Review Maps Food-Derived Multi-Target Antihypertensive Peptides, Mechanisms, and AI Screening Strategies

Hypertension is a significant global health challenge, often managed with traditional drugs that carry side effects. This has spurred interest in natural antihypertensive agents. While many existing peptides target single pathways, limiting efficacy against hypertension's complex pathophysiology, multi-target peptides offer a promising alternative by modulating multiple disease-related networks. Key pathways involved include the renin-angiotensin-aldosterone system (RAAS), where enzymes like renin and angiotensin-converting enzyme (ACE) play pivotal roles. Understanding these complex interactions is crucial for developing safer and more effective interventions.

This narrative review comprehensively summarized the latest research on multi-target antihypertensive peptides. The authors explored their main food sources, categorizing them into animal, plant, and microorganism origins. They detailed the bioactive mechanisms through which these peptides exert their effects. Furthermore, the review described the application of artificial intelligence (AI) and network pharmacology in screening for multi-target antihypertensive peptides, highlighting various machine learning (ML) models and activity prediction websites used in this process. The scope included current challenges and future research directions.

The review highlighted that multi-target antihypertensive peptides offer enhanced blood pressure control and reduced resistance risks compared to single-target agents, due to their ability to modulate complex hypertension-related networks. Diverse food sources, including dairy, fish, legumes, and fermented products, were identified as rich in these bioactive peptides. Their mechanisms often involve ACE inhibition, but also extend to other pathways such as renin inhibition, endothelin-1 antagonism, and antioxidant effects. The integration of AI and network pharmacology significantly accelerates the discovery and optimization of these peptides. > The review emphasizes that AI-driven screening, utilizing machine learning models and predictive algorithms, is transforming the identification of novel multi-target antihypertensive peptides, providing a theoretical basis for future development. This computational approach allows for efficient prediction of peptide activity and potential multi-target interactions, streamlining the discovery pipeline.