Original Article

Using support vector classification for SAR of fentanyl derivatives1

Ning DONG, Wen-cong LU, Nian-yi CHEN, You-cheng ZHU, Kai-xian CHEN


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 △E [energy difference between highest occupied molecular orbital energy
(HOMO) and lowest empty molecular orbital energy (LUMO)], MR
(molecular refractivity) and Mr (molecular weight).
Results: By using leave-oneout
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.
SVC can be used to investigate SAR of fentanyl derivatives and could be a promising
tool in the field of SAR research.