On Feb. 18, 2026, Merck, known as MSD outside of the U.S. and Canada, and Mayo Clinic, the world’s top-ranked hospital system, announced a research and development agreement to apply artificial intelligence (AI), advanced analytics and multimodal clinical data to support drug discovery and development. The agreement integrates Mayo Clinic’s Platform architecture as well as clinical and genomic datasets with Merck’s ambition to harness AI-enabled virtual cell technologies to enhance disease understanding, improve target identification, and drive early development decisions.

By leveraging Mayo Clinic Platform, which brings together data from Mayo Clinic U.S. and its international partner network in a secure environment, Merck will integrate Mayo Clinic’s clinical insights and genomic data sets, including AI and machine learning (ML)-enabled discovery spanning computational and spatial biology. The Mayo Clinic Platform_Orchestrate program provides Merck direct access to Mayo Clinic’s world-class clinical and scientific expertise, Platform data including de-identified clinical and multimodal data sets, registries and biorepositories, advanced AI tools and analytics, and the ability to scale solutions.

Under the agreement, which marks Mayo Clinic’s first strategic collaboration of this scale with a global biopharmaceutical company, Merck will leverage Mayo Clinic’s extensive multimodal data — including laboratory results, medical imaging, clinical notes and molecular data — to support validation of AI models and help translate research insights into discovery and development strategies.

The collaboration will initially focus on high-need therapeutic areas in three specialties where advanced analytics and multimodal approaches have the potential to advance progress in the development of more effective and tailored therapies: Gastroenterology — Inflammatory Bowel Disease (IBD); Dermatology — Atopic dermatitis; and Neurology — Multiple sclerosis.

The collaboration builds on Merck’s broader investments in AI/ML-enabled discovery, spanning computational or spatial biology, AI foundation models and real-world data, and reflects a shared focus on applying advanced technologies in ways that support disciplined, evidence-based drug development.