usingsupportvectorclassificationforsaroffentanylderivatives
文献类型：期刊论文
| 作者 | Nianyi CHEN1; Youcheng ZHU2; Kaixian CHEN2 ; Ning DONG1; Wencong Lu1
|
| 刊名 | actapharmacologicasinica
 |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：上海药物研究所
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