Accelerating scientific discovery with Co-Scientist
Ускорение научных открытий с помощью Co-Scientist
2026-05-19
SCID: 54.1/jwj66sd9
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antimicrobial resistancedrug repurposingmulti-agent AI systemscientific hypothesis generationtest-time compute scaling
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Abstract (AI)
Abstract Scientific discovery is driven by scientists generating hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent artificial intelligence (AI) system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and previous scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system’s design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling, and (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. Although this is a general-purpose system, we focus the validation in three biomedical applications: drug repurposing; novel-target discovery 1 ; and explaining mechanisms of antimicrobial resistance 2 . Specifically, Co-Scientist helped to identify new drug-repurposing candidates and synergistic combination therapies for acute myeloid leukaemia that were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI-empowered scientists.
Key Findings
1
Automated evaluations show that increasing test-time compute continually improves the quality of generated hypotheses over time.
2
Co-Scientist is a Gemini-based multi-agent AI system designed to generate demonstrably novel, experimentally testable scientific hypotheses from research objectives and prior evidence.
3
In biomedical validation tasks, Co-Scientist identified drug-repurposing candidates and synergistic combination therapies for acute myeloid leukaemia.
4
Its asynchronous multi-agent architecture enables flexible test-time compute scaling, while tournament evolution supports continuous critique, refinement, and self-improvement of hypotheses.
5
The identified acute myeloid leukaemia candidates and combination therapies were validated through in vitro experiments, demonstrating potential to accelerate scientific discovery.
Research Object
Co-Scientist, a multi-agent AI system for scientific hypothesis generation, validated in biomedical applications
Research Subject
The system’s ability to generate, critique, refine, and improve the novelty and quality of experimentally testable scientific hypotheses, including drug-repurposing and synergistic-therapy hypotheses for acute myeloid leukaemia
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2026-05-19
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