Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries
Selective multi-target serotonin reuptake inhibitors enhance antidepressant efficacy. Their discovery can be facilitated by multiple methods, including in-silico ones. In this study, we developed and tested an in-silico method, combinatorial support vector machines (COMBI-SVMs), for virtual screening (VS) multi-target serotonin reuptake inhibitors of seven target pairs (serotonin transporter paired with noradrenaline transporter, H3 receptor, 5-HT1A receptor, 5-HT1B receptor, 5-HT2C receptor, Melanocortin 4 receptor and Neurokinin 1 receptor respectively) from large compound libraries. COMBI-SVMs trained with 917-1,951 individual target inhibitors correctly identified 22%∼83.3% (majority >31.1%) of the 6∼216 dual inhibitors collected from literatures as independent testing sets. COMBI-SVMs showed moderate to good target selectivity in misclassifying as dual inhibitors 2.2%∼29.8% (majority <15.4%) of the individual target inhibitors of the same target pair and 0.58%-7.1% of the other 6 targets outside the target pair. COMBI-SVMs showed low dual inhibitor false hit rates (0.006%∼0.056%, 0.042%∼0.21%, 0.2%∼4%) in screening 17 million PubChem compounds, 168,000 MDDR compounds, and 7-8,181 MDDR compounds similar to the dual inhibitors. Compared with similarity searching, k-NN and PNN methods, COMBI-SVM produced comparable dual inhibitor yields, similar target selectivity, and lower false hit rate in screening 168,000 MDDR compounds. The annotated classes of many COMBI-SVMs identified MDDR virtual hits correlate with the reported effects of their predicted targets. COMBI-SVM is potentially useful for searching selective multi-target agents without explicit knowledge of these agents.