Evidence map›Paper›PMID 35799226›Full record

ArticleJournal of cheminformatics2022

BitterMatch: recommendation systems for matching molecules with bitter taste receptors.

Eitan Margulis, Yuli Slavutsky, Tatjana Lang, Maik Behrens, Yuval Benjamini, Masha Y Niv

Abstract read
In one paragraph

Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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.

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

13 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. BitterMasS: Predicting Bitterness from Mass Spectra.Journal of agricultural and food chemistry · 2024
    Article
  9. Article
  10. Article
  11. Bitter Odorants and Odorous Bitters: Toxicity and Human TAS2R Targets.Journal of agricultural and food chemistry · 2023
    Article
  12. Article
  13. Article
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

6 authors.

Eitan Margulis *The Institute of Biochemistry, Food Science and Nutrition, The Robert H Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.
Yuli Slavutsky *Department of Statistics and Data Science, Faculty of Social Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
Tatjana LangLeibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany.
Maik BehrensLeibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany.
Yuval BenjaminiDepartment of Statistics and Data Science, Faculty of Social Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
Masha Y NivThe Institute of Biochemistry, Food Science and Nutrition, The Robert H Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel. masha.niv@mail.huji.ac.il.

Funding

Israel Science Foundation 1129/19
6 · The paper itself

Abstract

Bitterness is an aversive cue elicited by thousands of chemically diverse compounds. Bitter taste may prevent consumption of foods and jeopardize drug compliance. The G protein-coupled receptors for bitter taste, TAS2Rs, have species-dependent number of subtypes and varying expression levels in extraoral tissues. Molecular recognition by TAS2R subtypes is physiologically important, and presents a challenging case study for ligand-receptor matchmaking. Inspired by hybrid recommendation systems, we developed a new set of similarity features, and created the BitterMatch algorithm that predicts associations of ligands to receptors with ~ 80% precision at ~ 50% recall. Associations for several compounds were tested in-vitro, resulting in 80% precision and 42% recall. The encouraging performance was achieved by including receptor properties and integrating experimentally determined ligand-receptor associations with chemical ligand-to-ligand similarities.BitterMatch can predict off-targets for bitter drugs, identify novel ligands and guide flavor design. The novel features capture information regarding the molecules and their receptors, which could inform various chemoinformatic tasks. Inclusion of neighbor-informed similarities improves as experimental data mounts, and provides a generalizable framework for molecule-biotarget matching.

Indexed as

BitterDrugsGPCRsMachine learningTaste

Identifiers

PMID35799226
PMCPMC9261901

What OpenQuestion holds

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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.