Leveraging In Silico Structure-Activity Models to Predict Acute Honey Bee (Apis mellifera) Toxicity for Agrochemicals.

Abstract

In the realm of crop protection products, ensuring the safety of pollinators stands as a pivotal aspect of advancing sustainable solutions. Extensive research has been dedicated to this crucial topic as well as new approach methodologies in toxicity testing. Hence, within the agricultural and chemical industries, prioritizing pollinator safety remains a constant objective during the development of predictive tools. One of these tools includes computational models like quantitative structure-activity relationships (QSARs) that are valuable in predicting the toxicity of chemicals. This research uses bee toxicity data to develop artificial neural network classification models for predicting honey bee acute toxicity. Bee toxicity data from 1542 compounds were used to develop models; the sensitivity and specificity of the best model were 0.90 and 0.91, respectively. These in silico models can aid in the discovery of next-generation crop protection products. These tools can guide the screening and selection of next-generation crop protection molecules with high margins of safety to pollinators, and candidates with favorable sustainability profiles can be identified at the early discovery stage as precursors to in vivo data generation.