Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.

ABSTRACT: Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol^-1 Å^-1 from the reference values, and the MLP-based free

SUBMITTER: Yao S 

PROVIDER: S-EPMC9900604 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature