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Proceedings of the AAAI Conference on Artificial Intelligence

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Dynamic Geometric Equivariant Network for Full-Atom Antib...
Weihong Huan · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Weihong Huang Wuhan University
  • Feng Yang Wuhan University
  • Qiang Zhang Wuhan University
  • Juan Liu Wuhan University

DOI:

https://doi.org/10.1609/aaai.v40i1.37007

Abstract

Antibody design is critically important in biomedical and therapeutic contexts but remains extremely challenging due to the complexity of antibody sequence–structure relationships and stringent antigen specificity requirements. Traditional computational approaches rely on multi-stage pipelines and often overlook full-atom details (e.g., side-chain conformations) as well as fine-grained geometric features, resulting in limited effectiveness. To overcome these limitations, we propose Dynamic Geometric Equivariant Network (DGENet), an end-to-end full-atom antibody design model that integrates a geometric-kinematic equivariant dynamic optimization module (GK-EDO) with an full-atom E(3)-equivariant message-passing architecture. This framework enables iterative optimization of antibody structures under explicit geometric and kinematic constraints, generating complete antibody structures (including backbone and side chains) and simultaneously jointly optimizing the sequences and 3D structures of the complementarity-determining regions (CDRs). DGENet also introduces a novel virtual anchor docking mechanism that employs an adaptive PNet-Kabsch module to explicitly guide antibody–antigen binding and achieve precise bound conformations. Evaluations on multiple benchmark datasets demonstrate that DGENet exhibits outstanding performance in antibody structure and sequence generation as well as in designing high-affinity antibodies, underscoring its reliability as an advanced antibody design model.

How to Cite

Huang, W., Yang, F., Zhang, Q., & Liu, J. (2026). Dynamic Geometric Equivariant Network for Full-Atom Antibody Design. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 444–452. https://doi.org/10.1609/aaai.v40i1.37007

Issue

Section

AAAI Technical Track on Application Domains I

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