From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing
This work improves drug repurposing for complex and rare diseases by incorporating real-world lab knowledge, though it is incremental as it builds on existing knowledge graph embedding methods.
The paper tackles the problem of drug repurposing by addressing the oversight of common-sense biomedical knowledge in existing methods, proposing LLaDR, a framework that uses large language models to enhance biomedical concept representations in knowledge graphs, resulting in state-of-the-art performance on benchmarks.
Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted to support this task. However, existing methods largely overlook common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments. To address this gap, we propose LLaDR, a Large Language Model-assisted framework for Drug Repurposing, which improves the representation of biomedical concepts within KGs. Specifically, we extract semantically enriched treatment-related textual representations of biomedical entities from large language models (LLMs) and use them to fine-tune knowledge graph embedding (KGE) models. By injecting treatment-relevant knowledge into KGE, LLaDR largely improves the representation of biomedical concepts, enhancing semantic understanding of under-studied or complex indications. Experiments based on benchmarks demonstrate that LLaDR achieves state-of-the-art performance across different scenarios, with case studies on Alzheimer's disease further confirming its robustness and effectiveness. Code is available at https://github.com/xiaomingaaa/LLaDR.