Acta Pharmacologica Sinica 2005 January; 26 (1): 107-112; doi: 10.1111/j.1745-7254.2005.00014.x

Ning DONG, Wen-cong LU², Nian-yi CHEN, You-cheng ZHU³, Kai-xian CHEN³

²Laboratory of Chemical Data Mining, Department of Chemistry, School of Science, Shanghai University, Shanghai 200436, China; ³Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China

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 ΔE [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.