Antigen-antibody binding is a critical process in the immune response. Although recent progress has advanced antibody design, current methods lack a generative framework for end-to-end modeling of full-atom antibody structures and struggle to fully exploit antigen-specific geometric information for optimizing local binding interfaces and global structures. To overcome these limitations, we introduce AbFlow, a flow-matching framework that leverages optimal transport to design full-atom antibodies end-to-end. AbFlow incorporates an extended velocity field network featuring an equivariant Surface Multi-channel Encoder, which uses surface-level antigen interaction data to refine the antibody structure, particularly the CDR-H3 region. Extensive experiments in paratoep-centric antibody design, multi-CDRs and full-atom antibody design, binding affinity optimization, and complex structure prediction show that AbFlow produces superior antigen-antibody complexes, especially at the contact interface, and markedly improves the binding affinity of generated antibodies.

翻译：抗原-抗体结合是免疫应答中的关键过程。尽管近期研究进展推动了抗体设计领域的发展，现有方法仍缺乏能够端到端建模全原子抗体结构的生成框架，且难以充分利用抗原特异性几何信息来优化局部结合界面与全局结构。为克服这些局限，我们提出AbFlow——一种利用最优传输理论实现端到端全原子抗体设计的流匹配框架。AbFlow引入了配备等变表面多通道编码器的扩展速度场网络，该网络利用表面层级的抗原相互作用数据优化抗体结构，特别是CDR-H3区域。在互补位中心抗体设计、多CDR区域与全原子抗体设计、结合亲和力优化以及复合物结构预测等任务中的大量实验表明，AbFlow能够生成更优的抗原-抗体复合物（尤其在接触界面），并显著提升生成抗体的结合亲和力。