Database, prediction, and antibacterial research of astringency based on large language models.

Astringency, a sensory experience causing mouth dryness, significantly impacts the taste of foods such as wine and tea, and astringent molecules may exhibit antibacterial properties. Traditional methods for predicting astringency are costly, and the connection between astringency and antibacterial activity remains largely unexplored. In this study, we present a pioneering computational approach that includes: (1) the creation of the first comprehensive astringency database comprising 238 molecules; (2) the development of a Ligand-Based Prediction (LBP) framework that combines large language models, deep learning, and traditional machine learning for enhanced molecular and peptide prediction; (3) an astringency predictor achieving 0.95 accuracy and 0.90 AUC, validated through electronic tongue measurements; (4) antibacterial predictors for molecules and peptides with accuracies of 0.92 and 0.88, respectively, revealing that 51 % of astringent molecules possess antibacterial properties; (5) accessibility of these predictors via the AstringentPD and ABPD web servers. This work not only enhances the understanding of taste-related molecules but also elucidates the relationship between astringency and antibacterial properties, setting the stage for future explorations in food science and medicinal applications.