<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>7107</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>24</issue>
    <volume>149</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2018-12-28</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A kernel-based approach to molecular conformation analysis</title>
    <abstract language="eng">We present a novel machine learning approach to understanding conformation dynamics of biomolecules. The approach combines kernel-based techniques that are popular in the machine learning community with transfer operator theory for analyzing dynamical systems in order to identify conformation dynamics based on molecular dynamics simulation data. We show that many of the prominent methods like Markov State Models, EDMD, and TICA can be regarded as special cases of this approach and that new efficient algorithms can be constructed based on this derivation. The results of these new powerful methods will be illustrated with several examples, in particular the alanine dipeptide and the protein NTL9.</abstract>
    <parentTitle language="eng">Journal of Chemical Physics</parentTitle>
    <identifier type="doi">10.1063/1.5063533</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2018-12-04</enrichment>
    <author>Stefan Klus</author>
    <submitter>Erlinda Körnig</submitter>
    <author>Andreas Bittracher</author>
    <author>Ingmar Schuster</author>
    <author>Christof Schütte</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
    <collection role="projects" number="ECMATH-CH2">ECMATH-CH2</collection>
    <collection role="projects" number="SFB-1114-B3">SFB-1114-B3</collection>
  </doc>
</export-example>
