Prachayasittikul, VedaWorachartcheewan, ApilakShoombuatong, WatsharaPrachayasittikul, VirapongNantasenamat, Chanin2016-06-062016-06-062015-08-191611-2156http://hdl.handle.net/2003/3504110.17877/DE290R-17089P-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.enEXCLI Journal;Vol. 14, 2015http://creativecommons.org/licenses/by/4.0/P-glycoproteinADMETmultidrug resistanceQSARdata mining61010.17179/excli2015-374article (journal)