2025-03-05 カリフォルニア大学リバーサイド校 (UCR)
<関連情報>
- https://news.ucr.edu/articles/2025/03/05/enhancing-mosquito-repellent-effectiveness
- https://elifesciences.org/reviewed-preprints/95532v1
ヒトと昆虫の嗅覚の機械学習ベースのモデリング 何百万もの化合物をスクリーニングし、心地よい香りの虫除けを同定する Machine Learning Based Modelling of Human and Insect Olfaction Screens Millions of compounds to Identify Pleasant Smelling Insect Repellents
Joel Kowalewski,Sean M. Boyle,Ryan Arvidson,Jadrian Ejercito,Anandasankar Ray
eLife Published:March 14, 2024
DOI:https://doi.org/10.7554/eLife.95532.1
Abstract
The rational discovery of behaviorally active odorants is impeded by a lack of understanding on how the olfactory system generates percept or valence for a volatile chemical. In previous studies we showed that chemical informatics could be used to model prediction of ligands for a large repertoire of odorant receptors in Drosophila (Boyle et al., 2013). However, it remained difficult to predict behavioral valence of volatiles since the activities of a large ensembles of odor receptors encode odor information, and little is known of the complex information processing circuitry. This is a systems-level challenge well-suited for Machine-learning approaches which we have used to model olfaction in two organisms with completely unrelated olfactory receptor proteins: humans (∼400 GPCRs) and insects (∼100 ion-channels). We use chemical structure-based Machine Learning models for prediction of valence in insects and for 146 human odor characters. Using these predictive models, we evaluate a vast chemical space of >10 million compounds in silico. Validations of human and insect behaviors yield very high success rates. The discovery of desirable fragrances for humans that are highly repulsive to insects offers a powerful integrated approach to discover new insect repellents.
