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

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

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.

• 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
• 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