Publication: Classification of P-glycoprotein-interacting compounds using machine-learning methods
1
2
Issued Date
2015-07
Resource Type
Language
eng
Bibliographic Citation
EXCLI Journal. Vol.14, 2015, 958-970
Suggested Citation
Watshara Shoombuatong, Apilak Worachartcheewan, Veda Prachayasittikul, Chanin Nantasenamat, Virapong Prachayasittikul Classification of P-glycoprotein-interacting compounds using machine-learning methods. EXCLI Journal. Vol.14, 2015, 958-970. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/2120
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Classification of P-glycoprotein-interacting compounds using machine-learning methods
Abstract
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 non-inhibitors, 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.