Conventional biomedical research is increasingly labor-intensive due to the exponential growth of scientific literature and datasets. Artificial intelligence (AI), particularly Large Language Models (LLMs), has the potential to revolutionize this process by automating various steps. Still, significant challenges remain, including the need for multidisciplinary expertise, logicality of experimental design, and performance measurements. This paper introduces BioResearcher, the first end-to-end automated system designed to streamline the entire biomedical research process involving dry lab experiments. BioResearcher employs a modular multi-agent architecture, integrating specialized agents for search, literature processing, experimental design, and programming. By decomposing complex tasks into logically related sub-tasks and utilizing a hierarchical learning approach, BioResearcher effectively addresses the challenges of multidisciplinary requirements and logical complexity. Furthermore, BioResearcher incorporates an LLM-based reviewer for in-process quality control and introduces novel evaluation metrics to assess the quality and automation of experimental protocols. BioResearcher successfully achieves an average execution success rate of 63.07% across eight previously unmet research objectives. The generated protocols averagely outperform typical agent systems by 22.0% on five quality metrics. The system demonstrates significant potential to reduce researchers' workloads and accelerate biomedical discoveries, paving the way for future innovations in automated research systems.

翻译：传统的生物医学研究因科学文献和数据集的指数级增长而日益劳动密集型。人工智能，特别是大语言模型，有潜力通过自动化多个步骤来革新这一过程。然而，重大挑战依然存在，包括对多学科专业知识的需求、实验设计的逻辑性以及性能评估。本文介绍了BioResearcher，这是首个旨在简化涉及干实验的完整生物医学研究流程的端到端自动化系统。BioResearcher采用模块化多智能体架构，集成了专门用于搜索、文献处理、实验设计和编程的智能体。通过将复杂任务分解为逻辑相关的子任务，并利用分层学习方法，BioResearcher有效应对了多学科需求和逻辑复杂性的挑战。此外，BioResearcher整合了一个基于LLM的评审员进行过程质量控制，并引入了新颖的评估指标来评估实验方案的质量和自动化程度。BioResearcher在八个先前未实现的研究目标上，平均执行成功率达到了63.07%。所生成的方案在五项质量指标上平均优于典型智能体系统22.0%。该系统展现出显著减轻研究人员工作负担和加速生物医学发现的潜力，为自动化研究系统的未来创新铺平了道路。