Bridging the computational-experimental gap: leveraging large language model to prioritize Alzheimer's therapeutics based on comparison of learning models.

Alzheimer's Disease1 (AD) necessitates accelerated treatment discovery, positioning drug repurposing as a vital strategy. While computational approaches such as knowledge graph reasoning and transcriptomics show promise, they often yield divergent results, complicating the selection of candidates for experimental follow-up2,3. To bridge the gap between computational prediction and in vivo validation, we propose an advanced framework leveraging large language models (LLMs). We systematically evaluated three state-of-the-art computational methods (TxGNN, CompGCN, and regularized logistic regression (RLR)) to generate a unified list of 90 candidates. An LLM-based agent was then used to automate evidence synthesis from biomedical literature, mimicking expert curation to efficiently refine the list using transparent selection criteria. Validated against real-world AD patient data, clinical trial registries, and pharmacological reviews, our framework demonstrated high robustness and clinical relevance. By integrating computational predictions with scalable evidence synthesis, this approach enhances the efficiency and consistency of candidate prioritization. Ultimately, this versatile framework offers a scalable pathway to accelerate the translation of repurposed drugs for AD and other complex diseases.