Food-Derived Multi-Target Antihypertensive Peptides: Sources, Mechanisms and AI-Driven Strategies

Hypertension is a major global public health challenge. Traditional antihypertensive drugs often cause side effects, which has prompted growing interest in natural antihypertensive agents. However, most existing antihypertensive peptides target single pathways, thereby constraining their effectiveness against hypertension's complex mechanisms. In contrast, multi-target peptides modulate complex hypertension-related networks, offering enhanced blood pressure control and reduced resistance risks. This narrative review comprehensively summarizes the latest research progress on multi-target antihypertensive peptides, including their main food sources (animal, plant, and microorganism sources) and bioactive mechanisms. In addition, this review also describes the process of artificial intelligence (AI) and network pharmacology-driven multi-target antihypertensive peptide screening, and summarizes the machine learning (ML) models and activity prediction websites that have been applied to antihypertensive peptide screening. Finally, this review explores the challenges and future directions in multi-target antihypertensive peptide research, thereby providing a theoretical basis for the development of novel multi-target antihypertensive peptides.