ReviewFrontiers in nutrition2025
Can artificial intelligence uncover the bioactive peptides' benefits for human health and knowledge? A narrative review.
Review in Frontiers in nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Functional Peptides: Comparing Synthetic and Sequence-Engineered Antibiofilm Pharmaceutics.Pharmaceutics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The intersection of Artificial Intelligence (AI) and food science has opened new frontiers in understanding the "dark matter" of food, the vast array of unidentified bioactive compounds that influence human health. This narrative review examines how AI, particularly machine learning and deep learning, is revolutionizing the discovery, characterization, and application of bioactive peptides and amino acids derived from food sources, both plant- and animal-based. These compounds exhibit diverse health benefits, including antioxidant, anti-inflammatory, antihypertensive, and antimicrobial properties, yet their complexity and the limitations of traditional methods have hindered comprehensive study. AI-driven approaches, such as predictive modeling, molecular dynamics simulations, and natural language processing, are accelerating the identification of bioactive peptides, optimizing extraction processes, and enabling personalized nutrition strategies. The integration of AI with omics technologies (e.g., nutrigenomics, proteomics) further enhances our understanding of how these peptides modulate physiological pathways. However, this is not without challenges and limitations, such as data quality, model interpretability, and persistent gaps in interdisciplinary collaboration. Additionally, the review highlights the lack of standardized databases and concerns about the use of AI, including the need for ethical approvals and protocols aligned with privacy laws, particularly in the context of personalized nutrition guidance. This review synthesizes current advancements, identifies research gaps, and underscores the transformative potential of AI in functional food development and precision nutrition. By addressing these challenges, AI can unlock the full therapeutic potential of food-derived bioactive compounds, providing innovative solutions to global health challenges such as non-communicable diseases. The findings advocate for robust interdisciplinary efforts to bridge computational and nutritional sciences, paving the way for scalable, evidence-based applications in health and wellness.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.