  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.
