virtual screening of multi-target serotonin reuptake inhibitors from large chemical libraries.

Shi Z, Ma X H, Qin C, Jia J, Jiang Y Y, Tan C Y, Chen Y Z "Journal of molecular graphics & modelling "

The main applications of virtual chemical screening include the selection of a minimal receptor-relevant subset of a chemical library with a maximal chemical diversity. We have previously reported that the combination of ligand-centric and receptor-centric virtual screening methods may provide a compromise between computational time and accuracy during the hit enrichment process.

In the present work, we propose a "progressive distributed docking" method that improves the virtual screening process using an iterative combination of shape-matching and docking steps. Known ligands with low docking scores were used as initial 3D templates for the shape comparisons with the chemical library. Next, new compounds with good template shape matches and low receptor docking scores were selected for the next round of shape searching and docking.

The present iterative virtual screening process was tested for enriching Peroxisome proliferator-activated receptor and Phosphoinositide 3-kinase relevant compounds from a selected subset of the chemical libraries. It was demonstrated that the iterative combination improved the lead-hopping practice by improving the chemical diversity in the selected list of virtual hits.

Selective multi-target serotonin reuptake inhibitors enhance antidepressant efficacy. Their discovery can be facilitated by multiple methods, including in silico ones. 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, H(3) receptor, 5-HT(1A) receptor, 5-HT(1B) receptor, 5-HT(2C) receptor, melanocortin 4 receptor and neurokinin 1 receptor respectively) from significant compound libraries.

COMBI-SVMs trained with 917-1951 individual goal inhibitors correctly identified 22-83.3% (majority >31.1%) of the 6-216 dual inhibitors collected from literature 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 7-8181 MDDR compounds similar to the dual inhibitors.


Compared with similarity searching, k-NN and PNN methods, COMBI-SVM produced comparable dual kinase 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.