BIPE: Artificial Intelligence-Driven Peptide Bitterness Intensity Prediction Engine

Bitterness, alongside sour, sweet, umami, and salty tastes, constitutes one of the five basic tastes and serves as a key dimension in shaping food flavor profiles. Food protein processing readily generates bitter peptides, whose intense bitterness often leads to consumer rejection, yet these peptides frequently carry beneficial bioactivities, necessitating a trade-off between flavor and functionality. This necessitates the quantitative assessment of bitterness intensity in the early stages of product development. However, experimental assays relying on sensory evaluation and electronic tongue instruments are complex, costly, and limited in throughput, constraining the systematic identification of bitter peptides and process optimization. Here, we present BIPE ( B itterness I ntensity P rediction E ngine), an end-to-end regression model that integrates ESM3 protein language model representations with a multilayer perceptron readout, performing regression of bitterness thresholds in log space to directly assess bitterness intensity from sequence alone. BIPE achieves R 2 = 0.9050 under 10-fold cross-validation and R 2 = 0.9449 on an independent test set. BIPE accurately reproduces trends in both electronic tongue readouts and human sensory scores, demonstrating a consistent external validity across assays. Besides, BIPE accurately differentiates the bitterness intensities of soybean protein hydrolysates produced by multiple commercial proteases. Finally, systematic scanning of the complete pentapeptide sequence space by BIPE further reveals amino acid compositional patterns associated with bitterness, providing mechanistic insights. By advancing from classification to quantitative regression, BIPE enables rational design of low-bitterness peptides, supports flavor engineering and process optimization, and establishes a reusable baseline for taste modeling.