"In one experiment, Natarajan and his colleagues used Google's Co-Scientist to look for approved drugs that could be repurposed to treat a form of blood cancer called acute myeloid leukaemia. The system identified a list of candidate drugs, from which human researchers selected five for further study. Three of these showed promise in preliminary studies on cells grown in the lab."
"Robin began by consulting AI agents trained to conduct literature reviews and used their reports to select lab experiments to test a variety of candidate drugs. Humans carried out those experiments and fed the data back to Robin, which then supplied them to an AI agent specialized in analysing data."
"Using this procedure, Robin suggested a list of molecular targets for treating dry age-related macular degeneration and identified a drug called ripasudil, which is used to treat the eye condition glaucoma, as a candidate trea"
"Each system still relies on human input at various stages, but they boast timelines that can be remarkably shorter than when the process is left to human minds and hands alone. When the systems were asked to identify existing drugs that might be repurposed for different conditions, they arrived at plausible answers in a matter of hours."
Two AI systems use teams of agents to accelerate laboratory workflows by generating hypotheses, proposing experiments, and analyzing results. Co-Scientist was used to identify approved drugs that could be repurposed for acute myeloid leukaemia, producing candidate lists that humans narrowed to five for further study, with three showing promise in preliminary cell studies. Robin, developed by a non-profit AI lab, used agents to perform literature reviews, select experiments, and then receive human experimental data for specialized analysis. Robin suggested molecular targets for dry age-related macular degeneration and identified ripasudil, a glaucoma drug, as a candidate treatment. Human input remains part of the loop at multiple stages.
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