A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists.

ABSTRACT:

Background

G-protein coupled receptors (GPCRs) sense and transmit extracellular signals into the intracellular machinery by regulating G proteins. GPCR malfunctions are associated with a variety of signaling-related diseases, including cancer and diabetes; at least a third of the marketed drugs target GPCRs. Thus, characterization of their signaling and regulatory mechanisms is crucial for the development of effective drugs.

Results

In this study, we developed a machine learning model to identify GPCR agonists and antagonists. We designed two-step prediction models: the first model identified the ligands binding to GPCRs and the second model classified the ligands as agonists or antagonists. Using 990 selected subset features from 5270 molecular descriptors calculated from 4

SUBMITTER: Oh J 

PROVIDER: S-EPMC9389651 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature