Merck and Mayo Clinic announced a research and development agreement on February 18, 2026 to apply artificial intelligence, advanced analytics, and multimodal clinical data to drug discovery and development. The partnership combines Mayo Clinic’s Platform architecture, clinical and genomic datasets, and AI‑enabled virtual cell technologies with Merck’s AI and machine‑learning research capabilities.
The collaboration gives Merck direct access to Mayo Clinic’s de‑identified clinical and multimodal data sets, including laboratory results, medical imaging, clinical notes and molecular data. By integrating these data with Merck’s AI tools, the company aims to improve target identification and accelerate early‑development decisions. The focus is on high‑need therapeutic areas in gastroenterology (inflammatory bowel disease), dermatology (atopic dermatitis), and neurology (multiple sclerosis).
While the financial terms of the agreement have not been disclosed, the partnership is expected to reduce the time and cost of bringing new candidates to market, potentially accelerating the launch of new therapies and strengthening Merck’s competitive position beyond oncology. Merck’s strategy to diversify its pipeline ahead of the Keytruda patent cliff is reinforced by this collaboration.
Merck’s CEO Robert M. Davis said the partnership “enhances our ability to innovate with the potential to bring important new therapies to patients faster.” Mayo Clinic’s President and CEO Gianrico Farrugia noted that the collaboration “will speed innovative breakthroughs to patients and redefine drug development.” The agreement also signals Mayo Clinic’s first strategic collaboration of this scale with a global biopharmaceutical company.
The partnership is part of Merck’s broader investment in AI and machine‑learning for drug discovery, which includes computational biology, AI foundation models, and real‑world data analysis. By leveraging Mayo Clinic’s large, high‑quality data sets and AI platform, Merck aims to accelerate disease understanding, improve target identification, and enhance early‑stage development decisions.
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