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Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors

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Abstract

Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from non-inhibitors using molecular solvation theory with excellent accuracy and precision. The excess chemical potential and partial molar volume in various solvents are calculated for PgP± (PgP inhibitors and non-inhibitors) compounds with the statistical–mechanical based three-dimensional reference interaction site model with the Kovalenko–Hirata closure approximation (3D-RISM-KH molecular theory of solvation). The statistical importance analysis of descriptors identified the 3D-RISM-KH based descriptors as top molecular descriptors for classification. Among the constructed classification models, the support vector machine predicted the test set of Pgp± compounds with highest accuracy and precision of ~ 97% for test set. The validation of models confirms the robustness of state-of-the-art molecular solvation theory based descriptors in identification of the Pgp± compounds.

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Notes

  1. The performance range is based on five different runs with a different number of test data points, as some of the machine learning methods are known to be size-dependent.

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Acknowledgements

This work was financially supported by the NSERC Discovery Grant (RES0029477), and Alberta Prion Research Institute Explorations VII Research Grant (RES0039402). Generous computing time provided by WestGrid (www.westgrid.ca) and Compute Canada/Calcul Canada (www.computecanada.ca) is acknowledged.

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Author notes

  1. Vijaya Kumar Hinge and Dipankar Roy have equally contributed to this work.

Authors and Affiliations

  1. Department of Mechanical Engineering, 10-203 Donadeo Innovation Centre for Engineering, University of Alberta, 9211-116 Street NW, Edmonton, AB, T6G 1H9, Canada

    Vijaya Kumar Hinge, Dipankar Roy & Andriy Kovalenko

  2. Nanotechnology Research Centre, 11421 Saskatchewan Drive, Edmonton, AB, T6G 2M9, Canada

    Andriy Kovalenko

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  1. Vijaya Kumar Hinge
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  2. Dipankar Roy
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  3. Andriy Kovalenko
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Correspondence to Andriy Kovalenko.

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Hinge, V.K., Roy, D. & Kovalenko, A. Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors. J Comput Aided Mol Des 33, 965–971 (2019). https://doi.org/10.1007/s10822-019-00253-5

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  • DOI: https://doi.org/10.1007/s10822-019-00253-5

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