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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. Inthis paper, we propose Multi-channel Equivariant Attention Network (MEAN) toco-design 1D sequences and 3D structures of CDRs. To be specific, MEANformulates antibody design as a conditional graph translation problem byimporting extra components including the target antigen and the light chain ofthe antibody. Then, MEAN resorts to E(3)-equivariant message passing along witha proposed attention mechanism to better capture the geometrical correlationbetween different components. Finally, it outputs both the 1D sequences and 3Dstructure via a multi-round progressive full-shot scheme, which enjoys moreefficiency and precision against previous autoregressive approaches. Our methodsignificantly surpasses state-of-the-art models in sequence and structuremodeling, antigen-binding CDR design, and binding affinity optimization.Specifically, the relative improvement to baselines is about 23% inantigen-binding CDR design and 34% for affinity optimization.

Conditional Antibody Design as 3D Equivariant Graph Translation