Computational Discovery of Plant-Derived Peptides with Therapeutic Potential: A Scoping Review

Computational methods are increasingly applied to the discovery of plant-derived peptides, but the extent, composition and reporting practice of this literature have not been mapped. Following PRISMA-ScR and Joanna Briggs Institute guidance, Scopus, Web of Science, ScienceDirect and PubMed were searched for records published between 2021 and 2026. Of 5062 unique records, 593 were screened at title and abstract, 88 full texts were assessed, 80 sources met all five eligibility criteria and 79 were charted. Overall, 49 (62.0%) were primary studies, 12 (15.2%) described a tool or database and 18 (22.8%) were narrative reviews. Antimicrobial peptides were the commonest class (33, 41.8%), followed by antioxidant (21, 26.6%) and ACE-inhibitory peptides (18, 22.8%). Only 11 sources (13.9%) trained and evaluated a model of their own, whereas 23 (29.1%) combined prediction with molecular docking. Of 61 empirical sources, 14 (23.0%) reported at least one quantitative prediction-task performance figure and 13 (21.3%) benchmarked a named comparator; the metrics used were not mutually comparable. Moreover, 18 of 62 applicable sources (29.0%) confirmed their own prediction experimentally; 70 (88.6%) named a plant source, most often Arabidopsis thaliana; and 8 (10.1%) linked their material to documented traditional medicine. No source applied machine learning to African plant peptides, an absence within the literature sampled rather than proof that none exists.