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 ΔE [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-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.
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Project supported by the National Natural Science Foundation of China (No 20373040).
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Dong, N., Lu, Wc., Chen, Ny. et al. Using support vector classification for SAR of fentanyl derivatives. Acta Pharmacol Sin 26, 107–112 (2005). https://doi.org/10.1111/j.1745-7254.2005.00014.x
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DOI: https://doi.org/10.1111/j.1745-7254.2005.00014.x
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