用大模型+知识图谱，让药物重定位更懂机制、更靠谱。
Large Language Models Meet Biomedical Knowledge Graphs for Mechanistically Grounded Therapeutic Prioritization
- 融合生物医学知识图谱与大模型推理，实现机制可解释的药物优先排序。
- 在12种癌症中，得分高的药物关联改善生存率的基因表达特征。
- 适合需要机制可信度的药物研发人员，尤其关注精准医疗场景。
药物重定位常被当作候选发现任务，但现有方法难以区分生物学合理候选与历史关联强的药物。本文提出DrugKLM，一种结合生物医学知识图谱结构与大语言模型机制推理的混合框架，实现机制可支撑的治疗优先级排序。在基准数据集上，DrugKLM优于仅用知识图谱或仅用大模型的基线方法，包括TxGNN。除提升召回率外，DrugKLM的置信度评分与分子表型功能一致：高分药物对应12个TCGA癌症中与生存改善相关的转录特征。该评分体系更关注生物扰动信号而非历史适应症模式。五种癌症的专家校验显示，DrugKLM能有效识别具备连贯机制解释和疾病特异性临床背景的候选药物。结果表明，DrugKLM是一个整合异构生物医学数据、生成机制可解释且临床相关的治疗假说的证据融合框架。
原文摘要 · Abstract (English)
Drug repurposing is often framed as a candidate identification task, but existing approaches provide limited guidance for distinguishing biologically plausible candidates from historically well-connected ones. Here we introduce DrugKLM, a hybrid framework that integrates biomedical knowledge graph structure with large language model-based mechanistic reasoning to enable mechanistically grounded therapeutic prioritization. Across benchmark datasets, DrugKLM outperforms knowledge graph-only and language model-only baselines, including TxGNN. Beyond improved recall, DrugKLM confidence scores exhibit functional alignment with molecular phenotypes: higher scores are associated with transcriptional signatures linked to improved survival across 12 TCGA cancers. The scoring framework preferentially captures biologically perturbational signals rather than historical indication patterns. Expert curation across five cancers further reveals systematic differences in prioritization behavior, with DrugKLM elevating candidates supported by coherent mechanistic rationale and disease-specific clinical context. Together, these results establish DrugKLM as an evidence-integrative framework that translates heterogeneous biomedical data into mechanistically interpretable and clinically grounded therapeutic hypotheses.
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