Eli Lilly and BigHat Biosciences collaborate on AI-driven antibody discovery
Eli Lilly partnered with BigHat Biosciences to co-develop next-generation therapeutic antibodies using AI technology. BigHat's Milliner platform leverages machine learning and synthetic biology high-speed wet labs to optimize key antibody attributes for accelerated biologics development.
- Organization
- Eli Lilly
- Industry
- Pharma
- Location
- United States
- Published
- April 2025
Reported outcomes
Strategic outcomes
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Eli Lilly
- Provider
- Microsoft
- Maturity
- Production
- Linked source
- Yahoo Finance
Lengthy and costly antibody discovery process delays therapeutics to market Difficulty optimizing multiple antibody attributes simultaneously (affinity, specificity, immunogenicity, manufacturability) High failure rates and inefficiencies in traditional wet lab experimentation Urgent need to accelerate biologics development for competitive advantage in pharma R&D Deployed BigHat's Milliner platform combining ML and synthetic biology for antibody engineering Used Azure ML to power machine learnin
Primary read
Use case focus
Showing 3 of 5
- 1AI-driven optimization of antibody candidate attributes (affinity, specificity, immunogenicity)
- 2Automated experimental design and validation in high-speed wet labs
- 3Accelerated in silico lead selection for next-generation therapeutic antibodies
- Lengthy and costly antibody discovery process delays therapeutics to market
- Difficulty optimizing multiple antibody attributes simultaneously (affinity, specificity, immunogenicity, manufacturability)
- High failure rates and inefficiencies in traditional wet lab experimentation
- Urgent need to accelerate biologics development for competitive advantage in pharma R&D
- Deployed BigHat's Milliner platform combining ML and synthetic biology for antibody engineering
- Used Azure ML to power machine learning models for rapid optimization of antibody properties
- Implemented high-speed wet lab automation to quickly test and validate AI-generated designs
- Collaborated closely between BigHat and Eli Lilly through R&D co-development and equity investment
- Accelerated antibody discovery timelines by leveraging AI-driven design and high-speed wet labs
- Improved therapeutic profiles of biologics across multiple key attributes
- Enabled up to 2 new antibody programs with faster progression towards clinical trials
- Supported advancement of internal GI cancer antibody-drug conjugate (ADC) program toward IND-enabling stage
Implementation partners1
Sources & evidence1
- Customer explicitly identified
- Deployment status explicitly supported
- Independent source available
- Technical implementation details available
- Recent evidence check available
- Last evidence check: Jul 22, 2026.
The case's original source is still reachable.
- Cited source last checked Jun 12, 2026 — ok (0/1 broken).
Measures whether this deployment's public evidence persists — not whether the system is still in production.
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