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Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors

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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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Acknowledgments

This research was supported by the National Natural Science Foundation of China (No 61073135) and the “111” project of “Introducing Talents of Discipline to Universities”.

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Authors and Affiliations

  1. Key Laboratory of Biorheological Science and Technology, Ministry of Education, Chongqing, 400044, China

    Wen Tan, Hu Mei & Li Yang

  2. College of Bioengineering, Chongqing University, Chongqing, 400044, China

    Wen Tan, Hu Mei, Xianchao Pan & Li Yang

  3. School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China

    Li Chao, Tengfei Liu & Mao Shu

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  1. Wen Tan
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  2. Hu Mei
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Correspondence to Hu Mei.

Electronic supplementary material

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10822_2013_9697_MOESM1_ESM.docx

The list of 87, 67, 43, 23, and 3 descriptors as well as the corresponding weights are provided in Table S1–S5. (DOCX 23 kb)

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Tan, W., Mei, H., Chao, L. et al. Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors. J Comput Aided Mol Des 27, 1067–1073 (2013). https://doi.org/10.1007/s10822-013-9697-8

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  • DOI: https://doi.org/10.1007/s10822-013-9697-8

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