Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)

A kernel-based approach to molecular conformation analysis

  • 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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics - number of accesses to the document
Metadaten
Author:Stefan Klus, Andreas Bittracher, Ingmar Schuster, Christof Schütte
Document Type:Article
Parent Title (English):Journal of Chemical Physics
Volume:149
Issue:24
Date of first Publication:2018/12/28
DOI:https://doi.org/10.1063/1.5063533
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.