Evidence map›Paper›PMID 29487905›Full record

ArticleScience advances2018

Functional odor classification through a medicinal chemistry approach.

Erwan Poivet, Narmin Tahirova, Zita Peterlin, Lu Xu, Dong-Jing Zou, Terry Acree, Stuart Firestein

Open access · goldAbstract read
In one paragraph

Article in Science advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
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.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 49 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Mlp4green: A Binary Classification Approach Specifically for Green Odor.International journal of molecular sciences · 2024
    Article
  5. Article
  6. Olfactory abstraction: a communicative and metacognitive account.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Design of self-assembly dipeptide hydrogels and machine learning via their chemical features.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  18. A Critique of Olfactory Objects.Frontiers in psychology · 2019
    Review
  19. Review
  20. Narrative review on artificially intelligent olfaction in halitosis.Journal of oral and maxillofacial pathology : JOMFP
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Erwan PoivetNeuroscience Institute, NYU Langone Medical Center, NY 10016, USA.ORCID 0000-0001-8988-9384
Narmin TahirovaDepartment of Biological Sciences, Columbia University, New York, NY 10027, USA.
Zita PeterlinCorporate Research and Development, Firmenich Incorporated, Plainsboro, NJ 08536, USA.
Lu XuDepartment of Biological Sciences, Columbia University, New York, NY 10027, USA.
Dong-Jing ZouDepartment of Biological Sciences, Columbia University, New York, NY 10027, USA.
Terry AcreeDepartment of Food Science, Cornell University, Ithaca, NY 14850, USA.ORCID 0000-0002-6330-6707
Stuart FiresteinDepartment of Biological Sciences, Columbia University, New York, NY 10027, USA.ORCID 0000-0003-1774-5853
Columbia University · USCornell University · USFirmenich (United States) · USNYU Langone Health · US

Funding

The perception of odor blends and mixtures: Modulation, Inhibition and Enhancement of Olfactory Receptors alters perception of complex blendsR01DC013553 · NIDCD · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI FIRESTEIN, STUART J · 2014 to 2024
$3.6M
NIDCD NIH HHS R01 DC013553
6 · The paper itself

Abstract

Crucial for any hypothesis about odor coding is the classification and prediction of sensory qualities in chemical compounds. The relationship between perceptual quality and molecular structure has occupied olfactory scientists throughout the 20th century, but details of the mechanism remain elusive. Odor molecules are typically organic compounds of low molecular weight that may be aliphatic or aromatic, may be saturated or unsaturated, and may have diverse functional polar groups. However, many molecules conforming to these characteristics are odorless. One approach recently used to solve this problem was to apply machine learning strategies to a large set of odors and human classifiers in an attempt to find common and unique chemical features that would predict a chemical's odor. We use an alternative method that relies more on the biological responses of olfactory sensory neurons and then applies the principles of medicinal chemistry, a technique widely used in drug discovery. We demonstrate the effectiveness of this strategy through a classification for esters, an important odorant for the creation of flavor in wine. Our findings indicate that computational approaches that do not account for biological responses will be plagued by both false positives and false negatives and fail to provide meaningful mechanistic data. However, the two approaches used in tandem could resolve many of the paradoxes in odor perception.

Indexed as

AnimalsBehavior, AnimalChemistry, PharmaceuticalEstersHumansMiceOdorantsOlfactory Receptor NeuronsSmellEsters

Identifiers

PMID29487905
PMCPMC5817921
OpenAlexW2788262020

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.