Antibody design is valuable for therapeutic usage and biological research. Existing deep-learning-based methods encounter several key issues: 1) incomplete context for Complementarity-Determining Regions (CDRs) generation; 2) incapability of capturing the entire 3D geometry of the input structure; 3) inefficient prediction of the CDR sequences in an autoregressive manner. In this paper, we propose Multi-channel Equivariant Attention Network (MEAN) to co-design 1D sequences and 3D structures of CDRs. To be specific, MEAN formulates antibody design as a conditional graph translation problem by importing extra components including the target antigen and the light chain of the antibody. Then, MEAN resorts to E(3)-equivariant message passing along with a proposed attention mechanism to better capture the geometrical correlation between different components. Finally, it outputs both the 1D sequences and 3D structure via a multi-round progressive full-shot scheme, which enjoys more efficiency and precision against previous autoregressive approaches. Our method significantly surpasses state-of-the-art models in sequence and structure modeling, antigen-binding CDR design, and binding affinity optimization. Specifically, the relative improvement to baselines is about 23% in antigen-binding CDR design and 34% for affinity optimization.

翻译：抗体设计在治疗应用和生物学研究中具有重要价值。现有基于深度学习的方法存在若干关键问题：1）互补决定区（CDR）生成缺乏完整上下文；2）无法捕获输入结构的完整三维几何信息；3）以自回归方式预测CDR序列的低效性。本文提出多通道等变注意力网络（MEAN），用于协同设计CDR的一维序列和三维结构。具体而言，MEAN通过引入靶抗原和抗体轻链等额外组件，将抗体设计建模为条件图翻译问题。随后，模型采用E(3)等变消息传递机制结合提出的注意力机制，以更好捕获不同组件间的几何相关性。最终，通过多轮渐进式全包方案同时输出一维序列和三维结构，相较于先前自回归方法具有更高效率和精度。我们的方法在序列与结构建模、抗原结合CDR设计及结合亲和力优化方面显著超越现有最优模型。具体而言，在抗原结合CDR设计和亲和力优化任务中，相较于基线方法的相对提升率分别达到约23%和34%。