title: Virtual screening models for finding novel antidepressants.
abstract: Virtual screening was carried out against various biological targets related to depression by support vector machine classification using the atom-type descriptors. The models were effective as over 75 and 95% of the molecules in external test datasets could be correctly classified, depending on target. Antidepressant compounds had predicted activity against 2.3 targets, on average. An introduction is given to virtual screening and the results of classification experiments are presented.
authors.affiliation: Department of Molecular Analytical Chemistry, Institute of Health Biosciences, The University of Tokushima Graduate School, Japan.
authors.forename: Zsolt
authors.initials: Z
authors.lastname: Lepp
authors.forename: Takashi
authors.initials: T
authors.lastname: Kinoshita
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Chuman
mesh_terms: D000465:Algorithms; D000928:Antidepressive Agents / Q000737:chemistry / Q000494:pharmacology*; D019295:Computational Biology; D003198:Computer Simulation; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008956:Models, Chemical; D011237:Predictive Value of Tests
chemical_list: D000928:Antidepressive Agents