A multi-skilled biomedical AI agent can automate biomedical research tasks, driving forward basic research into practical application.
The AI-powered “co-scientist” Biomni autonomously executes diverse research tasks, working across complex tasks across fields as diverse as genomics, immunology, pharmacology, and clinical medicine.
The large-language model (LLM), outlined in Science, mines through biomedical literature interpreting requests, both composing and executing multi-step workflows.
It can formulate hypotheses, performs complex bioinformatics analyses, and design rigorous experimental protocols, with tests showing comparable accuracy to experts while taking a fraction of the speed.
“Biomni is able to understand a simple question like, ‘Why are these patients responding differently to the drug?’” explained research Kexin Huang, who was studying for his PhD at Stanford University at the time of the research.
“Then it digs in, doing a lot of the scientific legwork.”
Initially, the researchers constructed a unified and comprehensive biomedical action space by systematically analyzing 2500 biomedical research papers spanning 25 distinct subfields, curated from literature repositories.
From this foundation, they developed an LLM-powered action discovery agent capable of reading papers and extracting key tasks, tools, and databases essential to driving biomedical discoveries.
These elements are then chosen and developed into Biomni-E1, the foundational environment that defines the biomedical action space for agentic interaction and includes 150 specialized biomedical tools, 105 software packages, and 59 databases.
The team then designed Biomni-A1, a general-purpose agent architecture capable of flexibly executing a broad spectrum of biomedical tasks by using tools and datasets provided by Biomni E1.
After a user query is entered, the agent uses a retrieval system to identify the most relevant tools, databases, and software needed.
It then applies LLM-based reasoning and domain expertise to generate a detailed, step-by-step plan. Each step is expressed through executable code, enabling precise and flexible compositions of biomedical actions—an essential 10 feature given the domain’s reliance on highly specialized tools and data resources.
This integrated system allows Biomni to efficiently generate solutions for challenging, large-scale biomedical problems, but also to generalize to tasks across previously unseen areas of biomedical research.
In this way it removes the laborious work from biomedical science, allowing researchers to focus on creating hypotheses and innovative experiments and collaborating across disciplines.
“The hurdle in biomedical science is not intelligence or ideas; it is mechanics,” said researcher Jure Leskovec, PhD, also at Stanford.
“It’s this laborious stuff that slows innovation. Biomni can do this work in minutes.”
The team tested Biomni’s practical capabilities through five case studies: analyzing wearable sensor data; performing comprehensive bioinformatics analyses on massive raw datasets such as single-cell RNA-seq and ATAC-seq data; designing laboratory protocols to assist wet-lab researchers; optimizing a protein sequence for better thermostability; and orchestrating robotics wet-lab instruments.
In one test, more than 450 files of real-world continuous glucose monitoring, food intake, and physical activity data from a single person was uploaded and Biomni asked to find interesting and plausible hypotheses.
The researchers asked a simple question: “Analyze this data, find interesting and plausible hypotheses.” In just 40 minutes, the AI-agent identified patterns relating food intake and body temperature that would have taken an estimated 60 or more hours for a human to complete.
“With Biomni, we introduce a scalable, general-purpose biomedical AI agent, pointing toward a future in which AI agents work alongside human researchers to accelerate biomedical discovery from basic research to translation,” the authors concluded.
A prototype of the AI agent is already being used by more than 10,000 labs in academia and industry, making it the most widely used AI co-scientist system in biomedicine.
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