ArticleJournal of computer-aided molecular design2013
Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors.
Article in Journal of computer-aided molecular design, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 32 citations in OpenAlex.
- Computational Insights into Allosteric Conformational Modulation of P-Glycoprotein by Substrate and Inhibitor Binding.Molecules (Basel, Switzerland) · 2020Article
- Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors.Journal of computer-aided molecular design · 2019Article
- An Energetically Favorable Ligand Entrance Gate of a Multidrug Transporter Revealed by Partial Nudged Elastic Band Simulations.Computational and structural biotechnology journal · 2019Article
- Molecular Modeling of Drug-Transporter Interactions-An International Transporter Consortium Perspective.Clinical pharmacology and therapeutics · 2018Review
- A novel adaptive ensemble classification framework for ADME prediction.RSC advances · 2018Article
- Screening dietary flavonoids for the reversal of P-glycoprotein-mediated multidrug resistance in cancer.Molecular bioSystems · 2016Article
- Computational modeling to predict the functions and impact of drug transporters.In silico pharmacology · 2015Article
- Enzastaurin inhibits ABCB1-mediated drug efflux independently of effects on protein kinase C signalling and the cellular p53 status.Oncotarget · 2015Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter. The over expression of P-gp leads to the development of multidrug resistance (MDR), which is a major obstacle to effective treatment of cancer. Thus, designing effective P-gp inhibitors has an extremely important role in the overcoming MDR. In this paper, both ligand-based quantitative structure-activity relationship (QSAR) and receptor-based molecular docking are used to predict P-gp inhibitors. The results show that each method achieves good prediction performance. According to the results of tenfold cross-validation, an optimal linear SVM model with only three descriptors is established on 857 training samples, of which the overall accuracy (Acc), sensitivity, specificity, and Matthews correlation coefficient are 0.840, 0.873, 0.813, and 0.683, respectively. The SVM model is further validated by 418 test samples with the overall Acc of 0.868. Based on a homology model of human P-gp established, Surflex-dock is also performed to give binding free energy-based evaluations with the overall accuracies of 0.823 for the test set. Furthermore, a consensus evaluation is also performed by using these two methods. Both QSAR and molecular docking studies indicate that molecular volume, hydrophobicity and aromaticity are three dominant factors influencing the inhibitory activities.
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