GeoGAD: Geometry-Aware Antibody Design Framework for Complementarity-Determining Region Precision Engineering.

Authors

Songjian Wei, Jinxiong Zhang, Yan Chen, Chunyan Tang, Jiayang Tan

Published in

Bioinformatics (Oxford, England). Jan 24, 2026. Epub Jan 24, 2026.

Abstract

Antibodies, as pivotal effector molecules of the immune system, neutralize pathogens through specific binding to antigens mediated by complementarity-determining regions (CDRs), highlighting the critical importance for precise antibody design in diagnostics and therapeutics. Despite significant advances in CDR design, current methods remain limited by inadequate modeling of geometric constraints, omission of multi-scale spatial relationships, and insufficient conformational representation capacity-factors that collectively degrade prediction accuracy.
To overcome these limitations, we present GeoGAD, a geometry-aware antibody design framework with Gaussian attention mechanisms. Key innovations include: (1) the introduction of rotational positional encoding to enhance geometric sensitivity; (2) a geometry-aware module that integrates multi-scale spatial features through dynamic message passing, adaptive edge refinement, and multi-edge-type coordinate optimization; and (3) a Gaussian attention mechanism that employs an edge-type-sensitive spatial Gaussian kernel to model long-range sequence dependencies, enabling focused attention on local critical residues while preserving global contextual modeling. Experimental evaluations demonstrate that GeoGAD achieves superior or comparable performance to state-of-the-art models across antibody sequence-structure co-modeling, CDR design, and affinity optimization benchmarks, particularly excelling in amino acid recovery rates (AAR) and structural accuracy metrics (RMSD, TM-score). By enhancing the design precision of antibody CDR regions, GeoGAD offers a geometrically consistent framework for the computational design of therapeutic antibodies.
The source code and implementation are available at https://github.com/WeiSongJian/GeoGAD, and the archival version for this manuscript is deposited at https://doi.org/10.5281/zenodo.18073443.

PMID:
41580967
Bibliographic data and abstract were imported from PubMed on 25 Jan 2026.

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