title: One-class classification as a novel method of ligand-based virtual screening: the case of glycogen synthase kinase 3β inhibitors.
abstract: A virtual screening system based on one-class classification with molecular fingerprints as descriptors is developed and tested on a series of 1226 inhibitors and 209 noninhibitors of glycogen synthase kinase 3β (GSK-3β). The suggested system outperforms the ones based on pharmacophore hypothesis and molecular docking in a retrospective study. However, in a prospective study it should not be used as a sole classifier. The system is exceptionally useful for the identification of new scaffolds among the virtual screening results obtained with other methods.
authors: Department of Chemistry, Moscow State University, Leninskie Gory 1/3, Moscow 119991, Russia.
authors: Pavel V
authors: PV
authors: Karpov
authors: Dmitry I
authors: DI
authors: Osolodkin
authors: Igor I
authors: II
authors: Baskin
authors: Vladimir A
authors: VA
authors: Palyulin
authors: Nikolay S
authors: NS
authors: Zefirov
mesh_terms: D017076:Computer-Aided Design; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology; D038362:Glycogen Synthase Kinase 3 / Q000037:antagonists & inhibitors* / Q000378:metabolism; D000071679:Glycogen Synthase Kinase 3 beta; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D016571:Neural Networks, Computer
chemical_list: D004791:Enzyme Inhibitors; D008024:Ligands; C000605506:GSK3B protein, human; D000071679:Glycogen Synthase Kinase 3 beta; D038362:Glycogen Synthase Kinase 3