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.

This study explores the role of olfaction in linking Brazilian cultural expressions with Yoruba heritages. Brazilian culture is deeply rooted in African traditions brought across the Atlantic, and this connection is evident in its foods, celebrations, and practices. Through a multidisciplinary approach combining bibliometric and historic review, fieldwork, and sensory analysis, the research reveals how distinct aromas, ranging from the scent of aromatic plants used in celebrations to culinary ingredients, act as olfactory markers of identity and collective memory in the state of Bahia and in a religious community in São Paulo. Historically, the transatlantic slave trade brought Yoruba peoples to Brazil, embedding their traditions deeply into the region’s cultural and everyday life. By documenting the shared olfactory elements between Bahian festivals such as the Lavagem do Bonfim and the practices observed within Yoruba-based cultural expressions in the Oduduwa Temple in São Paulo, the study underscores the significance of olfactory heritage to Afro-Brazilian culture and calls for greater scholarly attention to it. Recognizing smell as a dynamic conduit for history and emotion offers new insights into the survival and evolution of cultural traditions, urging further transdisciplinary research into the sensory narratives that shape our collective identities.

This work explores Generative Flow Networks (GFlowNets) as a computational approach for sustainable fragrance design, focusing on generating novel molecules that reproduce the scent profile of vanillin while reducing reliance on resource-intensive synthesis and environmentally vulnerable natural sources. An integrated pipeline couples a GFlowNet generator with a fragrance note predictor, which guides learning toward a target odor by rewarding molecules predicted to be aromatically similar to vanillin. Chemical validity and realism are enforced through chemistry filters that penalize unstable or implausible structures and through an odorless-vs-odorant classifier, so only chemically and olfactorily plausible candidates are selected. The agent is trained in a hybrid offline–online regime, implementing reinforcement-based exploration, with hyperparameters tuned via Bayesian optimization. As an independent validation layer, an olfactory receptor docking model estimates binding affinities to receptors associated with vanillin, and several generated candidates show comparable or higher predicted affinities than vanillin while retaining structural novelty. Overall, the results suggest that GFlowNets can capture multidimensional olfactory patterns and support CAPE-style exploration of vast chemical spaces under uncertainty, enabling more efficient exploration of olfactory ingredients.