Please use this identifier to cite or link to this item: https://doi.org/10.1021/ci050135u
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dc.titleEffect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods
dc.contributor.authorLi, H.
dc.contributor.authorYap, C.W.
dc.contributor.authorUng, C.Y.
dc.contributor.authorXue, Y.
dc.contributor.authorCao, Z.W.
dc.contributor.authorChen, Y.Z.
dc.date.accessioned2014-12-02T06:52:41Z
dc.date.available2014-12-02T06:52:41Z
dc.date.issued2005-09
dc.identifier.citationLi, H., Yap, C.W., Ung, C.Y., Xue, Y., Cao, Z.W., Chen, Y.Z. (2005-09). Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods. Journal of Chemical Information and Modeling 45 (5) : 1376-1384. ScholarBank@NUS Repository. https://doi.org/10.1021/ci050135u
dc.identifier.issn15499596
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/114321
dc.description.abstractThe ability or inability of a drug to penetrate into the brain is a key consideration in drug design. Drugs for treating central nervous system (CNS) disorders need to be able to penetrate the blood-brain barrier (BBB). BBB nonpenetration is desirable for non-CNS-targeting drugs to minimize potential CNS-related side effects. Computational methods have been employed for the prediction of BBB-penetrating (BBB+) and -nonpenetrating (BBB-) agents at impressive accuracies of 75-92% and 60-80%, respectively. However, the majority of these studies give a substantially lower BBB- accuracy, and thus overall accuracy, than the BBB+ accuracy. This work examined whether proper selection of molecular descriptors can improve both the BBB- and the overall accuracies of statistical learning methods. The methods tested include logistic regression, linear discriminate analysis, k nearest neighbor, C4.5 decision tree, probabilistic neural network, and support vector machine. Molecular descriptors were selected by using a feature selection method, recursive feature elimination (RFE). Results by using 415 BBB+ and BBB- agents show that RFE substantially improves both the BBB- and the overall accuracy for all of the methods studied. This suggests that statistical learning methods combined with proper feature selection is potentially useful for facilitating a more balanced and improved prediction of BBB+ and BBB- agents. © 2005 American Chemical Society.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1021/ci050135u
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCOMPUTATIONAL SCIENCE
dc.contributor.departmentBIOLOGICAL SCIENCES
dc.contributor.departmentSINGAPORE-MIT ALLIANCE
dc.description.doi10.1021/ci050135u
dc.description.sourcetitleJournal of Chemical Information and Modeling
dc.description.volume45
dc.description.issue5
dc.description.page1376-1384
dc.identifier.isiut000232208200023
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