ArticleNPJ science of food2025
A chemical language model for molecular taste prediction.
Article in NPJ science of food, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- FlavorGPN: a graph neural network for multi-label molecular flavor prediction.Molecular diversity · 2026Article
- Molecular and Computational Basis of Taste Perception: A Review toward the "Digital Language of Taste".ACS omega · 2026Review
- Predicting food taste with bound-driven optimization.Current research in food science · 2026Article
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Authors and funding
5 authors.
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Abstract
Determining molecular taste remains a significant challenge in food science. Here, we present FART (Flavor Analysis and Recognition Transformer), a chemical language model capable of predicting molecular taste from chemical structure. Trained on the largest public dataset (15,025 compounds) of molecular tastants to date, FART is the first model capable of parallel predictions across four taste categories: sweet, bitter, sour, and umami. FART achieves an accuracy above 91% for parallel taste prediction and outperforms previous state-of-the-art binary classifier models that specialize on predicting one taste class. Its transformer architecture allows for interpretability through gradient-based visualization of molecular features. The model identifies key structural elements driving taste properties and demonstrates utility in analyzing known tastants as well as novel compounds. By releasing both the model and dataset, we equip the food science community with tools for rapid taste prediction, accelerating flavor compound development and enabling systematic exploration of taste chemistry.
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Registered trials
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