Decoupled Sequence and Structure Generation for Realistic Antibody Design
<|reference_start|>Decoupled Sequence and Structure Generation for Realistic Antibody Design: Antibody design plays a pivotal role in advancing therapeutics. Although deep learning has made rapid progress in this field, existing methods jointly generate antibody sequences and structures, limiting task-specific optimization. In response, we propose an antibody sequence-structure decoupling (ASSD) framework, which separates sequence generation and structure prediction. Although our approach is simple, such a decoupling strategy has been overlooked in previous works. We also find that the widely used non-autoregressive generators promote sequences with overly repeating tokens. Such sequences are both out-of-distribution and prone to undesirable developability properties that can trigger harmful immune responses in patients. To resolve this, we introduce a composition-based objective that allows an efficient trade-off between high performance and low token repetition. Our results demonstrate that ASSD consistently outperforms existing antibody design models, while the composition-based objective successfully mitigates token repetition of non-autoregressive models. Our code is available at \url{https://github.com/lkny123/ASSD_public}.<|reference_end|>
@article{kim2024decoupled,
    title={Decoupled Sequence and Structure Generation for Realistic Antibody
  Design},
    author={Nayoung Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park},
    journal={arXiv preprint arXiv:2402.05982},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05982},
    primaryClass={q-bio.QM cs.LG}
}