Researchers have created a closed-loop AI laboratory capable of conducting research on brewer’s yeast. It can identify biological questions, recommend experiments, and evaluate experimental outcomes. The image shows the robot scientist Eve at Chalmers University of Technology in Sweden, which was specifically designed for drug discovery and which has now been updated with large language models and automated reasoning.
Image Credit: NIH Image Gallery/Chalmers University of Technology
Scientific Frontline: Extended "At a Glance" Summary: Autonomous AI Scientists
The Core Concept: A closed-loop artificial intelligence laboratory system capable of autonomously generating scientific hypotheses, designing and executing experiments, and analyzing the resulting biological data.
Key Distinction/Mechanism: Unlike conventional artificial intelligence tools that serve merely as passive data analyzers or decision support systems, this agentic architecture actively generates new scientific knowledge and iteratively refines its understanding with minimal human intervention.
Origin/History: Developed by researchers at Chalmers University of Technology and published in the Journal of the Royal Society Interface in late 2026, the system builds upon the pioneering legacy of earlier robot scientists, "Adam" and "Eve," which were initially engineered for basic knowledge generation and drug discovery.
Major Frameworks/Components:
- Large language models (LLMs) used to process and synthesize extensive scientific literature.
- Automated reasoning algorithms programmed to evaluate biological questions and design valid, testable experiments.
- Laboratory automation hardware engineered to physically execute experiments on biological subjects, such as the brewer's yeast, Saccharomyces cerevisiae.
- Integrated knowledge databases encompassing genomic mapping, metabolic pathways, and historical experimental outcomes.
Branch of Science: Systems Biology, Artificial Intelligence, Computational Biology, and Biotechnology.
Future Application: Accelerating high-throughput research in pharmacology, medicine, and biotechnology, which will ultimately allow human scientists to shift their focus away from routine experimental testing toward defining broad research priorities and maintaining ethical oversight.
Why It Matters: Modern biological systems and datasets present too much information for a human to analyze manually; autonomous artificial intelligence systems resolve this fundamental bottleneck, optimizing laboratory resources and vastly accelerating the pace of complex scientific discovery.
Researchers at Chalmers University of Technology in Sweden have developed an AI scientist capable of generating scientific hypotheses, designing experiments, and interpreting results. Making new biological discoveries with minimal human intervention is an important advance in self-driving laboratories.
By combining advances in large language models, automated reasoning, and laboratory automation, the researchers created a closed-loop AI laboratory capable of conducting research on brewer’s yeast, Saccharomyces cerevisiae. As the basis of the research, the AI was provided with scientific knowledge, including the yeast’s genome, metabolism, and previous studies.
“It is too much information for a human to analyze, but our AI scientist could identify promising biological questions, recommend experiments to test them, evaluate experimental outcomes, and iteratively refine its understanding based on new evidence. Rather than serving solely as decision-support tools, the AI scientist actively generates new scientific knowledge,” says Ievgeniia Tiukova, postdoctoral researcher at the Department of Life Sciences at Chalmers University of Technology and one of the authors of the new study.
Integrating the thinking power of AI with an experimental capability is unusual and cutting-edge within a rapidly expanding area where AI scientists autonomously perform extensive research. Tiukova compares the development to that of self-driving cars, where AI and machine learning are also used to process information, draw conclusions, and take action.
According to Ross King, professor at the Department of Computer Science and Engineering at Chalmers and the University of Gothenburg and the study’s senior author, autonomous laboratories will revolutionize research by systematically investigating biological systems much faster than is possible today.
“AI scientists will collaborate with human scientists to accelerate discoveries across biology, medicine, and biotechnology. Such AI systems have the potential to reduce the time required to explore complex scientific questions and optimize the use of laboratory resources,” says Professor King.
Both authors emphasize that autonomous AI will, for now, augment rather than replace scientists by increasingly undertaking the routine cycles of hypothesis generation and experimental testing.
“Human scientists remain essential for defining research priorities, interpreting broader scientific significance, and ensuring ethical oversight. Future generations of autonomous discovery systems will become increasingly capable of collaborating with human scientists, becoming valuable partners in addressing some of the most challenging questions in biology and medicine,” says Professor King.
About AI scientists
Professor Ross King was the first to develop the concept of a general-purpose robot scientist. His first robot scientist, Adam, was designed to autonomously carry out scientific experiments and generate new knowledge. He later developed a second robot scientist, Eve, which was specifically designed for drug discovery.
The underlying concept of using robotic systems to automate and accelerate scientific discovery has since been extended to other areas of research, including chemistry and other specialized scientific tasks.
Funding: The study has received funding from the Wallenberg AI, Autonomous Systems and Software Program (WASP), the UK Engineering and Physical Sciences Research Council, Chalmers AI Research Centre (CHAIR), and the Swedish Research Council for Sustainable Development, Formas.
Published in journal: Journal of the Royal Society Interface
Title: Agentic AI integrated with scientific knowledge: laboratory validation in systems biology
Authors: Daniel Brunnsåker, Alexander Howard Gower, Prajakta Naval, Erik Yuusuke Bjurström, Filip Kronström, Ievgeniia Tiukova, and Ross King
Source/Credit: Chalmers University of Technology
Edited by: Scientific Frontline
Reference Number: bio093026_01
