Open AccessUsing support vector classification for SAR of fentanyl derivatives 1
Author(s) -
DONG Ning,
LU Wencong,
CHEN Nianyi,
ZHU Youcheng,
CHEN Kaixian
Publication year - 2005
Publication title -
acta pharmacologica sinica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.514
H-Index - 90
eISSN - 1745-7254
pISSN - 1671-4083
DOI - 10.1111/j.1745-7254.2005.00014.x
Subject(s) - principal component analysis , homo/lumo , molecular descriptor , computer science , artificial intelligence , fentanyl , artificial neural network , biological system , pattern recognition (psychology) , quantitative structure–activity relationship , chemistry , molecule , machine learning , pharmacology , biology , organic chemistry
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 Δ E [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.