Cellular and Molecular Pharmacology

Using support vector classification for SAR of fentanyl derivatives

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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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Author information

Correspondence to Wen-cong Lu.

Additional information

Project supported by the National Natural Science Foundation of China (No 20373040).

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Keywords

  • structure-activity relationship
  • support vector machine
  • fentanyl derivatives
  • support vector classification

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