Quantitative structure-activity relationship studies of TIBO derivatives using support vector machines 期刊名称： Sar & Qsar in Environmental Rearch
作者： R. Darnagsupa/sup, A. Schmitzersupb/sup, Y. Belmiloudsupc/sup, D.
Villeminsupd/sup, A. Jaridsupa/sup, A. Chaitsupa/sup, E. Mazouzsupa/sup &,D.
Cherqaouisupa/supsup*/sup
年份： 2010年
期号： 第3-4期
关键词： QSAR; anti-HIV; TIBO; support vector machine; neural network; MLR
摘要：A quantitative structure-activity relationship (QSAR) study is suggested for the prediction of anti-HIV activity of tetrahydroimidazo[4,5,1-jk]
[1,4]benzodiazepinone (TIBO) derivatives. The model was produced by using the support vector machi
ne (SVM) technique to develop quantitative relationships between the anti-HIV activity and ten molecular descriptors of 89 TIBO derivatives. The performance and predictive capability of the SVM method were investigated and compared with other techniques such as artificial neural networks and multiple linear regression. The results obtained indicate that the SVM model with the kernel radial basis function can be successfully ud to predict the anti-HIV activity of TIBO derivatives with only ten molecular descriptors that can be calculated directly from only molecular structure. The
内容由中国教育图书进出口有限公司引进
