Large Language Model-Based Recommendation of Peptide Candidates for Age-Related Disease Research
Keywords:
large language models; peptide therapeutics; aging-related diseases; recommendation systems; biomedical knowledge infrastructure; governance; robustness; deploymentAbstract
Age-related diseases represent a complex and expanding burden on health systems, and peptide-based therapeutic candidates have emerged as a promising but difficult-to-navigate modality because of their structural diversity, uncertain bioavailability, immunogenicity, and context-dependent mechanisms. This paper examines the system-level design of large language model-based recommendation platforms for peptide candidates in age-related disease research. Rather than treating recommendation as a single generative act, the paper frames it as a socio-technical infrastructure problem involving knowledge integration, retrieval, validation, human oversight, deployment economics, and governance. The analysis considers how foundation models, protein structure prediction, molecular representation learning, and biomedical literature synthesis can be combined within modular architectures that support candidate triage without overstating predictive certainty. It discusses structural trade-offs among centralization and federation, latency and depth, model generality and domain specificity, and openness and intellectual property protection. The paper also addresses robustness under distribution shift, fairness in population and evidence representation, sustainability of compute-intensive workflows, and policy implications for regulatory review. A central argument is that recommendation quality depends less on model scale alone than on the surrounding evidence infrastructure, provenance controls, auditability, and appeal mechanisms that allow researchers to contest or refine outputs. Cross-domain comparisons with clinical decision support, drug repurposing, and materials discovery clarify both the promise and the limits of language model-driven peptide recommendation. The conclusion emphasizes that responsible deployment requires validated benchmarks, transparent model documentation, federated data strategies, and governance arrangements that align discovery acceleration with scientific accountability.
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