Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jmgm.2011.09.002
Title: Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries
Authors: Shi, Z.
Ma, X.H. 
Qin, C.
Jia, J.
Jiang, Y.Y.
Tan, C.Y.
Chen, Y.Z. 
Keywords: Antidepressants
Computer aided drug design
High-throughput screening
Multi-target
Support vector machines
Virtual screening
Issue Date: Feb-2012
Citation: Shi, Z., Ma, X.H., Qin, C., Jia, J., Jiang, Y.Y., Tan, C.Y., Chen, Y.Z. (2012-02). Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries. Journal of Molecular Graphics and Modelling 32 : 49-66. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jmgm.2011.09.002
Abstract: 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, H 3 receptor, 5-HT 1A receptor, 5-HT 1B receptor, 5-HT 2C receptor, melanocortin 4 receptor and neurokinin 1 receptor respectively) from large compound libraries. COMBI-SVMs trained with 917-1951 individual target 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
Source Title: Journal of Molecular Graphics and Modelling
URI: http://scholarbank.nus.edu.sg/handle/10635/105746
ISSN: 10933263
DOI: 10.1016/j.jmgm.2011.09.002
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