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GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling

Louison, Keverne A.; Louison, Keverne A; Dryden, Ian L.; Laughton, Charles A.

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Authors

Keverne A. Louison

Keverne A Louison

IAN DRYDEN IAN.DRYDEN@NOTTINGHAM.AC.UK
Professor of Statistics

CHARLES LAUGHTON CHARLES.LAUGHTON@NOTTINGHAM.AC.UK
Professor of Computational Pharmaceutical Science



Abstract

We describe a general approach to transforming molecular models between different levels of resolution, based on machine learning methods. The approach uses a matched set of models at both levels of resolution for training, but requires only the coordinates of their particles and no side information (e.g., templates for substructures, defined mappings, or molecular mechanics force fields). Once trained, the approach can transform further molecular models of the system between the two levels of resolution in either direction with equal facility.

Citation

Louison, K. A., Louison, K. A., Dryden, I. L., & Laughton, C. A. (2021). GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling. Journal of Chemical Theory and Computation, 17(12), 7930-7937. https://doi.org/10.1021/acs.jctc.1c00735

Journal Article Type Article
Acceptance Date Nov 8, 2021
Online Publication Date Dec 1, 2021
Publication Date Dec 14, 2021
Deposit Date Nov 12, 2021
Publicly Available Date Dec 2, 2022
Journal Journal of Chemical Theory and Computation
Print ISSN 1549-9618
Electronic ISSN 1549-9626
Publisher American Chemical Society
Peer Reviewed Peer Reviewed
Volume 17
Issue 12
Pages 7930-7937
DOI https://doi.org/10.1021/acs.jctc.1c00735
Public URL https://nottingham-repository.worktribe.com/output/6681933
Publisher URL https://pubs.acs.org/doi/full/10.1021/acs.jctc.1c00735

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