Classification of P-glycoprotein-interacting compounds using machine learning methods

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dc.contributor.authorPrachayasittikul, Veda
dc.contributor.authorWorachartcheewan, Apilak
dc.contributor.authorShoombuatong, Watshara
dc.contributor.authorPrachayasittikul, Virapong
dc.contributor.authorNantasenamat, Chanin
dc.date.accessioned2016-06-06T10:50:32Z
dc.date.available2016-06-06T10:50:32Z
dc.date.issued2015-08-19
dc.description.abstractP-glycoprotein (Pgp) is a drug transporter that plays important roles in multidrug resistance and drug pharmacokinetics. The inhibition of Pgp has become a notable strategy for combating multidrug-resistant cancers and improving therapeutic outcomes. However, the polyspecific nature of Pgp, together with inconsistent results in experimental assays, renders the determination of endpoints for Pgp-interacting compounds a great challenge. In this study, the classification of a large set of 2,477 Pgp-interacting compounds (i.e., 1341 inhibitors, 913 noninhibitors, 197 substrates and 26 non-substrates) was performed using several machine learning methods (i.e., decision tree induction, artificial neural network modelling and support vector machine) as a function of their physicochemical properties. The models provided good predictive performance, producing MCC values in the range of 0.739-1 for internal cross-validation and 0.665-1 for external validation. The study provided simple and interpretable models for important properties that influence the activity of Pgp-interacting compounds, which are potentially beneficial for screening and rational design of Pgp inhibitors that are of clinical importance.en
dc.identifier.doi10.17179/excli2015-374
dc.identifier.issn1611-2156
dc.identifier.urihttp://hdl.handle.net/2003/35041
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17089
dc.language.isoen
dc.relation.ispartofseriesEXCLI Journal;Vol. 14, 2015en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectP-glycoproteinen
dc.subjectADMETen
dc.subjectmultidrug resistanceen
dc.subjectQSARen
dc.subjectdata miningen
dc.subject.ddc610
dc.titleClassification of P-glycoprotein-interacting compounds using machine learning methodsen
dc.typeText
dc.type.publicationtypearticle
dcterms.accessRightsopen access
eldorado.dnb.zdberstkatid2132560-1

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