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A Reinforcement Learning Framework to Discover Natural Flavor Molecules

Flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. However, the development of natural flavors plays a critical role in modern society. Considering this, the...

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Autores principales: Queiroz, Luana P., Rebello, Carine M., Costa, Erbet A., Santana, Vinícius V., Rodrigues, Bruno C. L., Rodrigues, Alírio E., Ribeiro, Ana M., Nogueira, Idelfonso B. R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10048107/
https://www.ncbi.nlm.nih.gov/pubmed/36981074
http://dx.doi.org/10.3390/foods12061147
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author Queiroz, Luana P.
Rebello, Carine M.
Costa, Erbet A.
Santana, Vinícius V.
Rodrigues, Bruno C. L.
Rodrigues, Alírio E.
Ribeiro, Ana M.
Nogueira, Idelfonso B. R.
author_facet Queiroz, Luana P.
Rebello, Carine M.
Costa, Erbet A.
Santana, Vinícius V.
Rodrigues, Bruno C. L.
Rodrigues, Alírio E.
Ribeiro, Ana M.
Nogueira, Idelfonso B. R.
author_sort Queiroz, Luana P.
collection PubMed
description Flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. However, the development of natural flavors plays a critical role in modern society. Considering this, the present work proposes a novel framework based on scientific machine learning to undertake an emerging problem in flavor engineering and industry. It proposes a combining system composed of generative and reinforcement learning models. Therefore, this work brings an innovative methodology to design new flavor molecules. The molecules were evaluated regarding synthetic accessibility, the number of atoms, and the likeness to a natural or pseudo-natural product. This work brings as contributions the implementation of a web scraper code to sample a flavors database and the integration of two scientific machine learning techniques in a complex system as a framework. The implementation of the complex system instead of the generative model by itself obtained 10% more molecules within the optimal results. The designed molecules obtained as an output of the reinforcement learning model’s generation were assessed regarding their existence or not in the market and whether they are already used in the flavor industry or not. Thus, we corroborated the potentiality of the framework presented for the search of molecules to be used in the development of flavor-based products.
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spelling pubmed-100481072023-03-29 A Reinforcement Learning Framework to Discover Natural Flavor Molecules Queiroz, Luana P. Rebello, Carine M. Costa, Erbet A. Santana, Vinícius V. Rodrigues, Bruno C. L. Rodrigues, Alírio E. Ribeiro, Ana M. Nogueira, Idelfonso B. R. Foods Article Flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. However, the development of natural flavors plays a critical role in modern society. Considering this, the present work proposes a novel framework based on scientific machine learning to undertake an emerging problem in flavor engineering and industry. It proposes a combining system composed of generative and reinforcement learning models. Therefore, this work brings an innovative methodology to design new flavor molecules. The molecules were evaluated regarding synthetic accessibility, the number of atoms, and the likeness to a natural or pseudo-natural product. This work brings as contributions the implementation of a web scraper code to sample a flavors database and the integration of two scientific machine learning techniques in a complex system as a framework. The implementation of the complex system instead of the generative model by itself obtained 10% more molecules within the optimal results. The designed molecules obtained as an output of the reinforcement learning model’s generation were assessed regarding their existence or not in the market and whether they are already used in the flavor industry or not. Thus, we corroborated the potentiality of the framework presented for the search of molecules to be used in the development of flavor-based products. MDPI 2023-03-08 /pmc/articles/PMC10048107/ /pubmed/36981074 http://dx.doi.org/10.3390/foods12061147 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Queiroz, Luana P.
Rebello, Carine M.
Costa, Erbet A.
Santana, Vinícius V.
Rodrigues, Bruno C. L.
Rodrigues, Alírio E.
Ribeiro, Ana M.
Nogueira, Idelfonso B. R.
A Reinforcement Learning Framework to Discover Natural Flavor Molecules
title A Reinforcement Learning Framework to Discover Natural Flavor Molecules
title_full A Reinforcement Learning Framework to Discover Natural Flavor Molecules
title_fullStr A Reinforcement Learning Framework to Discover Natural Flavor Molecules
title_full_unstemmed A Reinforcement Learning Framework to Discover Natural Flavor Molecules
title_short A Reinforcement Learning Framework to Discover Natural Flavor Molecules
title_sort reinforcement learning framework to discover natural flavor molecules
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10048107/
https://www.ncbi.nlm.nih.gov/pubmed/36981074
http://dx.doi.org/10.3390/foods12061147
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