Quantitative structure–activity relationship predicting toxicity of pesticides towards Daphnia magna

Abstract

Global pesticide usage reaching 2.7 million metric tons annually, brings a grave threat to non-target organisms, especially aquatic organisms, resulting in serious concerns. Predicting aquatic toxicity of pesticides towards Daphnia magna is significant. In this work, random forest (RF) algorithm, together with ten Dragon molecular descriptors, was successfully utilized to develop a quantitative structure–activity/toxicity relationship (QSAR/QSTR) model for the toxicity pEC₅₀ of 745 pesticides towards Daphnia magna. The optimal QSTR model (RF Model I) based on the RF parameters of ntree = 50, mtry = 3 and nodesize = 5, yielded R² = 0.877, MAE = 0.570, rms = 0.739 (training set of 596 pEC₅₀), R² = 0.807, MAE = 0.732, rms = 0.902 (test set of 149 pEC₅₀), and R² = 0.863, MAE = 0.602, rms = 0.774 (total set of 745 pEC₅₀), which are accurate and satisfactory. The optimal RF model is comparable to other published QSTR models for Daphnia magna, although the optimal RF model possessed a small descriptor subset and dealt with a large dataset of pesticide toxicity pEC₅₀. Thus, the investigation in this work provides a reliable, applicable QSTR model for predicting the toxicity pEC₅₀ of pesticides towards Daphnia magna.