From Intention To Implementation: Automating Biomedical Research via LLMs

TL;DR

BioResearcher is an innovative end-to-end AI system that automates the entire biomedical research process, significantly reducing workload and accelerating discoveries by integrating multi-agent architecture, hierarchical learning, and quality control.

Contribution

This paper introduces BioResearcher, the first comprehensive system that automates the full biomedical research workflow using modular multi-agent design and novel evaluation metrics.

Findings

Achieved 63.07% success rate across eight research objectives

Generated protocols outperform typical systems by 22% on quality metrics

Demonstrated potential to reduce researcher workload and accelerate discovery

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, on average, 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.