NVIDIA & Eli Lilly: The AI Revolution in Drug Discovery | Jensen Huang & David Ricks

2026-02-02 · Source: NVIDIA · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Life Sciences & Biology, Pharmaceuticals & Biotechnology · Depth: Advanced, extended

Summary

NVIDIA and Eli Lilly have announced a landmark partnership to establish a co-innovation lab in the Bay Area, aiming to revolutionize drug discovery through accelerated computing and AI. This collaboration will involve building the world's largest dedicated on-prem biology supercomputer in Indianapolis and assembling a joint research team. The initiative seeks to overcome the pharmaceutical industry's long innovation cycles and empirical drug discovery methods by applying advanced AI, multimodality reasoning, and synthetic data flywheels. NVIDIA's 33 years of accelerated computing development, which has boosted AI performance a million times in a decade, will be combined with Eli Lilly's deep expertise in life sciences and drug development, including its success with GLP-1 therapies. The partnership will focus on computer-aided drug design, target discovery, and optimizing new therapeutic modalities like RNA and gene therapies, with the goal of transforming drug discovery from an artisanal process to an engineering problem.

Key takeaway

For CTOs and VPs of Engineering in life sciences, this partnership highlights a critical shift: integrating advanced AI and accelerated computing is no longer optional but essential for competitive drug discovery. Your teams should explore co-designing full-stack solutions, investing in dedicated AI infrastructure, and leveraging multimodality AI for complex biological problems. This approach promises to transform empirical discovery into a more predictable engineering process, significantly shortening innovation cycles and addressing unmet medical needs.

Key insights

Co-designing computing stacks for specific problems accelerates applications far beyond Moore's Law.

Principles

• Create conditions for great inventions.
• Speed up invention to beat patent cycles.
• AI needs massive, high-quality data.

Method

The NVIDIA-Eli Lilly co-innovation lab will integrate a dedicated biology supercomputer, AI research teams, and robotic wet labs to create a synthetic data flywheel for drug engineering and target discovery.

In practice

• Utilize multimodality AI for complex reasoning.
• Build dedicated computing infrastructure for AI research.
• Employ federated learning for secure data collaboration.

Topics

• NVIDIA-Eli Lilly Partnership
• AI Drug Discovery
• Accelerated Computing
• Generative AI
• Life Sciences Platforms

Best for: CTO, VP of Engineering/Data, Entrepreneur, Executive, Investor, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA.