Accelerating discovery of liver disease mechanisms with Co-Scientist
DeepMind's Co-Scientist AI system is being used by researcher Filippo Menolascina to identify new treatment mechanisms for liver disease and explain differential drug response across patients. The application demonstrates Co-Scientist's utility in biomedical hypothesis generation and drug discovery workflows. This represents a concrete scientific use case for AI-assisted research in a clinical domain.
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Uncovering repurposed medicines to fight liver fibrosis using Co-Scientist
A Stanford geneticist used Google DeepMind's Co-Scientist AI system to identify potential drug repurposing candidates for chronic liver disease and liver fibrosis. The work represents a real-world application of AI-assisted scientific discovery in a clinical domain. Co-Scientist is DeepMind's AI research assistant designed to accelerate hypothesis generation and experimental planning for scientists.
Finding the molecular switches behind new infectious diseases
DeepMind's Co-Scientist AI tool is being used by researcher Clare Bryant to identify genetic triggers in emerging infectious diseases. The application demonstrates Co-Scientist's utility in accelerating biological discovery, specifically in understanding molecular mechanisms underlying new pathogens. This represents a concrete scientific use case for AI-assisted research in infectious disease biology.
Fast-tracking genetic leads to reverse cellular aging with Co-Scientist
DeepMind's Co-Scientist AI system was used by biologists to identify novel genetic factors capable of rejuvenating human cells, advancing cellular aging reversal research. The work demonstrates Co-Scientist's utility as a scientific discovery tool in a high-stakes biological domain. This represents a concrete application of AI-assisted hypothesis generation and experimental prioritization in longevity biology.
Opening new paths in aging research: Calico uses DeepMind Co-Scientist
Calico Life Sciences is applying DeepMind's Co-Scientist AI system to aging research, using it to synthesize dispersed scientific findings and generate novel research leads. The collaboration represents a deployment of AI-assisted scientific discovery in a longevity biology context. This is a real-world application case for Co-Scientist, DeepMind's AI system designed to accelerate scientific research workflows.
Co-Scientist: A multi-agent AI partner to accelerate research
Google DeepMind has introduced Co-Scientist, a multi-agent AI system built on Gemini designed to serve as a collaborative research partner for scientists. The system aims to accelerate scientific discovery by assisting researchers across the research workflow. The announcement comes from DeepMind's blog, indicating a formal product or capability launch rather than a research preview.
Enabling a new model for healthcare with AI co-clinician
DeepMind has published a blog post outlining research into an AI co-clinician concept aimed at augmenting clinical care. The post describes a vision for AI-augmented healthcare where AI systems work alongside medical professionals. The content appears to be a high-level research direction announcement rather than a specific model or product release.
How scientists are using Claude to accelerate research and discovery
Anthropic describes how researchers are deploying Claude-powered systems across scientific workflows, highlighting three case studies: Biomni (a Stanford agentic platform integrating hundreds of biomedical tools), the Cheeseman Lab (automating large-scale gene knockout experiment interpretation), and others. The piece details Claude for Life Sciences and the AI for Science program, which provides free API credits to high-impact research projects. Specific benchmarks cited include compressing months-long GWAS analyses to 20 minutes and analyzing 336,000 single-cell datasets to identify novel transcription factors.
Eli Lilly Commits Up to $2.75 Billion to Insilico Medicine for AI-Driven Drug Discovery
Eli Lilly agreed to pay up to $2.75 billion to Insilico Medicine, a Hong Kong biotech using generative AI across its drug-discovery pipeline, with an initial $115 million for exclusive rights to undisclosed pre-clinical drug candidates. Insilico's platform uses PandaOmics for target identification and Chemistry42 for molecule design, reducing the time from target identification to preclinical candidates from 5-6 years to roughly 18 months and screening far fewer compounds than conventional methods. The deal is the third between the companies and follows positive Phase 2a results for Rentosertib, an AI-discovered drug targeting idiopathic pulmonary fibrosis. No AI-discovered drug has yet received regulatory approval, and the key open question is whether AI-accelerated compounds will show higher clinical trial success rates than traditionally developed drugs.