QSAR study on the Ah receptor-binding affinities of polyhalogenated dibenzo-p-dioxins using net atomic-charge descriptors and a radial basis neural network.
A radial basis function neural network (RBFN) has been used to correlate Ah receptor-binding affinities of polychlorinated, polybrominated, and polychlorinated-brominated dibenzo-p-dioxins with molecular weight and eight net atomic charge descriptors. Support vector machine (SVM) and partial least square (PLS) regression models based on the same data set have also been built. Leave-one-out cross-validation was used to train the RBFN, SVM, and PLS models. For predicting Ah receptor-binding affinities, the RBFN model with a squared cross-validation correlation coefficient (q2) of 0.8818 outperforms the SVM and PLS models and also compares favorably with any other reported quantitative structure-activity relationship model based on the same activity data set. The significance of the RBFN model with net atomic charges as descriptors suggests that electrostatic and dispersion-type interactions play important roles in governing the Ah receptor binding of polychlorinated, polybrominated, and polychlorinated-brominated dibenzo-p-dioxins.
Zheng
G
G
School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, PR China.
Xiao
M
M
Lu
X H
XH
D000465:Algorithms; D001665:Binding Sites; D004147:Dioxins / Q000737:chemistry*; D006219:Halogens / Q000737:chemistry; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D018336:Receptors, Aryl Hydrocarbon / Q000737:chemistry*; D055672:Static Electricity
D004147:Dioxins; D006219:Halogens; D018336:Receptors, Aryl Hydrocarbon; C020887:dibenzo(1,4)dioxin