In silico prediction of chemical acute contact toxicity on honey bees via machine learning methods

In recent years, the decline of honey bees and the collapse of bee colonies have caught the attention of ecologists, and the use of pesticides is one of the main reasons for the decline. Therefore, ecological risk assessment of pesticides is essential and necessary. In silico tools, such as QSAR models can play an important role in predicting physicochemical and biological properties of chemicals. In this study, a total of 54 classification models were developed by combination of 6 machine learning methods along with 9 kinds of molecular fingerprints based on the experimental honey bees acute contact toxicity data (LD₅₀) of 676 structurally diverse pesticides. The best model proposed was SVM algorithm combined with CDK extended fingerprint. The analysis of the applicability domain of the model successfully excluded some extreme molecules. Additionally, 9 structural alerts about honey bees acute contact toxicity were identified by information gain and substructure frequency analysis.