Empowering Biomedical Discovery with AI Agents

TL;DR

This paper discusses the development of AI agents that collaborate with humans in biomedical research, combining AI capabilities with human expertise to accelerate discovery and innovation.

Contribution

It introduces a framework for AI agents in biomedicine that integrate models, tools, and experimental platforms to enhance discovery processes.

Findings

AI agents can plan and execute biomedical discovery workflows.

They utilize large language and generative models for continual learning.

AI agents can assist in virtual simulations and therapy development.

Abstract

We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.