Deep Learning-Based Recommendation of Bioactive Peptides for Aging-Related Disease Intervention
Keywords:
deep learning; bioactive peptides; aging-related diseases; recommendation systems; biomedical data infrastructure; translational governance; fairness; sustainabilityAbstract
Aging-related diseases represent a complex and heterogeneous burden that spans metabolic, cardiovascular, neurodegenerative, musculoskeletal, and immune systems. Bioactive peptides have attracted attention as potentially modifiable interventions because they can influence inflammatory signaling, oxidative stress, proteostasis, mitochondrial function, and tissue repair. However, the discovery and recommendation of peptides for aging-related disease intervention remain constrained by fragmented evidence, limited bioavailability data, heterogeneous assay protocols, and weak integration between computational predictions and clinical translation. This paper examines deep learning-based recommendation of bioactive peptides from a system-level perspective rather than a narrow model-centered perspective. It analyzes the data infrastructure required for peptide representation, the architectural trade-offs among sequence models, structure-aware networks, graph representations, and multimodal learning, and the recommendation pipeline that connects candidate generation, ranking, uncertainty estimation, and human decision support. The discussion emphasizes deployment realities, including reproducibility, technical debt, computational sustainability, edge and federated architectures, and the governance of sensitive biomedical data. It further considers fairness, robustness, explainability, and policy implications, especially when recommendations may influence preclinical research priorities or clinical trial design. The paper argues that effective peptide recommendation systems must be designed as socio-technical infrastructures that combine predictive performance with traceable evidence, regulatory alignment, equitable access, and long-term maintainability. Such systems can accelerate translational research only if they remain accountable to biological validity, clinical safety, and public trust.
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