Publication: Classification of p-glycoprotein-interacting compounds using machine learning methods
Issued Date
2015-08-19
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ISSN
16112156
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2-s2.0-84940048822
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Mahidol University
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SCOPUS
Bibliographic Citation
EXCLI Journal. Vol.14, (2015), 958-970
Suggested Citation
Veda Prachayasittikul, Apilak Worachartcheewan, Watshara Shoombuatong, Virapong Prachayasittikul, Chanin Nantasenamat Classification of p-glycoprotein-interacting compounds using machine learning methods. EXCLI Journal. Vol.14, (2015), 958-970. doi:10.17179/excli2015-374 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/35114
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Title
Classification of p-glycoprotein-interacting compounds using machine learning methods
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Abstract
© 2015 Leibniz Research Centre for Working Environment and Human Factors. All rights reserved. P-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.