The news: Eli Lilly extended its partnership with AI drug developer Insilico in a deal worth up to $2.75 billion for exclusive licensing of select Insilico-discovered drug candidates, along with access to its Pharma.AI platform.

• Lilly is paying Insilico $115 million upfront, with additional payments tied to regulatory and commercial milestones.
• Across its AI-driven drug discovery efforts, Insilico has so far identified 28 drug candidates for preclinical development, with 10 in clinical trials across pharma.
• Its model centers on discovering and licensing candidates to pharma.

Why it matters: Lilly’s deal reflects a broader shift in how pharma companies are approaching drug discovery, as companies bring in AI partners and begin exploring outsourced early-stage R&D with specialized firms.

• Novo Nordisk struck a similar deal with Deep Apple for obesity candidates, while Pfizer expanded its collaboration with PostEra last year to include antibody-drug conjugate (ADC) targets.

Pharma companies are looking to use AI to identify stronger candidates faster, with the potential to reduce development timelines and costs that can take 10 to 15 years and reach up to $2.6 billion.

• GenAI can cut drug discovery and optimization timelines by 25% or more, per a December BCG report.
• A December analysis in ScienceDirect highlights AI’s value in early R&D, finding AI’s greatest impact is in early-stage discovery where it significantly speeds up molecule discovery and optimization while lowering experimental costs.

Implications for pharma companies: AI is becoming a core part of drug discovery, especially in early-stage R&D where it delivers the most value today. That shift is forcing pharma companies to rethink whether to build, partner, or outsource AI-enabled drug discovery—and how to balance each of those.

Companies that integrate AI effectively into early pipelines and R&D, balancing external AI tie-ups with long-held internal scientific expertise, could move faster and spend more efficiently. Those that are slower to adopt or lack the resources to buy in risk losing speed and cost advantages as AI-driven discovery becomes more standard.

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