SAR and QSAR study on the bioactivities of human epidermal growth factor receptor-2 (HER2) inhibitors
SAR and QSAR study on the bioactivities of human epidermal growth factor receptor-2 (HER2) inhibitors
复制标题
人表皮生长因子受体2（HER2）抑制剂生物活性的SAR和QSAR研究
DOI：
10.1080/1062936x.2017.1284898
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发表时间：
2017
影响因子：
3
通讯作者：
Zhang J. S.
中科院分区：
文献类型：
--
作者：
Qu D.;Yan A.;Zhang J. S.
本文建立了结构-活性关系（SAR, classification）和定量结构-活性关系（QSAR）模型来预测人表皮生长因子受体-2 （HER2）抑制剂的生物活性。在SAR研究中，我们建立了6个SAR（或分类）模型来区分高活性和弱活性HER2抑制剂。该数据集包含868个HER2抑制剂，通过Kohonen自组织映射（SOM）或随机方法将其分成包含580个抑制剂的训练集和包含288个抑制剂的测试集。采用支持向量机（SVM）、随机森林（RF）和多层感知器（MLP）方法建立SAR模型。在6个模型中，SVM模型与其他模型相比获得了更优的结果。最佳模型（1A模型）的预测准确率为90.27%，马修斯相关系数（MCC）为0.80。在QSAR研究中，我们选择286种HER2抑制剂，采用MLR、SVM和MLP方法建立了6种定量预测模型。最佳模型（模型4B）在检验集上的相关系数(r)为0.92。描述子分析表明，HAccN、孤对电负性和π电负性与HER2抑制剂的生物活性密切相关。
In this paper, structure–activity relationship (SAR, classification) and quantitative structure–activity relationship (QSAR) models have been established to predict the bioactivity of human epidermal growth factor receptor-2 (HER2) inhibitors. For the SAR study, we established six SAR (or classification) models to distinguish highly and weakly active HER2 inhibitors. The dataset contained 868 HER2 inhibitors, which was split into a training set including 580 inhibitors and a test set including 288 inhibitors by a Kohonen’s self-organizing map (SOM), or a random method. The SAR models were performed using support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP) methods. Among the six models, SVM models obtained superior results compared with other models. The prediction accuracy of the best model (model 1A) was 90.27% and the Matthews correlation coefficient (MCC) was 0.80 on the test set. For the QSAR study, we chose 286 HER2 inhibitors to establish six quantitative prediction models using MLR, SVM and MLP methods. The correlation coefficient (r) of the best model (model 4B) was 0.92 on the test set. The descriptors analysis showed that HAccN, lone pair electronegativity and π electronegativity were closely related to the bioactivity of HER2 inhibitors.