English(EN) DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search

新的DrugMCTS框架利用LLM和多智能体系统增强药物再利用

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了DrugMCTS，一个旨在利用大型语言模型改进药物再利用的新框架。该系统集成了检索增强生成（RAG）、多智能体协作和蒙特卡洛树搜索，以克服传统微调或RAG方法的局限性。DrugMCTS利用五个专门的智能体进行分子和蛋白质信息分析，实现了结构化推理，并在DrugBank和KIBA等数据集上取得了比现有LLM和深度学习模型更高的召回率和鲁棒性。 AI

影响该框架通过提高LLM在科学领域的效率和准确性，有可能加速药物发现。

排序理由该集群描述了一篇研究论文，其中详细介绍了一个用于药物再利用的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zerui Yang, Yuwei Wan, Siyu Yan, Yudai Matsuda, Tong Xie, Linqi Song · 2026-10-07 04:00

DrugMCTS：一个结合了多智能体、RAG和蒙特卡洛树搜索的药物再利用框架

arXiv:2507.07426v4 Announce Type: replace Abstract: 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 acq…

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DrugMCTS：一个结合了多智能体、RAG和蒙特卡洛树搜索的药物再利用框架

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