AbGaze: Attentive Geometric Representation Learning for End-to-End Antibody Design

Computational antibody design requires representations that capture the geomet- ric patterns underlying antigen–antibody interactions, yet existing approaches of- ten rely on scalar distances or surface-intrinsic features, leaving cross-molecular geometry largely implicit. We present AbGaze, an end-to-end antibody design framework based on attentive geometric representation learning, which encodes distance, spatial direction, and surface-normal orientation of antigen surfaces rel- ative to antibody-residue local frames, and adaptively aggregates these geometric interactions according to their interfacial context. The learned interaction repre- sentation is shared across multi-CDR co-design, complex structure prediction, and affinity optimization, with local-frame geometric supervision further constraining the representation. AbGaze outperforms prior methods across all three tasks: rel- ative to the second-best method, it improves amino-acid recovery by 7.1% and reduces structural error by 14.9% on average over the six CDRs, improves inter- face docking quality (DockQ) by 6.6%, and raises the affinity improvement rate (IMP) by 32.5%.