Please use this identifier to cite or link to this item: https://doi.org/10.1021/ci800387z
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dc.titleSVM model for virtual screening of Lck inhibitors
dc.contributor.authorLiew, C.Y.
dc.contributor.authorMa, X.H.
dc.contributor.authorLiu, X.
dc.contributor.authorYap, C.W.
dc.date.accessioned2014-10-29T01:59:09Z
dc.date.available2014-10-29T01:59:09Z
dc.date.issued2009-04-27
dc.identifier.citationLiew, C.Y., Ma, X.H., Liu, X., Yap, C.W. (2009-04-27). SVM model for virtual screening of Lck inhibitors. Journal of Chemical Information and Modeling 49 (4) : 877-885. ScholarBank@NUS Repository. https://doi.org/10.1021/ci800387z
dc.identifier.issn15499596
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/106384
dc.description.abstractLymphocyte-specific protein tyrosine kinase (Lck) inhibitors have treatment potential for autoimmune diseases and transplant rejection. A support vector machine (SVM) model trained with 820 positive compounds (Lck inhibitors) and 70 negative compounds (Lck noninhibitors) combined with 65 142 generated putative negatives was developed for predicting compounds with a Lck inhibitory activity of IC 50 ≤ 10 μM. The SVM model, with an estimated sensitivity of greater than 83% and specificity of greater than 99%, was used to screen 168 014 compounds in the MDDR and was found to have a yield of 45.8% and a false positive rate of 0.52%. The model was also able to identify novel Lck inhibitors and distinguish inhibitors from structurally similar noninhibitors at a false positive rate of 0.27%. To the best of our knowledge, the SVM model developed in this work is the first model with a broad applicability domain and low false positive rate, which makes it very suitable for the virtual screening of chemical libraries for Lck inhibitors. © 2009 American Chemical Society.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1021/ci800387z
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentPHARMACY
dc.description.doi10.1021/ci800387z
dc.description.sourcetitleJournal of Chemical Information and Modeling
dc.description.volume49
dc.description.issue4
dc.description.page877-885
dc.identifier.isiut000265528400013
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