Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10822-010-9321-0
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dc.titleConsensus model for identification of novel PI3K inhibitors in large chemical library
dc.contributor.authorLiew, C.Y.
dc.contributor.authorMa, X.H.
dc.contributor.authorYap, C.W.
dc.date.accessioned2014-10-29T01:50:34Z
dc.date.available2014-10-29T01:50:34Z
dc.date.issued2010-02
dc.identifier.citationLiew, C.Y., Ma, X.H., Yap, C.W. (2010-02). Consensus model for identification of novel PI3K inhibitors in large chemical library. Journal of Computer-Aided Molecular Design 24 (2) : 131-141. ScholarBank@NUS Repository. https://doi.org/10.1007/s10822-010-9321-0
dc.identifier.issn0920654X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105774
dc.description.abstractPhosphoinositide 3-kinases (PI3Ks) inhibitors have treatment potential for cancer, diabetes, cardiovascular disease, chronic inflammation and asthma. A consensus model consisting of three base classifiers (AODE, kNN, and SVM) trained with 1,283 positive compounds (PI3K inhibitors), 16 negative compounds (PI3K non-inhibitors) and 64,078 generated putative negatives was developed for predicting compounds with PI3K inhibitory activity of IC50 10 μM. The consensus model has an estimated false positive rate of 0.75%. Nine novel potential inhibitors were identified using the consensus model and several of these contain structural features that are consistent with those found to be important for PI3K inhibitory activities. An advantage of the current model is that it does not require knowledge of 3D structural information of the various PI3K isoforms, which is not readily available for all isoforms. © 2010 Springer Science+Business Media B.V.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s10822-010-9321-0
dc.sourceScopus
dc.subjectConsensus model
dc.subjectDrug discovery
dc.subjectMachine learning
dc.subjectPhosphoinositide 3-kinases
dc.subjectPI3K inhibitors
dc.subjectVirtual screening
dc.typeArticle
dc.contributor.departmentPHARMACY
dc.description.doi10.1007/s10822-010-9321-0
dc.description.sourcetitleJournal of Computer-Aided Molecular Design
dc.description.volume24
dc.description.issue2
dc.description.page131-141
dc.description.codenJCADE
dc.identifier.isiut000275651700004
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