From Intention To Implementation: Automating Biomedical Research via LLMs

• URL: http://arxiv.org/abs/2412.09429v2
• Date: Sun, 22 Dec 2024 05:34:46 GMT
• Title: From Intention To Implementation: Automating Biomedical Research via LLMs
• Authors: Yi Luo, Linghang Shi, Yihao Li, Aobo Zhuang, Yeyun Gong, Ling Liu, Chen Lin,
• Abstract summary: This paper introduces BioResearcher, the first end-to-end automated system designed to streamline the entire biomedical research process. By decomposing complex tasks into logically related sub-tasks, BioResearcher effectively addresses the challenges of multidisciplinary requirements and logical complexity. BioResearcher successfully achieves an average execution success rate of 63.07% across eight previously unmet research objectives.
• Score: 30.32209981487504
• License:
• Abstract: 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.

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