title: Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines.
abstract: Multitarget agents have been increasingly explored for enhancing efficacy and reducing countertarget activities and toxicities. Efficient virtual screening (VS) tools for searching selective multitarget agents are desired. Combinatorial support vector machines (C-SVM) were tested as VS tools for searching dual-inhibitors of 11 combinations of 9 anticancer kinase targets (EGFR, VEGFR, PDGFR, Src, FGFR, Lck, CDK1, CDK2, GSK3). C-SVM trained on 233-1,316 non-dual-inhibitors correctly identified 26.8%-57.3% (majority >36%) of the 56-230 intra-kinase-group dual-inhibitors (equivalent to the 50-70% yields of two independent individual target VS tools), and 12.2% of the 41 inter-kinase-group dual-inhibitors. C-SVM were fairly selective in misidentifying as dual-inhibitors 3.7%-48.1% (majority <20%) of the 233-1,316 non-dual-inhibitors of the same kinase pairs and 0.98%-4.77% of the 3,971-5,180 inhibitors of other kinases. C-SVM produced low false-hit rates in misidentifying as dual-inhibitors 1,746-4,817 (0.013%-0.036%) of the 13.56 M PubChem compounds, 12-175 (0.007%-0.104%) of the 168 K MDDR compounds, and 0-84 (0.0%-2.9%) of the 19,495-38,483 MDDR compounds similar to the known dual-inhibitors. C-SVM was compared to other VS methods Surflex-Dock, DOCK Blaster, kNN and PNN against the same sets of kinase inhibitors and the full set or subset of the 1.02 M Zinc clean-leads data set. C-SVM produced comparable dual-inhibitor yields, slightly better false-hit rates for kinase inhibitors, and significantly lower false-hit rates for the Zinc clean-leads data set. Combinatorial SVM showed promising potential for searching selective multitarget agents against intra-kinase-group kinases without explicit knowledge of multitarget agents.
authors: Bioinformatics and Drug Design Group, Department of Pharmacy, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543.
authors: X H
authors: XH
authors: Ma
authors: R
authors: R
authors: Wang
authors: C Y
authors: CY
authors: Tan
authors: Y Y
authors: YY
authors: Jiang
authors: T
authors: T
authors: Lu
authors: H B
authors: HB
authors: Rao
authors: X Y
authors: XY
authors: Li
authors: M L
authors: ML
authors: Go
authors: B C
authors: BC
authors: Low
authors: Y Z
authors: YZ
authors: Chen
mesh_terms: D000970:Antineoplastic Agents / Q000737:chemistry / Q000494:pharmacology; D016203:CDC2 Protein Kinase / Q000037:antagonists & inhibitors; D051357:Cyclin-Dependent Kinase 2 / Q000037:antagonists & inhibitors; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D066246:ErbB Receptors / Q000037:antagonists & inhibitors; D038362:Glycogen Synthase Kinase 3 / Q000037:antagonists & inhibitors; D006801:Humans; D019860:Lymphocyte Specific Protein Tyrosine Kinase p56(lck) / Q000037:antagonists & inhibitors; D047428:Protein Kinase Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D017468:Receptors, Fibroblast Growth Factor / Q000037:antagonists & inhibitors; D017479:Receptors, Platelet-Derived Growth Factor / Q000037:antagonists & inhibitors; D040262:Receptors, Vascular Endothelial Growth Factor / Q000037:antagonists & inhibitors; D060388:Support Vector Machine; D014584:User-Computer Interface; D019061:src-Family Kinases / Q000037:antagonists & inhibitors
chemical_list: D000970:Antineoplastic Agents; D047428:Protein Kinase Inhibitors; D017468:Receptors, Fibroblast Growth Factor; D066246:ErbB Receptors; D017479:Receptors, Platelet-Derived Growth Factor; D040262:Receptors, Vascular Endothelial Growth Factor; D019860:Lymphocyte Specific Protein Tyrosine Kinase p56(lck); D019061:src-Family Kinases; D016203:CDC2 Protein Kinase; D051357:Cyclin-Dependent Kinase 2; D038362:Glycogen Synthase Kinase 3