用强化推理提升大模型，精准提取复杂药物组合。
RexDrug: Reliable Multi-Drug Combination Extraction through Reasoning-Enhanced LLMs
- 分两阶段训练：自动生成专家级推理路径，再用多维奖励优化
- 在DrugComb数据集上超越现有方法，二元与多元任务均表现优异
- 适合医药研究、精准医疗领域，可处理复杂治疗方案提取
从大规模生物医学文献中自动提取药物组合对推进精准医疗和药理学研究至关重要。然而，现有关系抽取方法主要关注二元相互作用，难以建模变量长度的多元药物组合，需考虑复杂的相容性逻辑和分散证据。为此，我们提出RexDrug，一种基于大语言模型的端到端推理增强型关系抽取框架，用于多元药物组合提取。RexDrug采用两阶段训练策略：首先利用多智能体协作机制自动生成高质量专家式推理轨迹，用于监督微调；其次应用针对药物组合抽取设计的多维奖励函数进行强化学习，进一步提升推理质量与抽取准确率。在DrugComb数据集上的大量实验表明，RexDrug在多元提取任务中持续优于现有先进基线。在DDI13语料库上的额外评估也证实其在二元药物相互作用任务中的泛化能力。人工专家评估与自动推理指标显示，RexDrug能生成连贯的医学推理并准确识别复杂治疗方案。这些结果确立了RexDrug作为从非结构化文本中提取复杂生物医学关系的可扩展、可靠解决方案。代码与数据已公开于https://github.com/DUTIR-BioNLP/RexDrug。
原文摘要 · Abstract (English)
Automated Drug Combination Extraction (DCE) from large-scale biomedical literature is crucial for advancing precision medicine and pharmacological research. However, existing relation extraction methods primarily focus on binary interactions and struggle to model variable-length n-ary drug combinations, where complex compatibility logic and distributed evidence need to be considered. To address these limitations, we propose RexDrug, an end-to-end reasoning-enhanced relation extraction framework for n-ary drug combination extraction based on large language models. RexDrug adopts a two-stage training strategy. First, a multi-agent collaborative mechanism is utilized to automatically generate high-quality expert-like reasoning traces for supervised fine-tuning. Second, reinforcement learning with a multi-dimensional reward function specifically tailored for DCE is applied to further refine reasoning quality and extraction accuracy. Extensive experiments on the DrugComb dataset show that RexDrug consistently outperforms state-of-the-art baselines for n-ary extraction. Additional evaluation on the DDI13 corpus confirms its generalizability to binary drugdrug interaction tasks. Human expert assessment and automatic reasoning metrics further indicates that RexDrug produces coherent medical reasoning while accurately identifying complex therapeutic regimens. These results establish RexDrug as a scalable and reliable solution for complex biomedical relation extraction from unstructured text. The source code and data are available at https://github.com/DUTIR-BioNLP/RexDrug
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