MLA | Mardt, Andreas,et al."VAMPnets for deep learning of molecular kinetics".NATURE COMMUNICATIONS 9(2018). |
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