用几何神经网络设计抗体，精准匹配复杂抗原结合位点。
Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding
- 基于多尺度等变图扩散模型，联合优化抗体序列与结构。
- 在关键CDR-H3区域实现0.062Å的均方根偏差降低，氨基酸恢复率提升10.13%。
- 适合抗体药物研发人员，尤其关注复杂抗原靶向设计者。
抗体设计在治疗与诊断开发中仍具挑战性，尤其针对具有多样结合界面的复杂抗原。现有计算方法面临两大局限：（1）捕捉几何特征的同时保持对称性；（2）泛化至新型抗原界面。尽管近期进展显著，但这些方法常无法准确建模分子相互作用并维持结构完整性。为此，我们提出AbMEGD，一种端到端框架，整合多尺度等变图扩散技术，实现抗体序列与结构的联合设计。借助先进的几何深度学习，AbMEGD融合原子级几何特征与残基级嵌入，同时捕捉局部原子细节与全局序列-结构交互。其E(3)-等变扩散机制确保几何精度、计算效率与对复杂抗原的强泛化能力。实验基于SAbDab数据库显示，相比领先模型DiffAb，AbMEGD在氨基酸恢复率上提升10.13%，改进率提高3.32%，关键CDR-H3区域的均方根偏差降低0.062 Å。结果表明，AbMEGD能有效平衡结构完整性与功能提升，为序列-结构联合设计与亲和力优化树立新基准。代码已开源：https://github.com/Patrick221215/AbMEGD。
原文摘要 · Abstract (English)
Antibody design remains a critical challenge in therapeutic and diagnostic development, particularly for complex antigens with diverse binding interfaces. Current computational methods face two main limitations: (1) capturing geometric features while preserving symmetries, and (2) generalizing novel antigen interfaces. Despite recent advancements, these methods often fail to accurately capture molecular interactions and maintain structural integrity. To address these challenges, we propose \textbf{AbMEGD}, an end-to-end framework integrating \textbf{M}ulti-scale \textbf{E}quivariant \textbf{G}raph \textbf{D}iffusion for antibody sequence and structure co-design. Leveraging advanced geometric deep learning, AbMEGD combines atomic-level geometric features with residue-level embeddings, capturing local atomic details and global sequence-structure interactions. Its E(3)-equivariant diffusion method ensures geometric precision, computational efficiency, and robust generalizability for complex antigens. Furthermore, experiments using the SAbDab database demonstrate a 10.13\% increase in amino acid recovery, 3.32\% rise in improvement percentage, and a 0.062~Å reduction in root mean square deviation within the critical CDR-H3 region compared to DiffAb, a leading antibody design model. These results highlight AbMEGD's ability to balance structural integrity with improved functionality, establishing a new benchmark for sequence-structure co-design and affinity optimization. The code is available at: https://github.com/Patrick221215/AbMEGD.
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