Quantitative Structure–Activity Relationships for Enzymatic Activity of Chloroperoxidase on Metabolizing Organophosphorus Pesticides

Quantitative Structure–Activity Relationship (QSAR) is a powerful approach for predicting activities of organic pollutants with their structure descriptors. This study reports QSAR models for predicting enzymatic activity of chloroperoxidase on metabolizing selected Organophosphorus Pesticides (OPPs). We used ten quantum chemical descriptors computed with ab initio method at HF/6‐31G(d) level and Partial Least Squares (PLS) analysis with optimizing procedure for generating QSAR models. The correlation coefficient of the optimal model was 0.918, and the fitting results showed that it had high fitting precision and good predicting ability. The PLS assistant analysis indicated that the atomic charges of sulfur and phosphorus atoms in the SP bond of an OPP molecule were important in governing the enzymatic activity and the molecular dipole moment also had some effect on the enzymatic activity. It was found that OPPs with high absolute values of atomic charges on the sulfur and phosphorus atoms tended to be metabolized faster, whereas OPPs with stronger polarity tended to be metabolized slower by chloroperoxidase. The optimal model was used to predict the enzymatic activity of two OPPs and the results appeared to be reasonable.