用大模型增强药物重定位中的生物医学概念表示
From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing
- 利用大模型提取治疗相关文本表征，优化知识图谱嵌入
- 在阿尔茨海默病等复杂疾病上实现领先性能
- 适合医药研发、生物信息学领域研究人员使用
药物重定位在加速复杂与罕见疾病治疗发现中具有关键作用。生物医学知识图谱（KG）通过编码丰富的临床关联被广泛用于支持该任务。然而，现有方法普遍忽视真实实验室中的常识性生物医学概念知识，例如某些药物与特定治疗在机制上存在根本不相容性。为此，我们提出 LLaDR 框架，一种大语言模型辅助的药物重定位方法，旨在改进知识图谱中生物医学概念的表征。具体而言，我们从大语言模型中提取语义丰富的治疗相关实体文本表征，并用于微调知识图谱嵌入（KGE）模型。通过将治疗相关知识注入 KGE，LLaDR 显著提升生物医学概念的表征能力，增强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 在多种场景下均达到当前最优性能，阿尔茨海默病的案例研究进一步验证其鲁棒性与有效性。代码已公开于 https://github.com/xiaomingaaa/LLaDR。
原文摘要 · Abstract (English)
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.
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