Using support vector classification for SAR of fentanyl derivatives.

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.

3-Pyrroline containing arylacetamides: a novel series of remarkably selective kappa-agonists.

A series of 2-(substituted phenyl)-N-methyl-N-[(1S)-1-(substituted alkyl)-2-(1-(3-pyrrolinyl))ethyl]acetamides were synthesized and evaluated as highly selective kappa-agonists with K(i) values in low nanomolar range. 3-Pyrroline incorporated into the basic amino functionality in combination with 2-(methylthio)ethyl substituent on the carbon adjacent to the amide nitrogen remarkably enhanced the kappa-selectivity. 3,4-Dichlorophenyl derivative 1e was found the most potent and selective analgesic in this series with ED(50) value of 0.023 mg/kg.