Quantit四年级数学练习题
ative 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. Jarids陆战棋怎么摆
upa/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 vec写夸张句
tor 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]benzod乏味的英语
iazepinone (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 perfo热感冒有什么症状
rmance and predictive capability of the SVM method were investigated and compared with other techniques such as artif古代爵位
icial neural networks and multip品德的英文
le 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
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