Predictionof Environmental FateandToxicityofInsecticidesUsing Multi‐Target QSAR Approach

Abstract

Ecotoxicological risk assessments form the foundation of regulatory decisions for industrial chemicals used in various sectors. In this study, a multi‐target‐QSAR model established by a backpropagation neural network trained with the Levenberg‐Marquardt (LM) algorithm was used to construct a statistically robust and easily interpretable Mt‐QSAR model with high external predictability for the simultaneous prediction of the environmental fate in form of octanol‐water partition coefficient (LogP), (BCF) and acute oral toxicity in mammals and birds (LD50rat) and (LD50bird) for a wide range of chemical structural classes of insecticides. Principal component analysis was performed on descriptors selected by the SW‐MLR method, and the selected PCs were used for constructing the SW‐MLR‐PCA‐ANN model. The developed well‐trained model (RMSE=0.83, MPE=0.004, CCC=0.82, IIC=0.78, R2=0.69) was statistically robust as indicated by the external validation parameters (RMSE=0.93, MPE=0.008, CCC=0.77, IIC=0.68, R2=0.61). The AD of the developed Mt‐QSAR model was also defined to identify the most reliable predictions. Finally, the missing values in the dataset for the aforementioned targets were predicted using the constructed Mt‐QSAR model. The proposed approach can be used for simultaneous prediction of the environmental fate of new insecticides, especially ones that haven′t been tested yet.