Name
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. © 2013 Springer Science+Business Media Dordrecht. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10822-013-9697-8 | Journal of computer-aided molecular design |
Keywords | Field | DocType |
P-glycoprotein,Inhibitor,Support vector machine,Surflex-dock,Prediction | Docking (molecular),Quantitative structure–activity relationship,Matthews correlation coefficient,Molecule,Docking (dog),Chemistry,Multiple drug resistance,Bioinformatics,Homology modeling,P-glycoprotein | Journal |
Volume | Issue | ISSN |
27 | 12 | 15734951 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Tan | 1 | 1 | 0.69 |
| Hu Mei | 2 | 20 | 2.91 |
| Li Chao | 3 | 0 | 0.34 |
| Tengfei Liu | 4 | 92 | 7.09 |
| Xianchao Pan | 5 | 1 | 1.37 |
| Mao Shu | 6 | 0 | 0.34 |
| Li Yang | 7 | 0 | 0.34 |