Using support vector classification for SAR of fentanyl derivatives¹

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.
Conclusion:
SVC can be used to investigate SAR of fentanyl derivatives and could be a promising
tool in the field of SAR research.