Cradle and UCB announced a strategic collaboration on November 4, 2026, to apply Cradle’s AI protein design and engineering platform within selected UCB antibody discovery projects, from hit generation through lead optimization and preclinical readiness.
The collaboration adds Cradle’s end-to-end system to UCB’s expanding digital antibody discovery and engineering program. That program combines UCB’s existing antibody discovery platform with computational science, disease-biology expertise and a network of technology and research partners. UCB aims to identify and optimize high-quality candidates more quickly for diseases where patients still need better options.
Experimental Results Retrain Project Models
As UCB scientists use the platform, experimental results will feed back into program-specific models. The companies describe the resulting cycle as a compounding learning loop, or “lab-in-the-loop”: each round of laboratory evidence informs the next set of designs. Scientists continue to apply their expertise and judgement while optimizing candidates and assessing progression toward a potential candidate.
Cradle’s platform documentation explains the underlying workflow. Assay data train a model on an individual protein’s fitness landscape. Results uploaded from a plate—Cradle’s generations, a team’s rational designs or a mixture—retrain that model before the following round. For early hit generation, a project can start from a single sequence without assay data or from a noisy screening pool such as NGS enrichment counts from a display or selection campaign.
Multi-Property Antibody Design
Cradle’s antibody documentation groups optimization targets into binding, stability, manufacturability, immunogenicity and developability. Measurable objectives can include specificity and cross-reactivity, thermostability and aggregation, expression yield, T-cell epitope risk, and liabilities such as deamidation or oxidation. This gives concrete meaning to the announcement’s requirement that candidates be optimized across the full set of properties needed for success.
Users define a target candidate profile as a primary objective plus constraints. Cradle says the platform optimizes specified properties in parallel, manages trade-offs and estimates the probability of meeting each objective before an assay, while flagging unrealistic targets. A candidate plate can also be scored against the trained model before synthesis and assay spending, producing an expected hit rate, score distributions for each objective and an estimate of remaining headroom. Those forecasts support decisions to run, redesign or stop a round.
The system is accessible through a no-code web application for scientists and an API for computational teams. Research teams can inspect variants, compare mutations and place their own designs alongside Cradle-generated candidates on the same plate. Those external designs are scored on the same basis and become training signal for subsequent rounds, preserving a route for scientists’ own hypotheses to influence the model.
Executives Emphasize Scientist-Led Discovery
UCB Chief Scientific Officer Alistair Henry called AI a “scientific force multiplier” that should augment human expertise across biology, chemistry and translational research rather than be pursued for its own sake. He said combining predictive modeling with wet-lab validation could potentially help design better molecules, accelerate learning and bring high-quality therapeutics to patients sooner.
Cradle CEO and co-founder Stef van Grieken said AI would have its greatest impact on biologics R&D when it extends beyond individual experiments or programs and becomes part of how scientists work across a discovery organization. Within that vision, UCB scientists gain a common platform for more programs, modalities and complex engineering challenges across the company’s biotherapeutics pipeline, although the formal deployment covers selected discovery projects.
The collaboration ties that ambition to repeated experimental learning: each assay can sharpen the next design round, while scientists retain responsibility for candidate decisions. For UCB, the practical test is whether those cycles produce antibodies that meet binding, stability, manufacturability and developability requirements together and progress toward preclinical readiness.
