  Antibodies are versatile proteins that can bind to pathogens and provide
effective protection for human body. Recently, deep learning-based
computational antibody design has attracted popular attention since it
automatically mines the antibody patterns from data that could be complementary
to human experiences. However, the computational methods heavily rely on
high-quality antibody structure data, which is quite limited. Besides, the
complementarity-determining region (CDR), which is the key component of an
antibody that determines the specificity and binding affinity, is highly
variable and hard to predict. Therefore, the data limitation issue further
raises the difficulty of CDR generation for antibodies. Fortunately, there
exists a large amount of sequence data of antibodies that can help model the
CDR and alleviate the reliance on structure data. By witnessing the success of
pre-training models for protein modeling, in this paper, we develop the
antibody pre-training language model and incorporate it into the
(antigen-specific) antibody design model in a systemic way. Specifically, we
first pre-train an antibody language model based on the sequence data, then
propose a one-shot way for sequence and structure generation of CDR to avoid
the heavy cost and error propagation from an autoregressive manner, and finally
leverage the pre-trained antibody model for the antigen-specific antibody
generation model with some carefully designed modules. Through various
experiments, we show that our method achieves superior performances over
previous baselines on different tasks, such as sequence and structure
generation and antigen-binding CDR-H3 design.
