Computational Prediction of Compound-Protein Interactions for Orphan Targets Using CGBVS.

ABSTRACT: A variety of Artificial Intelligence (AI)-based (Machine Learning) techniques have been developed with regard to in silico prediction of Compound-Protein interactions (CPI)-one of which is a technique we refer to as chemical genomics-based virtual screening (CGBVS). Prediction calculations done via pairwise kernel-based support vector machine (SVM) is the main feature of CGBVS which gives high prediction accuracy, with simple implementation and easy handling. We studied whether the CGBVS technique can identify ligands for targets without ligand information (orphan targets) using data from G protein-coupled receptor (GPCR) families. As the validation method, we tested whether the ligand prediction was correct for a virtual orphan GPCR in which all ligand information for one selected target

SUBMITTER: Kanai C 

PROVIDER: S-EPMC8434178 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature