title: Using support vector classification for SAR of fentanyl derivatives.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  SVC can be used to investigate SAR of fentanyl derivatives and could be a promising tool in the field of SAR research.
authors.affiliation: Laboratory of Chemical Data Mining, Department of Chemistry, School of Science, Shanghai University, Shanghai 200436, China.
authors.forename: Ning
authors.initials: N
authors.lastname: Dong
authors.forename: Wen-cong
authors.initials: WC
authors.lastname: Lu
authors.forename: Nian-yi
authors.initials: NY
authors.lastname: Chen
authors.forename: You-cheng
authors.initials: YC
authors.lastname: Zhu
authors.forename: Kai-xian
authors.initials: KX
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D005283:Fentanyl / Q000031:analogs & derivatives / Q000737:chemistry*; D008958:Models, Molecular; D015394:Molecular Structure; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship
chemical_list: D005283:Fentanyl