Self-Organizing Map (SOM) and Support Vector Machine (SVM) Models for the Prediction of Human Epidermal Growth Factor Receptor (EGFR/ErbB-1) Inhibitors
Self-Organizing Map (SOM) and Support Vector Machine (SVM) Models for the Prediction of Human Epidermal Growth Factor Receptor (EGFR/ErbB-1) Inhibitors
复制标题
用于预测人表皮生长因子受体 (EGFR/ErbB-1) 抑制剂的自组织图 (SOM) 和支持向量机 (SVM) 模型
DOI：
10.2174/1386207319666160414105044
复制
发表时间：
2016-01-01
影响因子：
1.8
通讯作者：
Yan, Aixia
中科院分区：
文献类型：
--
作者：
Kong, Yue;Qu, Dan;Yan, Aixia
EGFR（ErbB-1/HER 1）激酶在肿瘤治疗中起着重要作用。利用Kohonen的自组织映射（SOM）和支持向量机（SVM）建立了两个分类模型，用于预测化合物是人表皮生长因子受体（ErbR-1）的抑制剂还是诱饵剂。收集包含1248个ATP结合位点抑制剂和3090个诱饵的数据集，并随机分为训练集（831个抑制剂和2064个诱饵）和测试集（417个抑制剂和1029个诱饵）。用ADRIANA软件计算表征分子结构的描述符。代码.通过Pearson相关分析和逐步回归分析，筛选出13个具有统计学意义的描述子，其中包括5个全局描述子和8个二维自相关描述子。SOM模型对训练集和测试集的预测准确率分别为98.5%和96.3%，SVM模型的预测准确率分别为99.0%和97.0%。这两种分类模型在区分EGFR抑制剂和诱骗剂方面都具有良好的性能。
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.