ArticleFoods (Basel, Switzerland)2023
A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction.
Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- PharmaGNN: a model for odor prediction based on graph neural networks.Journal of the science of food and agriculture · 2026Article
- Comprehensive Taste Profile Assessment of Underexplored Amino Acids and Protein Derivatives in Umami and Koku.Foods (Basel, Switzerland) · 2026Article
- Machine learning unveils three layers of food complexity.NPJ science of food · 2026Review
- A chemical language model for molecular taste prediction.NPJ science of food · 2025Article
- A systematic review of data and models for predicting food flavor and texture.Current research in food science · 2025Review
- Structure-based metabolite function prediction using graph neural networks.Bioinformatics advances · 2025Article
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Authors and funding
6 authors.
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Abstract
Taste determination in small molecules is critical in food chemistry but traditional experimental methods can be time-consuming. Consequently, computational techniques have emerged as valuable tools for this task. In this study, we explore taste prediction using various molecular feature representations and assess the performance of different machine learning algorithms on a dataset comprising 2601 molecules. The results reveal that GNN-based models outperform other approaches in taste prediction. Moreover, consensus models that combine diverse molecular representations demonstrate improved performance. Among these, the molecular fingerprints + GNN consensus model emerges as the top performer, highlighting the complementary strengths of GNNs and molecular fingerprints. These findings have significant implications for food chemistry research and related fields. By leveraging these computational approaches, taste prediction can be expedited, leading to advancements in understanding the relationship between molecular structure and taste perception in various food components and related compounds.
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