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.

The flavor is an essential component in developing numerous products in the market. The increasing consumption of processed and fast food and healthy packages has upraised the investment in new flavoring agents and, consequently, molecules with flavoring properties. In this context, this work brings a Scientific Machine Learning approach to address this product engineering need. Scientific Machine Learning in computational chemistry has opened paths in predicting a compound's properties without requiring synthesis. This work proposes a novel framework of deep generative models within this context to design new flavor molecules.

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.

In the past decades, the flavor industry's investment in research and development has increased to take innovative steps. Therefore, a new field to acknowledge the flavor industry challenges and concerns has arisen, developing innovative tools for the area of flavor engineering. Meanwhile, the lack of information and datasets regarding the flavored molecules and specific flavorings properties are obstacles to advances in this sector. In this context, this work presents the implementation of three Scientific Machine Learning techniques as an approach to specify flavoring characteristics in newly designed molecules. Therefore, this work brings an innovative methodology to design new natural flavor molecules with specific desired properties to product development. The Transfer Learning technique is presented, alongside a deep generative and a deep reinforcement learning models, to tackle the lack of data available when analyzing and studying flavor molecules and developing flavor-based products. This work brings as contributions the utilization of a web scrapper code to sample specific flavors’ databases, apply a generative model as well as a reinforcement learning one in a transfer learning context, integrates three Scientific Machine Learning techniques in a complex system as a framework, and approaches the transfer learning model training one-by-one keeping the parameters constant but training the neural networks specifically for each case. The deep transfer learning implementation in this purpose presented excellent results, regarding the generation of molecules based on specific flavor descriptors. Nine flavor descriptors were studied along this work and all of them presented more than 50% of new molecules generated within the outstanding results considered for the evaluation metric, Natural Product Likeness Score and Synthetic Accessibility Score. Finally, a discussion of the results is constructed based on the data availability, the presence in nature, and the multisensorial components of flavor impact for the specific flavors’ results.