Atomic partial charge predictions for furanoses by random forest regression with atom type symmetry function.

ABSTRACT: Furanoses that are components for many important biomolecules have complicated conformational spaces due to the flexible ring and exo-cyclic moieties. Machine learning algorithms, which require descriptors as structural inputs, can be used to efficiently compute conformational adaptive (CA) charges to capture the electrostatic potential variations caused by the conformational changes in the molecular mechanics (MM) calculations. In the present study, we introduced atom type symmetry function (ATSF) developed based on atom centered symmetry function (ACSF) for describing conformations for furanoses, in which atoms were categorized by atom types defined by their properties or connectivity in classic molecular mechanics (MM) force field parameters to generate a suitable coordinate size

SUBMITTER: Wang X 

PROVIDER: S-EPMC9048215 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature