2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors. (29th May 2017)

• Record Type: Journal Article
• Authors: Zhao, Manman; Wang, Lin; Zheng, Linfeng; Zhang, Mengying; Qiu, Chun; Zhang, Yuhui; Du, Dongshu; Niu, Bing
• Other Names: Bajic Vladimir Academic Editor.
• Is Part Of: BioMed research international. Volume 2017(2017)
• Publication Date: 2017-05-29
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2017/4649191
• Languages: English
• ISSNs: 2314-6133

Abstract

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 q 2 = 0.565 (cross-validated correlation coefficient) and r 2 = 0.888 (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.