PMID- 15659122
OWN - NLM
STAT- MEDLINE
DCOM- 20050616
LR  - 20191210
IS  - 1671-4083 (Print)
IS  - 1671-4083 (Linking)
VI  - 26
IP  - 1
DP  - 2005 Jan
TI  - Using support vector classification for SAR of fentanyl derivatives.
PG  - 107-12
AB  - 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)], MR (molecular 
      refractivity) and M(r) (molecular weight). RESULTS: By using leave-one-out 
      cross-validation test, the accuracies of prediction for activities of fentanyl 
      derivatives in SVC, principal component analysis (PCA), artificial neural network 
      (ANN) and K-nearest neighbor (KNN) models were 93%, 86%, 57%, and 71%, 
      respectively. The results indicated that the performance of the SVC model was 
      better than those of PCA, ANN, and KNN models for this data. CONCLUSION: SVC can 
      be used to investigate SAR of fentanyl derivatives and could be a promising tool 
      in the field of SAR research.
FAU - Dong, Ning
AU  - Dong N
AD  - Laboratory of Chemical Data Mining, Department of Chemistry, School of Science, 
      Shanghai University, Shanghai 200436, China.
FAU - Lu, Wen-cong
AU  - Lu WC
FAU - Chen, Nian-yi
AU  - Chen NY
FAU - Zhu, You-cheng
AU  - Zhu YC
FAU - Chen, Kai-xian
AU  - Chen KX
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Acta Pharmacol Sin
JT  - Acta pharmacologica Sinica
JID - 100956087
RN  - UF599785JZ (Fentanyl)
SB  - IM
MH  - *Algorithms
MH  - Fentanyl/analogs & derivatives/*chemistry
MH  - Models, Molecular
MH  - Molecular Structure
MH  - Neural Networks, Computer
MH  - *Numerical Analysis, Computer-Assisted
MH  - Principal Component Analysis
MH  - Quantitative Structure-Activity Relationship
EDAT- 2005/01/22 09:00
MHDA- 2005/06/17 09:00
CRDT- 2005/01/22 09:00
PHST- 2005/01/22 09:00 [pubmed]
PHST- 2005/06/17 09:00 [medline]
PHST- 2005/01/22 09:00 [entrez]
AID - APHS014 [pii]
AID - 10.1111/j.1745-7254.2005.00014.x [doi]
PST - ppublish
SO  - Acta Pharmacol Sin. 2005 Jan;26(1):107-12. doi: 10.1111/j.1745-7254.2005.00014.x.