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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10048107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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