Transferable Coarse Graining via Contrastive Learning of Graph Neural Networks.

ABSTRACT: Coarse-grained (CG) force fields are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. These simulations employ simplified models grouping atoms into interaction sites, enabling the study of complex biomolecular systems over biologically relevant timescales. Efforts are underway to develop accurate and transferable CG force fields, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to many-body effects, lack of analytical expressions for the PMF, and limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing

SUBMITTER: Airas J 

PROVIDER: S-EPMC10515757 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature