Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery.

ABSTRACT: The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity and selectivity for a receptor subtype was compared with explicit information on ligand-receptor interactions from induced-fit docking. Both methods have proved their usefulness in drug response predictions. Yet, their successful combination still requires allosteric/orthosteric assignment of ligands from datasets. Our ligand datasets included activities of two members of the secretin receptor family: GCGR and GLP-1R. Simultaneous activation of two or three receptors of this family by dual or triple agonists is not a typical kind of information i

SUBMITTER: Mizera M 

PROVIDER: S-EPMC8071054 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature