Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations.

ABSTRACT: Implicit solvent models, such as Poisson-Boltzmann models, play important roles in computational studies of biomolecules. A vital step in almost all implicit solvent models is to determine the solvent-solute interface, and the solvent excluded surface (SES) is the most widely used interface definition in these models. However, classical algorithms used for computing SES are geometry-based, so that they are neither suitable for parallel implementations nor convenient for obtaining surface derivatives. To address the limitations, we explored a machine learning strategy to obtain a level set formulation for the SES. The training process was conducted in three steps, eventually leading to a model with over 95% agreement with the classical SES. Visualization of tested molecular surfaces shows t

SUBMITTER: Wei H 

PROVIDER: S-EPMC9132718 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature