Target Audience

Pharmaceutical companies, biotech firms, and research organizations focused on antibody-based therapeutics and drug discovery.

Challenge

Bayer faces the challenge of accelerating antibody discovery and optimization to bring higher-quality molecules into clinical development faster. Traditional methods require multiple optimization cycles, increasing time and cost while reducing the probability of technical success. AI-driven solutions are needed to improve molecular potency, safety, and manufacturability while scaling across portfolios and teams.

Solution Approach

Bayer collaborates with Cradle to integrate Cradle’s generative AI platform into its R&D workflows. This platform uses machine learning to streamline design-test-learn cycles, reduce optimization iterations, and improve molecular attributes. The lab-in-the-loop approach ensures that AI-driven insights are directly applied by expert scientists, enhancing productivity and scalability without requiring deep ML expertise.

Value Add

The collaboration enables Bayer to deliver faster, more effective medicines to patients by reducing development cycles, improving molecule quality, and increasing the success rate of clinical candidates. Cradle’s platform also supports enterprise-grade AI adoption, allowing Bayer to operationalize AI at scale across its biologics portfolio.

References

Implemented by Bayer (a global pharmaceutical leader) and Cradle (an AI-driven protein engineering platform serving top pharma companies).

Read more here: https://www.bayer.com/en/us/news-stories/ai-enabled-antibody-discovery-and-optimization