Please use this identifier to cite or link to this item:
|Title:||Prediction of P-glycoprotein substrates by a support vector machine approach||Authors:||Xue, Y.
|Issue Date:||Jul-2004||Citation:||Xue, Y., Yap, C.W., Sun, L.Z., Cao, Z.W., Wang, J.F., Chen, Y.Z. (2004-07). Prediction of P-glycoprotein substrates by a support vector machine approach. Journal of Chemical Information and Computer Sciences 44 (4) : 1497-1505. ScholarBank@NUS Repository. https://doi.org/10.1021/ci049971e||Abstract:||P-glycoproteins (P-gp) actively transport a wide variety of chemicals out of cells and function as drug efflux pumps that mediate multidrug resistance and limit the efficacy of many drugs. Methods for facilitating early elimination of potential P-gp substrates are useful for facilitating new drug discovery. A computational ensemble pharmacophore model has recently been used for the prediction of P-gp substrates with a promising accuracy of 63%. It is desirable to extend the prediction range beyond compounds covered by the known pharmacophore models. For such a purpose, a machine learning method, support vector machine (SVM), was explored for the prediction of P-gp substrates. A set of 201 chemical compounds, including 116 substrates and 85 nonsubstrates of P-gp, was used to train and test a SVM classification system. This SVM system gave a prediction accuracy of at least 81.2% for P-gp substrates based on two different evaluation methods, which is substantially improved against that obtained from the multiple-pharmacophore model. The prediction accuracy for nonsubstrates of P-gp is 79.2% using 5-fold cross-validation. These accuracies are slightly better than those obtained from other statistical classification methods, including k-nearest neighbor (k-NN), probabilistic neural networks (PNN), and C4.5 decision tree, that use the same sets of data and molecular descriptors. Our study indicates the potential of SVM in facilitating the prediction of P-gp substrates.||Source Title:||Journal of Chemical Information and Computer Sciences||URI:||http://scholarbank.nus.edu.sg/handle/10635/114376||ISSN:||00952338||DOI:||10.1021/ci049971e|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 1, 2022
WEB OF SCIENCETM
checked on Jul 1, 2022
checked on Jun 23, 2022
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.