AbFlow用流匹配模型端到端设计抗体，提升结合亲和力。
AbFlow : End-to-end Paratope-Centric Antibody Design by Interaction Enhanced Flow Matching
- 基于最优传输的流匹配框架，整合抗原表面交互信息。
- 在接触界面和全局结构上显著提升抗体结合亲和力。
- 适合抗体药物设计、精准对接优化的研究者使用。
抗原-抗体结合是免疫应答的关键过程。尽管近期抗体设计取得进展，现有方法缺乏端到端生成全原子抗体结构的框架，且难以充分利用抗原特异性几何信息优化局部结合界面与整体结构。为此，我们提出AbFlow，一种基于最优传输的流匹配框架，用于端到端设计全原子抗体。AbFlow引入具备等变特性的表面多通道编码器（Surface Multi-channel Encoder），利用抗原表面交互数据精炼抗体结构，尤其聚焦于CDR-H3区域。大量实验表明，该方法在围绕表位的抗体设计、多CDR区域建模、全原子抗体生成、结合亲和力优化及复合物结构预测任务中表现优异，生成的抗原-抗体复合物在接触界面尤为出色，并显著提升抗体结合亲和力。
原文摘要 · Abstract (English)
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.
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