Abstract: With the advance of food and nutritional science, personalized nutrition intervention has gradually emerged as a crucial approach for promoting health and preventing chronic diseases. Food-derived bioactive peptides have gained increasing attention due to their diverse physiological activities, including antioxidant, antimicrobial, antihypertensive, and immunomodulatory effects. However, traditional research strategies are still labor-intensive and inefficient. The rapid development of artificial intelligence (AI) offers new opportunities for the efficient screening and rational design of functional peptides. This review systematically summarizes the nutritional and health functions of antimicrobial peptides, antioxidant peptides, angiotensin-converting enzyme (ACE) inhibitory peptides, and other bioactive peptides, with a particular focus on recent progress in AI-assisted prediction, generation, and activity validation of functional peptides. Furthermore, the potential application value of AI in personalized nutrition interventions is highlighted. Current challenges regarding data quality, model interpretability, and experimental validation are also critically discussed. Finally, possible solutions such as multimodal modeling, transfer learning, and integrated computational-experimental strategies are proposed. Collectively, AI-driven peptide research is expected to accelerate the transition of food and nutritional science toward more intelligent and precise approaches, providing vital support for the development of personalized nutrition and health intervention systems.

Key words: food nutrition; food-derived bioactive peptides; artificial intelligence; personalized nutrition