Classification of hydroxylated polychlorinated biphenyls as agonists and nonagonists of estrogen receptors using linear discriminant analysis and decision tree models

Abstract

Hydroxylated polychlorinated biphenyls (OH-PCBs) are potential endocrine disruptors due to their interaction with nuclear receptors. However, experimental evaluation of their estrogenic activity is costly and time-consuming, limiting data availability. In this study, quantitative structure-activity relationship (QSAR) models were constructed using linear discriminant analysis (LDA) and decision tree (DT) with both 2D autocorrelation and arithmetic residuals in K-groups analysis (ARKA) descriptors to classify OH-PCBs as agonists or nonagonists of estrogen receptors (ERα and ERβ). For the ERα dataset, the training, test, and cross-validation set accuracies were 89.2%, 84.0%, and 88.0% for the LDA model developed with 2D autocorrelation descriptors (Model I); 89.2%, 72.0%, and 84.9% for the DT model developed with 2D autocorrelation descriptors (Model II); and 89.2%, 80.0%, and 87.0% for the ARKA-based model (Model V). Area under receiver operating characteristic (AUC-ROC) values of 0.959, 0.903, and 0.954 were obtained for Models I, II, and V respectively. For the ERβ dataset, the training, test, and cross-validation set accuracies were 90.5%, 84.0%, and 87.9% for the LDA model constructed with 2D autocorrelation descriptors (Model III); 89.2%, 68.0%, and 83.9% for the DT model constructed with 2D autocorrelation descriptors (Model IV); and 87.8%, 80.0%, and 84.9% for the ARKA-based model (Model VI). Values for AUC-ROC of 0.966, 0.892, and 0.945 were obtained for Models III, IV, and VI respectively. Overall, the QSAR models reported in this article provide a reliable and efficient approach for screening OH-PCBs for estrogenic activity, offering valuable tools for environmental risk assessment, with ARKA descriptors serving as effective alternatives to conventional descriptors.