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  Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics

Köhler, J., Chen, Y., Krämer, A., Clementi, C., & Noé, F. (2023). Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics. Journal of Chemical Theory and Computation, 19(3), 942-952. doi:10.1021/acs.jctc.3c00016.

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JCTC_Köhler et al_2023.pdf (Publisher version), 5MB
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© 2023 The Authors. Published by American Chemical Society.
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 Creators:
Köhler, Jonas, Author
Chen, Yaoyi1, Author                 
Krämer, Andreas, Author
Clementi, Cecilia, Author
Noé, Frank, Author
Affiliations:
1IMPRS for Biology and Computation (Anne-Dominique Gindrat), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479666              

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 Abstract: Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time and length scales inaccessible to all-atom simulations. Parametrizing CG force fields to match all-atom simulations has mainly relied on force-matching or relative entropy minimization, which require many samples from costly simulations with all-atom or CG resolutions, respectively. Here we present flow-matching, a new training method for CG force fields that combines the advantages of both methods by leveraging normalizing flows, a generative deep learning method. Flow-matching first trains a normalizing flow to represent the CG probability density, which is equivalent to minimizing the relative entropy without requiring iterative CG simulations. Subsequently, the flow generates samples and forces according to the learned distribution in order to train the desired CG free energy model via force-matching. Even without requiring forces from the all-atom simulations, flow-matching outperforms classical force-matching by an order of magnitude in terms of data efficiency and produces CG models that can capture the folding and unfolding transitions of small proteins.

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Language(s): eng - English
 Dates: 2023-01-202023-02-14
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1021/acs.jctc.3c00016
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Title: Journal of Chemical Theory and Computation
  Other : JCTC
  Abbreviation : J. Chem. Theory Comput.
Source Genre: Journal
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Publ. Info: Washington, D.C. : American Chemical Society
Pages: - Volume / Issue: 19 (3) Sequence Number: - Start / End Page: 942 - 952 Identifier: ISSN: 1549-9618
CoNE: https://pure.mpg.de/cone/journals/resource/111088195283832