Graph-Based Drug–Peptide Recommendation for Discovering Novel Therapeutic Strategies against Age-Related Diseases

Abstract

Age-related diseases are increasingly understood as interconnected manifestations of biological aging rather than isolated clinical entities, creating a need for therapeutic discovery strategies that can reason across drugs, peptides, proteins, pathways, and phenotypes. This paper examines graph-based drug–peptide recommendation as a socio-technical infrastructure for discovering novel therapeutic strategies against age-related diseases. It argues that graph learning can expand the search space beyond conventional small-molecule repurposing by representing peptides as first-class therapeutic entities with distinct pharmacokinetic, manufacturing, and regulatory properties. The discussion develops a system-level architecture that integrates heterogeneous biomedical knowledge, graph representation learning, recommendation ranking, expert review, and clinical translation. It then analyzes structural trade-offs involving data quality, model interpretability, computational scale, deployment latency, and sustainability. Particular attention is given to governance, fairness, robustness, and policy implications, because recommendations that influence therapeutic research priorities can amplify existing inequalities if training data, validation cohorts, and access pathways are not carefully governed. The paper concludes that graph-based drug–peptide recommendation should not be treated as a purely algorithmic pipeline but as a governed discovery infrastructure requiring transparent evidence trails, federated data stewardship, lifecycle evaluation, and equitable translation pathways. Such an infrastructure may help identify combination and repurposing opportunities that target shared aging mechanisms while preserving the flexibility needed for disease-specific clinical development.