GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds.

ABSTRACT: The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molecular association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse molecular featurization methods to describe the input molecules. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews' correlation coefficient scores (MCC). Our data suggest that combined with molecular fingerprinting, ensemble decision trees and gradient boo

SUBMITTER: Ahmed M 

PROVIDER: S-EPMC8097070 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature