Screening for New Antidepressant Leads of Multiple Activities by Support Vector Machines
Zsolt Lepp, Takashi Kinoshita, Hiroshi Chuman · Journal of Chemical Information and Modeling · 2005
Virtual screening was carried out against 21 biological targets related to depression by support vector machine classification using the same atom-type descriptors. The models were effective as 0.2-0.8 of theoretical enrichments of the external test data sets could be achieved, depending on the target. The set of predicted active molecules had large diversity and contained examples with high dissimilarity to the compounds of training sets. Filtering the database of known antidepressants by all 21 models it was found that on average compounds were classified active for 2.3 targets.