Under the collaboration, UCB will apply Cradle’s platform within selected discovery projects from hit generation through lead optimization and preclinical readiness. The collaboration reflects UCB’s mission of translating cutting-edge science into meaningful advances for patients. By fully integrating AI across the discovery process and strengthening its digital antibody engineering capabilities, UCB aims to accelerate the ability to identify and optimize high-quality candidates for diseases where patients continue to need better options.
“At UCB, we view AI not as something to chase for its own sake, but as a scientific force multiplier that augments human expertise across biology, chemistry, and translational research. We are excited to partner with Cradle as we expand our digital antibody engineering capabilities,” said Alistair Henry, Chief Scientific Officer of UCB. “Adding Cradle’s end-to-end platform across our discovery pipeline allows us to apply computational approaches in a deeply rigorous and translational way. Predictive modeling combined with wet-lab validation allows us to potentially design better molecules, learn faster, and bring high-quality therapeutics to patients even sooner.”
As UCB scientists use the Cradle platform across programs, experimental results continuously feed back into program-specific models. This creates a compounding learning loop or “lab-in-the-loop” that supports scientists in applying their expertise and judgement to optimize candidates across the full set of properties required for success and accelerate progression toward a potential candidate.
“AI will have its greatest impact on biologics R&D when it can be scaled beyond individual experiments or programs and become part of how scientists work across the discovery organization,” said Stef van Grieken, CEO and Co-Founder of Cradle. “Partnering with UCB brings Cradle across its biotherapeutics pipeline, giving scientists a common platform to apply AI to more programs, more modalities and more complex engineering challenges and ultimately advance better therapies.”
