Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field

Abstract

Preventing beneficial insects like honey bees (Apis mellifera) from contacting pesticides on crops using odorants could counter current pollinator declines. However, the discovery of behaviorally aversive odorants is impeded by the complexity of the honey bee olfactory system where >180 olfactory receptors detect volatiles and generate valence. To solve this systems-level challenge we generated a machine-learning model to predict aversive valence from chemical structure using published olfactory behavior data in honey bees. We refine the predictive model by generating species level behavioral data for honey bees and Drosophila on an initial set of novel predicted repellents. The improved second computational model was then used to screen a chemical space of >50 million compounds and identify >130 repellent candidates. Behavioral validation using honey bees in the laboratory show a high predictive success. Additional testing of the top seven candidates using freely foraging honey bees in a field assay confirmed strong repellency, thus predicting a high probability to repel foraging bees from pesticide-treated crops. Machine learning, with iterative testing and modeling therefore provides a powerful approach for rational discovery of aversive volatiles for control of insects for which limited data is available. SIGNIFICANCE STATEMENTWith honey bee populations declining partly due to pesticide exposure, we aimed to find smells that could keep bees away from pesticide-treated crops. We overcome challenges studying the complex bee olfactory system by developing an AI model trained on existing bee behavior data to predict chemicals bees would find aversive. The predictive model screened millions of compounds, identifying more than 130 potential repellents. Behavior testing in the lab and in field tests confirmed the effectiveness of the bee repellents. This method could lead to bee-safe pesticide formulations, potentially protecting pollinator populations while maintaining crop protection.