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• W3099423575 abstract "Abstract There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput 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." @default.
• W3099423575 created "2020-11-23" @default.
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• W3099423575 date "2018-01-02" @default.
• W3099423575 modified "2023-10-13" @default.
• W3099423575 title "VAMPnets for deep learning of molecular kinetics" @default.
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• W3099423575 doi "https://doi.org/10.1038/s41467-017-02388-1" @default.
• W3099423575 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5750224" @default.
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