Predicting partition coefficients for the SAMPL7 physical property challenge using the ClassicalGSG method.

ABSTRACT: The prediction of [Formula: see text] values is one part of the statistical assessment of the modeling of proteins and ligands (SAMPL) blind challenges. Here, we use a molecular graph representation method called Geometric Scattering for Graphs (GSG) to transform atomic attributes to molecular features. The atomic attributes used here are parameters from classical molecular force fields including partial charges and Lennard-Jones interaction parameters. The molecular features from GSG are used as inputs to neural networks that are trained using a "master" dataset comprised of over 41,000 unique [Formula: see text] values. The specific molecular targets in the SAMPL7 [Formula: see text] prediction challenge were unique in that they all contained a sulfonyl moeity. This motivated a set of Cl

SUBMITTER: Donyapour N 

PROVIDER: S-EPMC8295205 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature