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            Machine Learning in Biomolecular Simulations

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            Contributor(s)
            Verkhivker, Gennady (editor)
            Spiwok, Vojtech (editor)
            Gervasio, Francesco L. (editor)
            Language
            English
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            Abstract
            This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact
            URI
            https://directory.doabooks.org/handle/20.500.12854/73291
            Keywords
            machine learning; molecular dynamics computer simulation; molecular modeling; intrinsically disordered proteins; ligand design; collective variable; sampling enhancement; non-linear dimensionality reduction; kinetics
            DOI
            10.3389/978-2-88963-136-0
            ISBN
            9782889631360
            Publisher
            Frontiers Media SA
            Publisher website
            www.frontiersin.org
            Publication date and place
            2019
            Classification
            Science: general issues
            Pages
            129
            • Imported or submitted locally

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            • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

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            Credits


            • logo Investir l'avenirInvestir l'avenir
            • logo MESRIMESRI
            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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