Predicting acute toxicity of pesticides towards <i>Daphnia magna</i> with random forest algorithm

A large number of pesticides are released into the environment, resulting in serious threat for aquatic organisms. In this work, 15 quantum chemical descriptors were used to develop a quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for toxicity pEC₅₀ of 745 pesticides towards <i>Daphnia magna</i>, by using random forest algorithm. The optimal QSTR model in this paper yielded a coefficient of determination of 0.828, root-mean-square error of 0.798, and mean absolute error of 0.628 for the test set of 149 pesticides, which are accurate values compared with those of QSTR models published recently. Research has revealed that increasing molecular size (or molar volume), the most positive atomic Mulliken (or APT) charge with hydrogens summed into heavy, and the highest occupied molecular orbital (HOMO) energy, can result in higher toxicity pEC₅₀. Increasing the lowest unoccupied molecular orbital (LUMO) energy and the HOMO and LUMO energy gap can lead to lower toxicity pEC₅₀.