QSAR Model for Predicting Pesticide Aquatic Toxicity

A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures
were encoded into molecular descriptors by an automated, seamless procedure available within the
OpenMolGRID system. Finally, various linear and nonlinear regression techniques were used to obtain
stable and thoroughly validated QSARs. The final model was developed by a counterpropagation neural
network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with
McFarland's principle for biological activity and makes use of seven molecular descriptors, namely HACA-2, HOMO−LUMO energy gap, Kier and Hall index, HA dependent HDSA-1, BETA polarizability, FHBCA
fractional HBSA, and LogP. The model was extensively tested by the test set (R2 = 0.79), the y-scrambling
test, and sensitivity/stability tests.