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Open AccessFeature PaperArticle

A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking

Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, 55128 Mainz, Germany
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Author to whom correspondence should be addressed.
Cells 2019, 8(10), 1286; https://doi.org/10.3390/cells8101286
Received: 9 September 2019 / Revised: 18 October 2019 / Accepted: 20 October 2019 / Published: 21 October 2019
(This article belongs to the Special Issue ABC Transporters: From Basic Functions to Diseases)
P-glycoprotein (P-gp) is an important determinant of multidrug resistance (MDR) because its overexpression is associated with increased efflux of various established chemotherapy drugs in many clinically resistant and refractory tumors. This leads to insufficient therapeutic targeting of tumor populations, representing a major drawback of cancer chemotherapy. Therefore, P-gp is a target for pharmacological inhibitors to overcome MDR. In the present study, we utilized machine learning strategies to establish a model for P-gp modulators to predict whether a given compound would behave as substrate or inhibitor of P-gp. Random forest feature selection algorithm-based leave-one-out random sampling was used. Testing the model with an external validation set revealed high performance scores. A P-gp modulator list of compounds from the ChEMBL database was used to test the performance, and predictions from both substrate and inhibitor classes were selected for the last step of validation with molecular docking. Predicted substrates revealed similar docking poses than that of doxorubicin, and predicted inhibitors revealed similar docking poses than that of the known P-gp inhibitor elacridar, implying the validity of the predictions. We conclude that the machine-learning approach introduced in this investigation may serve as a tool for the rapid detection of P-gp substrates and inhibitors in large chemical libraries. View Full-Text
Keywords: artificial intelligence; drug discovery; machine learning; molecular docking; multidrug resistance; P-glycoprotein artificial intelligence; drug discovery; machine learning; molecular docking; multidrug resistance; P-glycoprotein
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Kadioglu, O.; Efferth, T. A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking. Cells 2019, 8, 1286.

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