Evidence map›Paper›PMID 37008127›Full record

ArticleACS omega2023

Generating Flavor Molecules Using Scientific Machine Learning.

Luana P Queiroz, Carine M Rebello, Erbet A Costa, Vinícius V Santana, Bruno C L Rodrigues, Alírio E Rodrigues, Ana M Ribeiro, Idelfonso B R Nogueira

Open access · goldAbstract read
In one paragraph

Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
4.2field-weighted citation impact, top 5% 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

4 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
  2. Review on Lipase-Catalyzed Flavor Synthesis: Global Trends and Advances.Journal of agricultural and food chemistry · 2025
    Review
  3. Article
  4. 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

8 authors at 3 institutions in 3 countries.

Luana P QueirozLSRE-LCM-Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.ORCID https://orcid.org/0000-0003-3092-8136
Carine M RebelloDepartamento de Engenharia Química, Escola Politécnica (Polytechnic Institute), Universidade Federal da Bahia, 40210-630 Salvador, Brazil.
Erbet A CostaDepartamento de Engenharia Química, Escola Politécnica (Polytechnic Institute), Universidade Federal da Bahia, 40210-630 Salvador, Brazil.
Vinícius V SantanaLSRE-LCM-Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.ORCID https://orcid.org/0000-0002-2940-1268
Bruno C L RodriguesLSRE-LCM-Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.
Alírio E RodriguesLSRE-LCM-Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.ORCID https://orcid.org/0000-0002-0715-4761
Ana M RibeiroLSRE-LCM-Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.ORCID https://orcid.org/0000-0003-4269-1420
Idelfonso B R NogueiraChemical Engineering Department, Norwegian University of Science and Technology, Sem Sælandsvei 4, Kjemiblokk 5, 7491 Trondheim, Norway.ORCID https://orcid.org/0000-0002-0963-6449
Universidade do Porto · PTUniversidade Federal da Bahia · BRNorwegian University of Science and Technology · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Flavor is an essential component in the development of numerous products in the market. The increasing consumption of processed and fast food and healthy packaged food has upraised the investment in new flavoring agents and consequently in molecules with flavoring properties. In this context, this work brings up a scientific machine learning (SciML) approach to address this product engineering need. SciML in computational chemistry has opened paths in the compound's property prediction without requiring synthesis. This work proposes a novel framework of deep generative models within this context to design new flavor molecules. Through the analysis and study of the molecules obtained from the generative model training, it was possible to conclude that even though the generative model designs the molecules through random sampling of actions, it can find molecules that are already used in the food industry, not necessarily as a flavoring agent, or in other industrial sectors. Hence, this corroborates the potential of the proposed methodology for the prospecting of molecules to be applied in the flavor industry.

Identifiers

PMID37008127
PMCPMC10061502
OpenAlexW4324326558

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.