AI助手帮科学家从文献中找出癌症药物组合的可行假设。
From Literature to Hypotheses: An AI Co-Scientist System for Biomarker-Guided Drug Combination Hypothesis Generation
- 构建知识图谱融合文献与数据库，支持推理生成假设。
- 可交互验证并排序候选组合，每条都有可追溯证据。
- 适合肿瘤转化研究者做探索性假设生成与决策辅助。
生物医学文献和数据库的快速增长使研究人员难以系统地将生物标志物机制与可操作的药物组合假设联系起来。我们提出 AI Co-Scientist（CoDHy），一个交互式、人机协同的系统，用于癌症研究中的生物标志物引导型药物组合假设生成。CoDHy 将结构化生物医学数据库与非结构化文献证据整合到特定任务的知识图谱中，作为基于图的推理与假设构建的基础。系统结合知识图谱嵌入与基于代理的推理，生成、验证并排序候选药物组合，同时明确将每个假设建立在可检索的证据之上。通过网页界面，用户可配置科学背景、检查中间结果，并迭代优化假设，实现透明且由研究者主导的探索过程，而非自动化决策。我们展示了 CoDHy 在转化肿瘤学中作为探索性假设生成与决策支持系统的应用，重点阐述其设计、交互流程及实际使用案例。
原文摘要 · Abstract (English)
The rapid growth of biomedical literature and curated databases has made it increasingly difficult for researchers to systematically connect biomarker mechanisms to actionable drug combination hypotheses. We present AI Co-Scientist (CoDHy), an interactive, human-in-the-loop system for biomarker-guided drug combination hypothesis generation in cancer research. CoDHy integrates structured biomedical databases and unstructured literature evidence into a task-specific knowledge graph, which serves as the basis for graph-based reasoning and hypothesis construction. The system combines knowledge graph embeddings with agent-based reasoning to generate, validate, and rank candidate drug combinations, while explicitly grounding each hypothesis in retrievable evidence. Through a web-based interface, users can configure the scientific context, inspect intermediate results, and iteratively refine hypotheses, enabling transparent and researcher-steerable exploration rather than automated decision-making. We demonstrate CoDHy as a system for exploratory hypothesis generation and decision support in translational oncology, highlighting its design, interaction workflow, and practical use cases.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。