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.

翻译：药物重定位在加速治疗发现方面发挥着关键作用，尤其对于复杂和罕见疾病。编码了丰富临床关联的生物医学知识图谱已被广泛采用以支持此任务。然而，现有方法在很大程度上忽视了现实世界实验室中的常识性生物医学概念知识，例如指示某些药物与特定治疗根本不相容的机制先验。为弥补这一不足，我们提出了LLaDR——一种用于药物重定位的大语言模型辅助框架，该框架改进了知识图谱内生物医学概念的表示。具体而言，我们从大语言模型中提取生物医学实体的语义增强的治疗相关文本表示，并利用其微调知识图谱嵌入模型。通过将治疗相关知识注入知识图谱嵌入，LLaDR显著改善了生物医学概念的表示，增强了对研究不足或复杂适应症的语义理解。基于基准测试的实验表明，LLaDR在不同场景下均实现了最先进的性能，针对阿尔茨海默病的案例研究进一步证实了其鲁棒性和有效性。代码发布于 https://github.com/xiaomingaaa/LLaDR。