ArticleScientific reports2020
Machine learning decodes chemical features to identify novel agonists of a moth odorant receptor.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 33 citations in OpenAlex.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- From data to scent: validating an ensemble AI model that predicts novel insect odourant interactions.Journal of molecular modeling · 2026Article
- Insight into the Relationships Between Chemical, Protein and Functional Variables in the PBP/GOBP Family in Moths Based on Machine Learning.International journal of molecular sciences · 2025Article
- A deep learning and digital archaeology approach for mosquito repellent discovery.Chemical senses · 2025Article
- Accelerating Ligand Discovery for Insect Odorant Receptors.International journal of biological sciences · 2025Article
- Combining Machine Learning and Electrophysiology for Insect Odorant Receptor Studies.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Review
- Article
- Large-Scale G Protein-Coupled Olfactory Receptor-Ligand Pairing.ACS central science · 2022Article
- Mosquito Olfactory Response Ensemble enables pattern discovery by curating a behavioral and electrophysiological response database.iScience · 2022Article
- Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors.International journal of molecular sciences · 2021Article
- Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists.Cellular and molecular life sciences : CMLS · 2021Article
- Bitter Taste and Olfactory Receptors: Beyond Chemical Sensing in the Tongue and the Nose.The Journal of membrane biology · 2021Article
- Antennal transcriptome sequencing and identification of candidate chemoreceptor proteins from an invasive pest, the American palm weevil, Rhynchophorus palmarum.Scientific reports · 2021Article
- Odorant receptor phylogeny confirms conserved channels for sex pheromone and host plant signals in tortricid moths.Ecology and evolution · 2020Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Odorant receptors expressed at the peripheral olfactory organs are key proteins for animal volatile sensing. Although they determine the odor space of a given species, their functional characterization is a long process and remains limited. To date, machine learning virtual screening has been used to predict new ligands for such receptors in both mammals and insects, using chemical features of known ligands. In insects, such approach is yet limited to Diptera, whereas insect odorant receptors are known to be highly divergent between orders. Here, we extend this strategy to a Lepidoptera receptor, SlitOR25, involved in the recognition of attractive odorants in the crop pest Spodoptera littoralis larvae. Virtual screening of 3 million molecules predicted 32 purchasable ones whose function has been systematically tested on SlitOR25, revealing 11 novel agonists with a success rate of 28%. Our results show that Support Vector Machine optimizes the discovery of novel agonists and expands the chemical space of a Lepidoptera OR. More, it opens up structure-function relationship analyses through a comparison of the agonist chemical structures. This proof-of-concept in a crop pest could ultimately enable the identification of OR agonists or antagonists, capable of modifying olfactory behaviors in a context of biocontrol.
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