Molecular Diversity

, Volume 20, Issue 4, pp 945–961 | Cite as

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds

  • Trieu-Du Ngo
  • Thanh-Dao Tran
  • Minh-Tri Le
  • Khac-Minh ThaiEmail author
Original Article


The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure–activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.


Machine learning Docking P-glycoprotein Chalcone Drug bank 



This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant number 106-YS.05-2015.31 to Khac-Minh Thai.

Compliance with ethical standards

Conflict of interest

The authors confirm that this article content has no conflict of interest.

Supplementary material

11030_2016_9688_MOESM1_ESM.pdf (988 kb)
Supplementary material 1 (pdf 987 KB)
11030_2016_9688_MOESM2_ESM.xlsx (2.4 mb)
Supplementary material 2 (xlsx 2409 KB)


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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Medicinal Chemistry, Faculty of PharmacyUniversity of Medicine and Pharmacy at Ho Chi Minh CityHo Chi Minh CityViet Nam

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