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BioMed Research International
Volume 2017, Article ID 4649191, 11 pages
https://doi.org/10.1155/2017/4649191
Research Article

2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

1Shanghai Key Laboratory of Bio-Energy Crops, College of Life Science and Shanghai University High Performance Computing Center, Shanghai University, Shanghai 200444, China
2Department of Oncology, Hainan General Hospital, Haikou, Hainan 570311, China
3Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
4Changhai Hospital, Second Military Medical University, Shanghai 200433, China
5Department of Life Science, Heze University, Heze, Shandong 274500, China

Correspondence should be addressed to Yuhui Zhang; moc.621@8002gnahc_gnog, Dongshu Du; nc.ude.uhs@udsd, and Bing Niu; moc.361@ycocyhp

Received 6 January 2017; Revised 19 February 2017; Accepted 12 March 2017; Published 29 May 2017

Academic Editor: Vladimir Bajic

Copyright © 2017 Manman Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with (cross-validated correlation coefficient) and (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR.