Contrastive Learning for Bioactive Peptide Representation and Personalized Health Recommendation
Keywords:
contrastive learning, bioactive peptides, personalized health, recommender systems, protein representation, data governance, fairness, clinical translation, federated learning, sustainabilityAbstract
Bioactive peptides occupy a distinctive position in nutrition, pharmacology, and personalized health because their effects depend on sequence, structure, bioavailability, host physiology, and context of consumption. Computational systems that recommend peptides or peptide-rich interventions must therefore represent molecules, individuals, and environments in ways that support robust generalization rather than narrow pattern matching. This paper examines contrastive learning as a system-level strategy for bioactive peptide representation and personalized health recommendation. Rather than treating contrastive learning merely as an algorithmic technique, the discussion frames it as an infrastructural choice that shapes data collection, model governance, deployment, fairness, and clinical translation. The paper first reviews related work in self-supervised representation learning, protein language modeling, and health recommender systems. It then proposes a layered architecture that combines molecular encoders, individual context encoders, multimodal alignment, and human-in-the-loop recommendation services. Subsequent sections analyze governance challenges, including consent, provenance, privacy, federated learning, bias, and transparency. The paper also considers robustness under distribution shift, sustainability of large-scale training, edge deployment, and policy implications for regulated health products. The central argument is that contrastive learning can improve peptide representation by aligning heterogeneous views of bioactivity and individual response, but its value depends on governance mechanisms that make data, models, and recommendations accountable. A systems perspective is necessary because peptide recommendation is not only a prediction problem but also a sociotechnical infrastructure problem involving clinical validity, user trust, and equitable access.
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