arXiv 2605.181442026-05-18cs.AI

基于证据的前沿映射与代理假设生成在纳米医学中

Evidence-Grounded Frontier Mapping and Agentic Hypothesis Generation in Nanomedicine

ARIA Lab, Signal Processing Systems, Department of Electrical Engineering, Eindhoven University of Technology（ARIA实验室，信号处理系统，电气工程系，埃因霍温理工大学）；Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology（化学生物学实验室，生物医学工程系，埃因霍温理工大学）；The Louis Family Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology（定向药物输送与个性化医学技术实验室，化学工程系，技术离子-以色列理工学院）；Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging (ExMI), RWTH Aachen University Hospital（纳米医学与诊疗学系，实验分子成像研究所（ExMI），亚琛工业大学医院）；Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center（内科学系和Radboud感染疾病中心（RCI），Radboud大学医学中心）

Christiaan G. A. Viviers, Koen de Bruin, Mirre M. Trines, Ayla M. Hokke, Roy van der Meel, Avi Schroeder, Twan Lammers, Willem J. M. Mulder, Fons van der Sommen

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AI总结：

该研究提出了一种结合文章嵌入、相似性图分析、稀疏前沿提取、结构化证据包检索和审计过的大型语言模型（LLM）工作流的系统pArticleMap，用于支持纳米医学研究方向的选择和假设生成，通过生成和评分基于引用的假设，实现了证据导向的研究辅助。

摘要：

纳米医学研究涵盖了递送化学、免疫学、成像、生物材料和疾病特定的转化科学，但其概念设计空间仍然在大量异质文献中碎片化。截至目前，人工智能在纳米医学中的应用主要集中在性质预测和配方优化，对研究方向选择层面的证据导向发现支持关注较少。我们引入了pArticleMap，一个结合文章嵌入、相似性图分析、稀疏前沿提取、结构化证据包检索和审计过的大型语言模型（LLM）工作流的文献映射和研究假设生成系统。该系统不同于预测未来概念共现，而是针对低密度文章级桥接区域和聚类界面，然后在代理设置中利用大型语言模型生成和评分基于引用的假设。我们通过回顾性实现基准（在历史截止点下生成后续文献）和盲人类读者评估层，在提示条件下的纳米医学任务中评估该系统。在4个选定的回顾性包中，pArticleMap在基准协议下生成了想法并选择了任务保留的假设（获胜想法）。对于任务级保留的假设，获得了一个汇总的黄金回收率10.8%，召回@10为15.9%，未来邻域率61.0%，表明该系统经常能够达到正确的前瞻性邻域（论文想法），即使没有精确的论文级回收。人类-代理协议总体上是中等的，表明内部评分是有用的支持信号，但不能替代专家判断。这些结果将pArticleMap定位为一种保守的、基于证据的研究助手，用于纳米医学。

英文摘要：

Nanomedicine research spans delivery chemistry, immunology, imaging, biomaterials, and disease-specific translational science, yet its conceptual design space remains fragmented across a large and heterogeneous literature. To date, artificial intelligence in nanomedicine has focused primarily on property prediction and formulation optimization, with much less attention to evidence-grounded discovery support at the level of research direction selection. We introduce pArticleMap, a literature-mapping and research-hypothesis-generation system that combines article embeddings, similarity-graph analysis, sparse frontier extraction, structured evidence-pack retrieval, and an audited large-language-model (LLM) workflow for grounded ideation. Rather than forecasting future concept co-occurrence, pArticleMap targets low-density article-level bridge regions and cluster interfaces, then generates and scores citation-grounded hypotheses with large language models in an agentic setup. We evaluate the system with a retrospective realization benchmark (generate later literature under a historical cutoff) and a blinded human reader assessment layer across cue-conditioned nanomedicine tasks. Across 4 selected retrospective bundles, pArticleMap generated ideas and selected task-retained hypotheses (winner ideas) under the benchmark protocol. For task-level retained hypotheses, a pooled gold recovery rate of 10.8% was obtained, with a recall@10 of 15.9% and a future-neighborhood rate of 61.0%, indicating that the system often reached the correct forward-looking neighborhood (paper ideas) even without exact paper-level recovery. Human-agent agreement is modest overall, indicating that internal scoring is useful as a support signal but does not replace expert judgment. These results position pArticleMap as a conservative, evidence-grounded research assistant for nanomedicine.

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