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

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) m...

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Published in:BioMed Research International Vol. 2017; pp. 1 - 12
Main Authors: Zhao, Manman, Wang, Lin, Zheng, Linfeng, Zhang, Mengying, Qiu, Chun, Zhang, Yuhui, Du, Dongshu, Niu, Bing
Format: equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 5/29/2017
Online Access:View this record in EBSCOhost
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        23146133
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      jtl: BioMed Research International
      issn: 23146133
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      dt: 5/29/2017
      vid: 2017
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        123286096
        123286096
        123286096
        10.1155/2017/4649191
        123286096
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      ppct: 11
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        atl: 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors.
      aug:
        au:
          Zhao, Manman
          Wang, Lin
          Zheng, Linfeng
          Zhang, Mengying
          Qiu, Chun
          Zhang, Yuhui
          Du, Dongshu
          Niu, Bing
        affil: Shanghai Key Laboratory of Bio-Energy Crops, College of Life Science and Shanghai University High Performance Computing Center, Shanghai University, Shanghai 200444, China
      sug:
        subj:
          Epidermal Growth Factors
          Models, Structural
          Human
          Neoplasms Therapy
          Imaging, Three-Dimensional
          Correlation Coefficient
          Sensitivity and Specificity Evaluation
          Funding Source
      ab: 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 q2=0.565 (cross-validated correlation coefficient) and r2=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.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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