Food-Derived Antihypertensive Peptides: A Review of Preparation Strategies, Multitarget Mechanisms, and Machine Learning Advances

The review consolidates diverse sources of food-derived antihypertensive peptides together with novel preparation strategies, aiming at scalability and feasibility in industrial production. Rather than organizing around a single source or a single process, it places sources and preparation strategies within one framework explicitly oriented toward scalability and industrial feasibility. This is a review-level synthesis; the abstract states it 'summarizes the diverse sources of antihypertensive peptides and novel preparation strategies' without reporting specific process parameters or yield data.

The review discusses the multitarget mechanisms of food-derived antihypertensive peptides and their structure-activity relationships in depth. By pairing 'multitarget mechanisms' with 'structure-activity relationships,' it signals that antihypertensive effects should not be reduced to a single pathway or a single sequence feature. This is a literature-level treatment of mechanisms and structure-activity relationships; the abstract describes it as 'deeply review their multitarget mechanisms and structure-activity relationships' without listing specific targets or quantitative model metrics.

Machine learning models play a贯穿 role in the precise identification and activity prediction of antihypertensive peptides. Machine learning is positioned as spanning the identification-to-prediction pipeline rather than serving as an auxiliary step in one preparation stage. The abstract states that 'machine learning models facilitate the precise identification and activity prediction,' without specifying model types, dataset sizes, or predictive performance values.

The authors argue that future efforts should integrate advanced biotechnologies and intelligent platforms to accelerate the translation of antihypertensive peptides from laboratory to clinical use. The integration of biotechnology and intelligent platforms is framed as a directional judgment about the translation pathway, not merely an extension of a single technical route. This is a forward-looking statement, expressed as 'future endeavors should integrate advanced biotechnologies and intelligent platforms,' with no timeline or clinical validation results provided.

The scope of this review is the topic of food-derived antihypertensive peptides: it is intended for readers interested in food-derived bioactive peptides, process scale-up, multitarget mechanisms and structure-activity relationships, and machine-learning-assisted peptide identification and activity prediction. It works as a framing read for entering this area, helping build an overall picture from source to preparation to mechanism to prediction to translation, rather than providing implementation details of a specific process or a specific model.

Because only the abstract is currently visible, readers should still watch for: the specific list of peptide sources and preparation strategies covered, the particular pathways involved in the multitarget mechanisms, the quantitative description of structure-activity relationships, and the data types and predictive performance of the machine learning models, none of which appear in the abstract. Whether and how these elements support the judgment about accelerating the laboratory-to-clinic transition remains an open question that requires reading the full text.