计算机工程与应用2017，Vol.53Issue(9)：11-16,6.DOI:10.3778/j.issn.1002-8331.1612-0111
机器学习预测ACEI的比较与分析
Comparison and analysis of machine learning prediction of ACEI
摘要
Abstract
Angiotensin Converting Enzyme Inhibitor(ACEI) plays an important role in the treatment of hypertension. Candidate small molecular data sets are constructed from the database of complex compounds and the sample sets obtained from the data set using molecular docking techniques are used to construct the classification model. The classifi-cation model of angiotensin converting enzyme inhibitors and non inhibitors is established by using support vector machine, K nearest neighbor, decision tree, random forest and Naive Bayes method, respectively. The support vector machine has higher prediction rate compared with other methods and the overall prediction and correlation coefficients of the model are 82. 4%and 0. 653, respectively. The results show that the support vector machine method has a good effect on the virtual screening of angiotensin converting enzyme inhibitors.关键词
血管紧张素转换酶抑制剂(ACEI)/分子对接/机器学习/支持向量机Key words
Angiotensin Converting Enzyme Inhibitor(ACEI)/molecular docking/machine learning/support vector machine分类
信息技术与安全科学引用本文复制引用
胡明伟,丁彦蕊..机器学习预测ACEI的比较与分析[J].计算机工程与应用,2017,53(9):11-16,6.基金项目
国家自然科学基金(No.21541006). （No.21541006）
