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.

Clustered Distribution of Natural Product Leads of Drugs in the Chemical Space as Influenced by the Privileged Target-Sites

“…were removed by using Corina, The number of NPs were reduced to 137,836 after removing the duplicate entries, small NPs with molecular weight <50 Daltons (drug leads are >100 Daltons30) and the NPs whose molecular fingerprints could not be computed by using available software tools such as PaDEL36. Duplicates were identified and removed by structural comparison based on a set of 98 molecular descriptors we have used for classifying bioactive molecules37 and implemented in the online server MODEL38, open-source software PaDEL36, and our own software, which can distinguish different molecules non-distinguishable by the 881-bit Pubchem molecular fingerprints.…”

Getting the most out of PubChem for virtual screening

“…However, such an adverse effect is expected to be relatively small, as demonstrated in the study by Han et al . 98 Many studies 15,98-101 have shown that computational models derived from putative negatives can perform reasonably well in VS.…”

“…101,107-110 Some of these studies 108 employed a combinatorial approach in which predictive models were separately constructed for each target and subsequently used for parallel screening against each target to find compounds that simultaneously bind to multiple targets. This approach may also be used for identifying selective ligands for structurally related protein targets.…”

Structure-based drug design and biological evaluation of 2-acetamidobenzothiazole derivative as EGFR kinase inhibitor

“…Mutations that lead to EGFR overexpression or over activity have been associated with a variety of human cancers, including lung 5 , colon 6 and breast 7 cancers. Therefore, inhibitors of EGFR-inhibiting EGFR kinase activity by competing with its cognate ligands-may potentially constitute a new class of effective drugs in clinical use or cancer therapy 8,9 . Anilinoquinazolines are the most developed class of drugs that inhibit EGFR tyrosine kinase intracellularly 10,11 .…”