用多智能体与树搜索提升药物重定位的精准度
DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search
- 五智能体协同检索分析分子蛋白数据，实现结构化推理
- 在DrugBank和KIBA数据集上召回率显著优于基线模型
- 适合需要高精度药物发现的研究者使用
大型语言模型在药物重定位等科学领域展现出巨大潜力，但其推理能力受限于预训练知识范围。传统方法如微调或检索增强生成，或计算开销大，或难以充分利用结构化科学数据。为此，我们提出DrugMCTS框架，融合RAG、多智能体协作与蒙特卡洛树搜索，通过五个专精智能体负责分子与蛋白信息的检索与分析，实现结构化、迭代式推理。在DrugBank和KIBA数据集上的实验表明，DrugMCTS在召回率和鲁棒性方面均显著优于通用大模型及深度学习基线。结果凸显了结构化推理、智能体协作与反馈驱动搜索机制在推进大模型药物重定位应用中的关键作用。
原文摘要 · Abstract (English)
Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their effectiveness remains constrained when reasoning extends beyond the knowledge acquired during pretraining. Conventional approaches, such as fine-tuning or retrieval-augmented generation, face limitations in either imposing high computational overhead or failing to fully exploit structured scientific data. To overcome these challenges, we propose DrugMCTS, a novel framework that synergistically integrates RAG, multi-agent collaboration, and Monte Carlo Tree Search for drug repositioning. The framework employs five specialized agents tasked with retrieving and analyzing molecular and protein information, thereby enabling structured and iterative reasoning. Extensive experiments on the DrugBank and KIBA datasets demonstrate that DrugMCTS achieves substantially higher recall and robustness compared to both general-purpose LLMs and deep learning baselines. Our results highlight the importance of structured reasoning, agent-based collaboration, and feedback-driven search mechanisms in advancing LLM applications for drug repositioning.
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