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Journal of Computer-Aided Molecular Design

, Volume 33, Issue 11, pp 965–971 | Cite as

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

  • Vijaya Kumar Hinge
  • Dipankar Roy
  • Andriy KovalenkoEmail author
Article
  • 86 Downloads

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.

Keywords

P-glycoprotein (PgP) PgP inhibitors Multidrug resistance (MDR) 3D-RISM-KH Solvation free energy Excess chemical potential Partial molar volume (PMV) 

Notes

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.

Supplementary material

10822_2019_253_MOESM1_ESM.xlsx (4.2 mb)
Supplementary file1 (XLSX 4323 kb)
10822_2019_253_MOESM2_ESM.docx (896 kb)
Supplementary file2 (DOCX 896 kb)

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Copyright information

© Crown 2019

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, 10-203 Donadeo Innovation Centre for EngineeringUniversity of AlbertaEdmontonCanada
  2. 2.Nanotechnology Research CentreEdmontonCanada

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