Artificial Intelligence for Marine Functional Peptide Discovery: Challenges, Advances, and Future Perspectives

Marine organisms represent a rich but underexplored source of bioactive peptides with potential applications in therapeutics, nutraceuticals, and biotechnology. However, conventional marine peptide discovery relies on labor-intensive workflows involving specimen collection, extraction, purification, structural characterization, and experimental validation, limiting the efficient exploration of the rapidly expanding marine sequence space. Recent advances in artificial intelligence (AI) provide new opportunities to accelerate peptide discovery by enabling large-scale sequence analysis, functional prediction, and candidate prioritization. This review summarizes recent advances in AI-driven marine functional peptide discovery, covering peptide resources, computational representations, machine-learning and deep-learning methods, protein language models, generative approaches, genome and metagenome mining, and structure-aware prediction strategies. We further discuss representative applications involving marine microorganisms, animal proteomes, cyanobacterial biosynthetic pathways, metagenomic datasets, and marine-derived peptide candidates, highlighting current achievements and limitations. Finally, we analyze key challenges and future perspectives, including marine-specific datasets, multimodal AI models integrating sequence, structural, chemical, and biosynthetic information, rigorous benchmarking, and computational–experimental integration. This review provides a comprehensive framework for understanding how AI can support the discovery and development of functional peptides from marine resources.