The use of carbon thirteen nuclear magnetic resonance spectra to predict dioxin and furan binding affinities to the aryl hydrocarbon receptor

Four spectroscopic data-activity relationship (SDAR) models for polychlorinated dibenzofurans (PCDFs) and dibenzodioxins (PCDDs) binding to the aryl hydrocarbon receptor (AhR) have been developed based on simulated C-13 nuclear magnetic resonance (NMR) data. Models were developed using discriminant function analysis of the compounds' spectral data. An SDAR model with two classifications for 26 PCDF compounds had a leave-one-out (LOO) cross-validation accuracy of 89%. A two-classification SDAR model for 14 PCDD compounds had LOO cross-validation accuracy of 95%. A two-classification SDAR model combining 14 PCDD and 26 PCDF compounds had LOO cross-validation accuracy of 88%, while a four-classification SDAR model based on the same 14 PCDD and 26 PCDF compounds had LOO cross-validation accuracy of 92%. We used each appropriate SDAR model to classify 41 PCDD and/or 121 PCDF compounds with unknown binding affinities to the AhR. The SDAR models provide a rapid, simple, and valid way to model the PCDF and PCDD binding activity in relation to the AhR.