Antibody discovery can return thousands of binders and still produce too few genuine choices. Sequence diversity is not epitope diversity, affinity is not mechanism, and a potent binder may fail during humanization, expression, or formulation.

 

We develop closed-loop experimental and computational workflows that move these decisions upstream. Single-cell functional data, domain-level binding patterns, structural modeling, epitope binning, humanization, and developability assessment are integrated to cluster candidates, quantify uncertainty, and select molecules worth building. Experimental results are fed back into modeling and design rules so each cycle becomes more discriminating.

 

- Function-first discovery — capture biological activity and binding pattern while the initial candidate pool is still broad.

- Structure-aware epitope binning — combine experimental domain mapping with computational complex modeling and confidence gates.

- Humanization and developability — preserve function while considering framework choice, stability and manufacturability.

- Closed-loop learning — use experimental validation to update ranking, quality-control and design rules.