Self-Organizing Map (SOM) and Support Vector Machine (SVM) Models for the Prediction of Human Epidermal Growth Factor Receptor (EGFR/ ErbB-1) Inhibitors

Title:Self-Organizing Map (SOM) and Support Vector Machine (SVM) Models for the Prediction of Human Epidermal Growth Factor Receptor (EGFR/ ErbB-1) Inhibitors

Volume: 19 Issue: 5

Author(s): Yue Kong, Dan Qu, Xiaoyan Chen, Ya-Nan Gong and Aixia Yan

Affiliation:

Keywords: Human Epidermal Growth Factor Receptor (EGFR), EGFR inhibitors, classification models, Self-organizing Map (SOM), Support Vector Machine (SVM).

Abstract: EGFR (ErbB-1/HER1) kinase plays an important role in cancer therapy. Two classification models were established to predict whether a compound is an inhibitor or a decoy of human EGFR (ErbR-1) by using Kohonen’s self-organizing map (SOM) and support vector machine (SVM). A dataset containing 1248 ATP binding site inhibitors and 3090 decoys was collected and randomly divided into a training set (831 inhibitors and 2064 decoys) and a test set (417 inhibitors and 1029 decoys). The descriptors that represent molecular structures were calculated by software ADRIANA.Code. Thirteen significant descriptors including five global descriptors and eight 2D property autocorrelation descriptors were selected by Pearson correlation analysis and stepwise analysis. The prediction accuracies on training set and test set are 98.5% and 96.3% for SOM model, 99.0% and 97.0% for SVM model, respectively. Both of these two classification models have good performance on distinguishing EGFR inhibitors from decoys.

Cite this article as:

Kong Yue, Qu Dan, Chen Xiaoyan, Gong Ya-Nan and Yan Aixia, Self-Organizing Map (SOM) and Support Vector Machine (SVM) Models for the Prediction of Human Epidermal Growth Factor Receptor (EGFR/ ErbB-1) Inhibitors, Combinatorial Chemistry & High Throughput Screening 2016; 19 (5) . https://dx.doi.org/10.2174/1386207319666160414105044