Combining Machine Learning and Electrophysiology for Insect Odorant Receptor Studies.

Arthur Comte, Sébastien Fiorucci, Emmanuelle Jacquin-Joly

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Arthur ComteINRAE, Sorbonne Université, CNRS, IRD, Université Paris Cité, Université Paris-Est Créteil Val de Marne, Institut d'Ecologie et des Sciences de l'Environnement de Paris (iEES-Paris) , Versailles, France.

Sébastien FiorucciUniversité Côte d'Azur, CNRS, Institut de Chimie de Nice, Nice, France.

Emmanuelle Jacquin-JolyINRAE, Sorbonne Université, CNRS, IRD, Université Paris Cité, Université Paris-Est Créteil Val de Marne, Institut d'Ecologie et des Sciences de l'Environnement de Paris (iEES-Paris) , Versailles, France. emmanuelle.joly@inrae.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Insects rely on olfaction in many aspects of their life, and odorant receptors are key proteins in this process. Whereas a plethora of insect odorant receptor sequences is available, most of them are still orphan or uncompletely characterized, since their functional studies are usually limited by restricted odorant panels. With joint approaches that combine computational methods like machine learning and electrophysiology measurements, researchers can expand the chemical space of insect odorant receptors and speed up the discovery of new active ligands. This chapter details the methodology for setting up a quantitative structure-activity relationship (QSAR) predictive model for identifying odorant receptor agonists and for conducting single sensillum recordings to validate the predictions.

Indexed as

ElectrophysiologyInsectaInsect ProteinsMachine LearningReceptors, OdorantAnimalsLigandsQuantitative Structure-Activity RelationshipInsect ProteinsLigandsReceptors, OdorantDrosophila melanogasterEmpty neuron systemInsectKNIMEOdorant receptorQuantitative structure–activity relationshipSingle sensillum recording

Identifiers

PMID40249485

DOI10.1021/ci400737s

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.