A Reinforcement Learning Framework to Discover Natural Flavor Molecules

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Queiroz, LP (Author) Other; Rebello, CM (Author) Other; Costa, EA (Author) Other; Santana, VV (Author) Other; Rodrigues, BCL (Author) Other; Rodrigues, AE (Author) Other; Ana M. Ribeiro (Author) FEUP; Nogueira, IBR (Author) Other

Journal

Title: Foods

Vol. 12

Final page: 1147

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Associated Projects

LA LSRE-LCM - Financiamento Plurianual 2020-2023 para a Unidade LA LSRE-LCM Laboratório de Processos de Separação e Reacção - Laboratório de Catálise e Materiais

Other information

Authenticus ID: P-00Y-39J

DOI: 10.3390/foods12061147

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20