Abstract
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 this https URL
Abstract (translated)
从大规模生物医学文献中自动提取药物组合（DCE）对于推进精准医疗和药理学研究至关重要。然而，现有的关系抽取方法主要关注二元相互作用，并且难以建模长度可变的n-元药物组合，这些组合需要考虑复杂的兼容逻辑及分布证据。为了克服这些问题，我们提出了RexDrug——一个基于大规模语言模型、用于n-元药物组合提取的端到端推理增强的关系抽取框架。RexDrug采用了一种两阶段训练策略：首先利用多代理协同机制自动生成高质量的专业级推理痕迹，以进行监督微调；其次应用具有多维奖励函数的强化学习来进一步优化推理质量和抽取准确性，该奖励函数特别针对DCE设计。在DrugComb数据集上的广泛实验表明，RexDrug在n-元提取方面始终优于最新的基准方法。对DDI13语料库进行的额外评估证实了其在二元药物相互作用任务中的泛化能力。人类专家评价及自动推理指标进一步表明，RexDrug能够生成连贯的医学推理，并准确识别复杂的治疗方案。这些结果使RexDrug成为从非结构化文本中提取复杂生物医学关系的一种可扩展且可靠的解决方案。源代码和数据可在[提供的URL]获取。
URL
https://arxiv.org/abs/2603.08166
