Using support vector classification for SAR of fentanyl derivatives.

ABSTRACT

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.