Bayer, Cradle Collaborate on AI-Driven Antibody Engineering

Bayer has entered into a three-year strategic collaboration with Cradle to deploy an AI-enabled software platform for antibody discovery and optimization. Under the agreement, Bayer will integrate Cradle’s generative AI tools into its existing research and development workflows to enhance lead generation and optimization across its therapeutic antibody pipeline.

The collaboration focuses on addressing one of the most resource-intensive stages of biologics development: iterative antibody optimization. By applying machine learning models directly within design–test–learn cycles, Bayer aims to reduce the number of experimental iterations required while improving molecular potency, safety, and manufacturability, particularly as programs move toward more complex mechanisms of action.

Cradle’s platform supports protein engineering through a lab-in-the-loop approach, combining computational design with continuous experimental feedback. This enables antibody scientists to evaluate and refine candidate molecules using real laboratory data, allowing AI models to adapt dynamically as new results are generated. The goal is to increase the probability of technical success as candidates progress toward clinical development.

A central requirement for Bayer was the ability to deploy AI at scale across teams without requiring deep machine learning expertise. Cradle’s software is designed to be scientist-centric, allowing antibody researchers to apply AI-supported design methods within familiar workflows. Following a successful proof-of-concept phase, Bayer selected Cradle from multiple evaluated vendors based on suitability for enterprise-wide adoption.

The collaboration also includes a joint machine learning research project aimed at further extending AI capabilities for antibody engineering. This work will build on Bayer’s internal expertise in antibody design, synthesis, and applied machine learning, complemented by Cradle’s experience in scalable AI software development.

Together, the two companies aim to operationalize AI as a core component of biologics R&D, supporting faster candidate generation and higher-quality antibody development while maintaining close integration between computational tools and experimental science.

www.bayer.com