Researchers at Cambridge are using an AI tool called Co-Scientist to identify molecular mechanisms that cause severe disease when pathogens jump from animals to humans. Professor Clare Bryant's lab tested the tool on flu research and found it could narrow experimental focus from candidate proteins down to specific amino acids in six months instead of the typical two to three years. If the AI-guided approach identifies the correct targets, this could accelerate the discovery of intervention points to prevent zoonotic disease severity in humans.
Calico Life Sciences used an AI tool called Co-Scientist to analyze aging research literature and generate testable hypotheses about how cells protect themselves against stress. The team applied this approach to study the integrated stress response, a cellular mechanism that changes with age, and designed experiments based on AI-generated hypotheses that produced novel findings. The results will be published and may inform understanding of how cellular stress response contributes to aging-related diseases.
A biomedical team at the University of Edinburgh used an AI system called Co-Scientist to identify overlooked biological connections in metabolic dysfunction-associated steatohepatitis (MASH), a liver disease involving multiple intertwined processes. The AI synthesised evidence across liver biology and pharmacology to propose that the NLRP3 inflammasome links inflammation and metabolism in MASH, a connection that had not previously been integrated into a single explanation. This hypothesis, later experimentally verified, could enable the development of targeted combination therapies for patients who do not respond to existing single-target drugs like resmetirom.
Ritu Raman and Ryan Flynn are collaborating on ALS research by combining their separate expertise in tissue engineering and RNA biology, facilitated by an AI tool called Co-Scientist. Co-Scientist helped Raman rapidly synthesize months of fragmented ALS literature and generate testable hypotheses for her tissue models within weeks. Their combined approach now targets RNA-based mechanisms at cell surfaces as potential therapeutic avenues for ALS.
Researchers at Stanford University used an AI tool called Co-Scientist to identify existing medicines that could treat liver fibrosis, a scarring condition that kills over 1.4 million people annually. When tested against human liver cells, two of three AI-selected drug candidates blocked fibrosis progression, while the cancer drug vorinostat blocked 91% of a damage response driving liver scarring. The findings suggest that drugs altering gene activity patterns warrant clinical investigation as potential treatments for the condition.
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