Using Support Vector Machine (SVM) for Classification of Selectivity of H1N1 Neuraminidase Inhibitors
Using Support Vector Machine (SVM) for Classification of Selectivity of H1N1 Neuraminidase Inhibitors
复制标题
使用支持向量机 (SVM) 对 H1N1 神经氨酸酶抑制剂的选择性进行分类
DOI：
10.1002/minf.201500107
复制
发表时间：
2016
影响因子：
3.6
通讯作者：
Liu Zhenming
中科院分区：
文献类型：
--
作者：
Li Yang;Kong Yue;Zhang Mengdi;Yan Aixia;Liu Zhenming
抑制神经氨酸酶是预防流感病毒传播的最有前景的策略之一。数据集1收集神经氨酸酶抑制剂479个，数据集2收集A/P/8/34的神经氨酸酶抑制剂208个。利用支持向量机建立了4个预测化合物是神经氨酸酶活性抑制剂还是弱活性神经氨酸酶抑制剂的计算模型。每个化合物由MASSC指纹和ADRIANA代码描述符表示。对所有模型的测试集的预测精度都在78 %以上。模型2B在测试集上的预测精度和马修斯相关系数分别为89.71 %和0.81。分子的极化率、分子形状、分子大小和氢键与神经氨酸酶抑制剂的活性有关。这些模型可以从作者处获得。
Inhibition of the neuraminidase is one of the most promising strategies for preventing influenza virus spreading. 479 neuraminidase inhibitors are collected for dataset 1 and 208 neuraminidase inhibitors for A/P/8/34 are collected for dataset 2. Using support vector machine (SVM), four computational models were built to predict whether a compound is an active or weakly active inhibitor of neuraminidase. Each compound is represented by MASSC fingerprints and ADRIANA.Code descriptors. The predication accuracies for the test sets of all the models are over 78 %. Model 2B, which is the best model, obtains a prediction accuracy and a Matthews Correlation Coefficient (MCC) of 89.71 % and 0.81 on test set, respectively. The molecular polarizability, molecular shape, molecular size and hydrogen bonding are related to the activities of neuraminidase inhibitors. The models can be obtained from the authors.