Evidence-Grounded Frontier Mapping and Agentic Hypothesis Generation in Nanomedicine
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866916022815031296 |
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| author | Viviers, Christiaan G. A. de Bruin, Koen Trines, Mirre M. Hokke, Ayla M. van der Meel, Roy Schroeder, Avi Lammers, Twan Mulder, Willem J. M. van der Sommen, Fons |
| author_facet | Viviers, Christiaan G. A. de Bruin, Koen Trines, Mirre M. Hokke, Ayla M. van der Meel, Roy Schroeder, Avi Lammers, Twan Mulder, Willem J. M. van der Sommen, Fons |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18144 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evidence-Grounded Frontier Mapping and Agentic Hypothesis Generation in Nanomedicine Viviers, Christiaan G. A. de Bruin, Koen Trines, Mirre M. Hokke, Ayla M. van der Meel, Roy Schroeder, Avi Lammers, Twan Mulder, Willem J. M. van der Sommen, Fons Artificial Intelligence 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. |
| title | Evidence-Grounded Frontier Mapping and Agentic Hypothesis Generation in Nanomedicine |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.18144 |