Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks.

ABSTRACT: Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, 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 the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demo

SUBMITTER: Airas J 

PROVIDER: S-EPMC10755853 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature