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-up^2,3. To bridge the gap between computational prediction and in vivo validation, we propose an advanced framework leveraging large language models (LLMs).