usingsupportvectorclassificationforsaroffentanylderivatives

	usingsupportvectorclassificationforsaroffentanylderivatives
	Nianyi CHEN 1; Youcheng ZHU 2; Kaixian CHEN 2; Ning DONG 1; Wencong Lu 1
刊名	actapharmacologicasinica
	2005
卷号	26 期号:1 页码:107
关键词	MACHINES structure-activity relationship support vector machine fentanyl derivatives support vector classification
ISSN号	1671-4083
英文摘要	Aim: To discriminate between fentanyl derivatives with high and low activities. Methods: The support vector classification (SVC) method, a novel approach, was employed to investigate structure-activity relationship (SAR) of fentanyl derivatives based on the molecular descriptors, which were quantum parameters including DeltaE energy difference between highest occupied molecular orbital energy (HOMO) and lowest empty molecular orbital energy (LUMO)
语种	英语
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/288121]
专题	中国科学院上海药物研究所
作者单位	1.上海大学 2.中国科学院上海药物研究所
推荐引用方式 GB/T 7714	Nianyi CHEN,Youcheng ZHU,Kaixian CHEN,et al. usingsupportvectorclassificationforsaroffentanylderivatives[J]. actapharmacologicasinica,2005,26(1):107.
APA	Nianyi CHEN,Youcheng ZHU,Kaixian CHEN,Ning DONG,&Wencong Lu.(2005).usingsupportvectorclassificationforsaroffentanylderivatives.actapharmacologicasinica,26(1),107.
MLA	Nianyi CHEN,et al."usingsupportvectorclassificationforsaroffentanylderivatives".actapharmacologicasinica 26.1(2005):107.