NVIDIA and Eli Lilly announced a first-of-its-kind AI co-innovation lab that will invest up to $1 billion over five years to transform drug discovery by combining pharmaceutical expertise with advanced AI infrastructure and continuous learning systems. The partnership, announced at the J.P. Morgan Healthcare Conference in January 2026, represents a significant shift in how major pharmaceutical companies approach the earliest and most time-consuming stages of medicine development .

The co-located lab, based in the San Francisco Bay Area, will bring together Lilly's domain experts in biology, chemistry, and medicine with NVIDIA's AI researchers and engineers to build powerful AI models that can accelerate medicine development. Rather than treating AI as a separate tool, the companies are designing a system where computational and laboratory work inform each other continuously, 24 hours a day .

What Makes This Partnership Different From Other AI Drug Discovery Efforts?

The core innovation lies in what the companies call a "scientist-in-the-loop" framework. Instead of AI running experiments in isolation, the system tightly connects Lilly's wet labs, where physical experiments happen, with computational dry labs where AI models are trained and refined. This creates a feedback loop where experiments generate data, AI learns from that data, and the improved AI models guide the next round of experiments .

The infrastructure will be built on NVIDIA's BioNeMo platform, a specialized system for biological and chemical AI models, and will leverage NVIDIA's Vera Rubin architecture for next-generation computing power. Lilly previously announced its own AI supercomputer, described as the most powerful in the pharmaceutical industry, which will train large foundation models capable of identifying, optimizing, and validating new molecules with exceptional speed and accuracy .

"AI is transforming every industry, and its most profound impact will be in life sciences. NVIDIA and Lilly are bringing together the best of our industries to invent a new blueprint for drug discovery, one where scientists can explore vast biological and chemical spaces in silico before a single molecule is made," said Jensen Huang, founder and CEO of NVIDIA.

Jensen Huang, Founder and CEO at NVIDIA

How Will the Lab Accelerate Drug Development?

• Continuous Learning Systems: The lab will create AI systems that continuously learn from experimental data, allowing researchers to test hypotheses computationally before conducting expensive physical experiments in the laboratory.
• Foundation and Frontier Models: The teams will develop next-generation AI models specifically trained on biological and chemical data, enabling faster identification of promising drug candidates across multiple disease areas.
• Physical AI and Robotics: Beyond drug discovery, the partnership will integrate robotics and digital twins to enhance manufacturing capacity, model supply chains, and optimize production lines before making physical changes in the real world.
• Multimodal AI Integration: The lab will explore applications across clinical development, manufacturing, and commercial operations, using AI agents and digital twins to integrate different types of data and decision-making processes.

The investment extends beyond pure drug discovery. NVIDIA and Lilly plan to apply AI across clinical development, manufacturing, and commercial operations. Using NVIDIA Omniverse libraries and RTX PRO servers, Lilly can create digital twins of its manufacturing lines to model and stress-test entire supply chains before implementing changes in physical facilities .

"For nearly 150 years, we've been working to bring life-changing medicines to patients. Combining our volumes of data and scientific knowledge with NVIDIA's computational power and model-building expertise could reinvent drug discovery as we know it. By bringing together world-class talent in a startup environment, we're creating the conditions for breakthroughs that neither company could achieve alone," stated David A. Ricks, chair and CEO of Lilly.

David A. Ricks, Chair and CEO at Eli Lilly

How Does This Fit Into Lilly's Broader AI Strategy?

This co-innovation lab builds on Lilly's existing AI infrastructure and recent partnerships. The company has already begun sharing select AI models through Lilly TuneLab, a platform that provides biotech companies access to Lilly's proprietary models built on decades of research data. The new lab will integrate NVIDIA Clara, an open-source foundation model for life sciences, into future workflow offerings .

Lilly's commitment to AI-driven drug discovery has also extended internationally. In March 2026, the company reached a $2.75 billion deal with Hong Kong-based Insilico Medicine to bring AI-discovered drugs to the global market. Insilico has developed at least 28 drugs using generative AI tools, with nearly half already in clinical trials. The agreement includes $115 million upfront, with the remainder tied to regulatory and commercial milestones, plus royalties on future sales .

"This collaboration allows us to explore novel mechanisms and accelerate the identification of promising therapeutic candidates across multiple disease areas," explained Andrew Adams, group vice president of Molecule Discovery at Lilly, noting that Insilico's AI-enabled discovery is "a powerful complement" to Lilly's clinical development capabilities.

Andrew Adams, Group Vice President of Molecule Discovery at Lilly

The Insilico partnership demonstrates that Lilly is pursuing multiple pathways to integrate AI into drug discovery. While the NVIDIA co-innovation lab focuses on building proprietary models and infrastructure, the Insilico deal provides access to externally developed AI-discovered candidates that can move through Lilly's established clinical and regulatory processes .

The NVIDIA-Lilly lab is expected to begin operations in South San Francisco early in 2026. The partnership signals a broader industry trend where pharmaceutical companies are moving beyond simply adopting AI tools and instead building deep technical partnerships with AI infrastructure providers to create integrated systems that fundamentally change how medicines are discovered and developed .