2026-09-24 カリフォルニア大学リバーサイド校（UCR）
Header image credit: Yu Fang/UCR.
＜関連情報＞
- https://news.ucr.edu/articles/2026/09/24/machine-learning-helps-identify-chemicals-repel-honey-bees-pesticides
- https://elifesciences.org/articles/104831
ミツバチの嗅覚行動の機械学習により、野外の自由飛行するミツバチの忌避性臭気物質が特定される Machine learning of honey bee olfactory behavior identifies repellent odorants in free-flying bees in the field
Joel KowalewskiBarbara F Baer-ImhoofTom GudaMatthew LuyPayton DePalmaBoris BaerAnandasankar Ray
eLife Published:Sep 23, 2026
DOI:https://doi.org/10.7554/eLife.104831.3
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 >170 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 shows 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.
