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