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

翻译：从大规模生物医学文献中自动提取药物组合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，难以对可变长度的n元药物组合进行建模，其中需要考虑复杂的相容性逻辑和分散的证据。为解决这些局限性，我们提出了RexDrug，一个基于大语言模型的、用于n元药物组合提取的端到端推理增强关系抽取框架。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、类似专家的推理轨迹用于监督微调。其次，应用专门为药物组合提取任务定制的、具有多维奖励函数的强化学习，以进一步提升推理质量和抽取准确性。在DrugComb数据集上的大量实验表明，RexDrug在n元抽取任务上持续优于最先进的基线模型。在DDI13语料库上的额外评估证实了其在二元药物-药物相互作用任务上的泛化能力。人类专家评估和自动推理指标进一步表明，RexDrug在准确识别复杂治疗方案的同时，能产生连贯的医学推理。这些结果确立了RexDrug作为一种可扩展且可靠的解决方案，用于从非结构化文本中提取复杂的生物医学关系。源代码和数据可在 https://github.com/DUTIR-BioNLP/RexDrug 获取。