Explainable AI for Personalized Selection of Therapeutic Peptides in Age-Related Diseases

Abstract

Age-related diseases constitute a growing share of global morbidity, and therapeutic peptides have emerged as a versatile modality capable of modulating pathways implicated in cellular senescence, inflammation, and metabolic dysregulation. Yet the personalization of peptide therapy remains difficult because candidate selection depends on heterogeneous patient data, incomplete mechanistic understanding, and regulatory expectations of clinical justification. This paper examines how explainable artificial intelligence can be positioned as a system-level capability for personalized selection of therapeutic peptides in age-related diseases, rather than as a narrow post-hoc visualization layer. We argue that explainability must be engineered into the data infrastructure, model lifecycle, clinician interface, and governance apparatus of peptide selection platforms. The discussion develops a layered architecture that integrates omics, clinical, and pharmacological signals, and then analyzes structural trade-offs involving predictive performance, interpretability, robustness under distribution shift, fairness across aging populations, computational sustainability, and policy compliance. Particular attention is given to the tension between intrinsic and post-hoc explanation strategies, the instability of attribution methods in high-dimensional biomedical settings, and the institutional conditions under which explanations become actionable for clinicians, regulators, and patients. The paper concludes that explainable peptide selection should be treated as a socio-technical infrastructure whose credibility depends as much on documentation, auditing, and accountability mechanisms as on algorithmic design choices.