Drug repurposing for Alzheimer's disease using a graph-of-thoughts based large language model to infer drug-disease relationships in a comprehensive knowledge graph.

ABSTRACT: Drug repurposing (DR) offers a promising alternative to the high cost and low success rate of traditional drug development, especially for complex diseases like Alzheimer's disease (AD). This study addressed DR for AD from three key angles: (1) demonstrating how disease-specific knowledge graphs can improve DR performance, (2) evaluating the role of large language models (LLMs) in enhancing the usability and efficiency of these graphs, and (3) assessing whether Graph-of-Thoughts (GoT)-enhanced LLMs, when integrated with AD knowledge graphs, can outperform traditional machine learning and LLM-based approaches. We tested five distinct DR strategies (DR1-DR5) for AD: DR1, a machine learning method using TxGNN; DR2, a machine learning model leveraging the Alzheimer's KnowledgeBase (AlzKB); DR3

SUBMITTER: Wang ZP 

PROVIDER: S-EPMC12326721 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature