ArticleScientific reports2019
Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 31 citations in OpenAlex.
- Review
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Portable geosmin detection system based on sensor cells expressing insect odorant receptors.Scientific reports · 2026Article
- Molecular bases of insect odorant receptor function: specificity and evolution.Biological reviews of the Cambridge Philosophical Society · 2026Review
- 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
- Classification of Odor-Derived Electroantennograms with Machine Learning.Integrative organismal biology (Oxford, England) · 2025Article
- Combining Machine Learning and Electrophysiology for Insect Odorant Receptor Studies.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Accelerating Ligand Discovery for Insect Odorant Receptors.International journal of biological sciences · 2025Article
- Identification of human skin microbiome odorants that manipulate mosquito landing behavior.Scientific reports · 2024Article
- Review
- Article
- Characterizations of botanical attractant of Halyomorpha halys and selection of relevant deorphanization candidates via computational approach.Scientific reports · 2022Article
- Human attractive cues and mosquito host-seeking behavior.Trends in parasitology · 2022Review
- Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists.Cellular and molecular life sciences : CMLS · 2021Article
- Volatile compounds reveal age: a study of volatile organic compounds released by Chrysomya rufifacies immatures.International journal of legal medicine · 2021Article
- Machine learning decodes chemical features to identify novel agonists of a moth odorant receptor.Scientific reports · 2020Article
- Identification and Expression Profile of Olfactory Receptor Genes Based onFrontiers in physiology · 2020Article
- Sequence-Based Prediction of Olfactory Receptor Responses.Chemical senses · 2019Article
- Volatile allosteric antagonists of mosquito odorant receptors inhibit human-host attraction.The Journal of biological chemistryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Olfaction is a key component of the multimodal approach used by mosquitoes to target and feed on humans, spreading various diseases. Current repellents have drawbacks, necessitating development of more effective agents. In addition to variable odorant specificity subunits, all insect odorant receptors (ORs) contain a conserved odorant receptor co-receptor (Orco) subunit which is an attractive target for repellent development. Orco directed antagonists allosterically inhibit odorant activation of ORs and we previously showed that an airborne Orco antagonist could inhibit insect olfactory behavior. Here, we identify novel, volatile Orco antagonists. We functionally screened 83 structurally diverse compounds against Orco from Anopheles gambiae. Results were used for training machine learning models to rank probable activity of a library of 1280 odorant molecules. Functional testing of a representative subset of predicted active compounds revealed enrichment for Orco antagonists, many structurally distinct from previously known Orco antagonists. Novel Orco antagonist 2-tert-butyl-6-methylphenol (BMP) inhibited odorant responses in electroantennogram and single sensillum recordings in adult Drosophila melanogaster and inhibited OR-mediated olfactory behavior in D. melanogaster larvae. Structure-activity analysis of BMP analogs identified compounds with improved potency. Our results provide a new approach to the discovery of behaviorally active Orco antagonists for eventual use as insect repellents/confusants.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.