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.

本文提出了多通道等变注意力网络（MEAN）用于抗体设计，通过导入目标抗原和抗体的轻链等组件，将抗体设计公式化为条件图转换问题，并采用 E (3)- 等变信息传递和建议的注意力机制来更好地捕捉不同组件之间的几何关系，最终通过多轮渐进式全射法输出了 1D 序列和 3D 结构，相对于先前的自回归方法，我们的方法在序列和结构建模、抗原结合 CDR ，结合亲和力优化等方面的表现显著优于现有的最先进模型，特别是在抗原结合 CDR 设计和亲和力优化方面表现出了约 23% 和 34% 的相对改进。