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AbGaze advances computational antibody design by introducing an attentive geometric representation learning framework that explicitly encodes cross-molecular spatial relationships rather than relying on implicit scalar distances. This approach significantly outperforms existing methods in amino-acid recovery, structural error reduction, and affinity optimization across multiple co-design tasks. The work represents a notable step forward in applying specialized machine learning architectures to solve complex protein engineering challenges.
AbGaze: Attentive Geometric Representation Learning for End-to-End Antibody Design
arXiv:2609.35296v1 Announce Type: new Abstract: Computational antibody design requires representations that capture the geometric patterns underlying antigen--antibody interactions, yet existing approaches often 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 relative to antibody-residue local frames, and adaptively aggregates these geometric interactions according to their interfacial context. The learned interaction representation 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: relative 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 interface docking quality (DockQ) by 6.6%, and raises the affinity improvement rate (IMP) by 32.5%.
Abstract:Computational antibody design requires representations that capture the geometric patterns underlying antigen--antibody interactions, yet existing approaches often 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 relative to antibody-residue local frames, and adaptively aggregates these geometric interactions according to their interfacial context. The learned interaction representation 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: relative 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 interface docking quality (DockQ) by 6.6%, and raises the affinity improvement rate (IMP) by 32.5%.
Artificial Intelligence (cs.AI)
arXiv:2609.35296 [cs.AI]
(or arXiv:2609.35296v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.35296 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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