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.
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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.
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.
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.
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.
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.
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.
Measuring AI's capability to accelerate biological research
OpenAI introduces a real-world evaluation framework designed to measure how AI systems can accelerate biological research in wet lab settings. The work uses GPT-5 to optimize a molecular cloning protocol as a concrete demonstration case. The framework explicitly addresses both the potential benefits and biosecurity risks of AI-assisted experimentation, positioning this as a dual-use capability assessment.