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

• Published in: BioData mining Vol. 18; no. 1; pp. 51 - 19
• Main Authors: Wang, Zhiping Paul, Li, Xi, Matsumoto, Nicholas, Venkatesan, Mythreye, Chang, Jui-Hsuan, Moran, Jay, Choi, Hyunjun, Li, Binglan, Meng, Yufei, Hernandez, Miguel E., Moore, Jason H.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 05.08.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Advances in Data Mining for Biomedical Informatics and Healthcare; Algorithms; Alzheimer's disease; Artificial intelligence; Bioinformatics; Biomedical and Life Sciences; Chatbots; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data Mining and Knowledge Discovery; Drug development; Drug discovery; Drug therapy; Graphs; Knowledge bases (artificial intelligence); Knowledge representation; Large language models; Learning algorithms; Life Sciences; Machine learning; Methods; Neurodegenerative diseases; Patient outcomes; Performance enhancement; Performance evaluation; United States
• ISSN: 1756-0381; 1756-0381
• DOI: 10.1186/s13040-025-00466-5

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, an LLM-based chatbot built on AlzKB; DR4, our ESCARGOT framework combining GoT-enhanced LLMs with AlzKB; and DR5, a general reasoning-driven LLM approach. Results showed that AlzKB significantly improved DR outcomes. ESCARGOT further enhanced performance while reducing the need for coding or advanced expertise in knowledge graph analysis. Because the architecture of AlzKB is easily adaptable to other diseases and ESCARGOT can integrate with various knowledge graph platforms, this framework offers a broadly applicable, innovative tool for accelerating drug discovery through repurposing.