{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vampnets-deep-learning-of-molecular-kinetics","title":"VAMPnets: Deep learning of molecular kinetics","arxiv_id":"1710.06012","date":"2017-10-16","proceeding":null,"authors":["Andreas Mardt","Luca Pasquali","Hao Wu","Frank Noé"],"abstract":"There is an increasing demand for computing the relevant structures,\nequilibria and long-timescale kinetics of biomolecular processes, such as\nprotein-drug binding, from high-throughput molecular dynamics simulations.\nCurrent methods employ transformation of simulated coordinates into structural\nfeatures, dimension reduction, clustering the dimension-reduced data, and\nestimation of a Markov state model or related model of the interconversion\nrates between molecular structures. This handcrafted approach demands a\nsubstantial amount of modeling expertise, as poor decisions at any step will\nlead to large modeling errors. Here we employ the variational approach for\nMarkov processes (VAMP) to develop a deep learning framework for molecular\nkinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire\nmapping from molecular coordinates to Markov states, thus combining the whole\ndata processing pipeline in a single end-to-end framework. Our method performs\nequally or better than state-of-the art Markov modeling methods and provides\neasily interpretable few-state kinetic models.","url_abs":"http://arxiv.org/abs/1710.06012v2","url_pdf":"http://arxiv.org/pdf/1710.06012v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vampnets-deep-learning-of-molecular-kinetics","repo_url":"https://github.com/markovmodel/deeptime","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1710.06012","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.06012"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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