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This study leverages machine learning to screen millions of chemical compounds, identifying specific scents that repel honey bees from pesticides without harming them. By bypassing exhaustive physical testing, this computational approach accelerates the discovery of protective agents for declining pollinator populations. The successful validation in both lab and field settings demonstrates a practical application of AI in addressing critical environmental challenges.
Sensorygen’s machine learning spots scents to keep bees safe from pesticides

Researchers used machine learning to identify 130 chemical compounds that repel honey bees from pesticides, offering a potential solution to declining populations.
Researchers at the University of California, Riverside have created a machine learning model that screened over 50 million compounds to identify approximately 130 potential bee repellents, offering a new approach to protecting pollinator populations from pesticide exposure. The interdisciplinary team, led by Anandasankar Ray, a professor of molecular, cell and systems biology, directly addressed the challenge…
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