Virtual screening models for finding novel antidepressants.
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.
Lepp
Zsolt
Z
Department of Molecular Analytical Chemistry, Institute of Health Biosciences, The University of Tokushima Graduate School, Japan.
Kinoshita
Takashi
T
Chuman
Hiroshi
H
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
D000928:Antidepressive Agents