Please use this identifier to cite or link to this item: https://doi.org/10.1021/ci900135u
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dc.titleVirtual screening of bl inhibitors from large compound libraries by support vector machines
dc.contributor.authorLiu, X.H.
dc.contributor.authorMa, X.H.
dc.contributor.authorTan, C.Y.
dc.contributor.authorJiang, Y.Y.
dc.contributor.authorGo, M.L.
dc.contributor.authorLow, B.C.
dc.contributor.authorChen, Y.Z.
dc.date.accessioned2014-05-19T02:56:15Z
dc.date.available2014-05-19T02:56:15Z
dc.date.issued2009-09-28
dc.identifier.citationLiu, X.H., Ma, X.H., Tan, C.Y., Jiang, Y.Y., Go, M.L., Low, B.C., Chen, Y.Z. (2009-09-28). Virtual screening of bl inhibitors from large compound libraries by support vector machines. Journal of Chemical Information and Modeling 49 (9) : 2101-2110. ScholarBank@NUS Repository. https://doi.org/10.1021/ci900135u
dc.identifier.issn15499596
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/53255
dc.description.abstractAbl promotes cancers by regulating cell morphogenesis, motility, growth, and survival. Successes of several marketed and clinical trial Abl inhibitors against leukemia and other cancers and appearances of reduced efficacies and drug resistances have led to significant interest in and efforts for developing new Abl inhibitors. In silico methods of pharmacophore, fragment, and molecular docking have been used in some of these efforts. It is desirable to explore other in silico methods capable of searching large compound libraries at high yields and reduced false-hit rates. We evaluated support vector machines (SVM) as a virtual screening tool for searching Abl inhibitors from large compound libraries. SVM trained and tested by 708 inhibitors and 65 494 putative noninhibitors correctly identified 84. 4 to 92. 3% inhibitors and 99. 96 to 99. 99% noninhibitors in 5-fold cross validation studies. SVM trained by 708 pre-2008 inhibitors and 65 494 putative noninhibitors correctly identified 50. 5% of the 91 inhibitors reported since 2008 and predicted as inhibitors 29 072 (0. 21%) of 13. 56M PubChem, 659 (0. 39%) of 168K MDDR, and 330 (5. 0%) of 6 638 MDDR compounds similar to the known inhibitors. SVM showed comparable yields and substantially reduced false- hit rates against two similarity based and another machine learning VS methods based on the same training and testing data sets and molecular descriptors. These suggest that SVM is capable of searching Abl inhibitors from large compound libraries at low false-hit rates. © 2009 American Chemical Society.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1021/ci900135u
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentPHARMACY
dc.contributor.departmentBIOLOGICAL SCIENCES
dc.description.doi10.1021/ci900135u
dc.description.sourcetitleJournal of Chemical Information and Modeling
dc.description.volume49
dc.description.issue9
dc.description.page2101-2110
dc.identifier.isiut000270093800009
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