Accurate ranking of antibody candidates according to their binding affinity is essential for therapeutic antibody discovery. However, existing methods treat affinity comparisons independently and ignore the contextual information encoded in other labeled comparisons, limiting their ability to capture antigen-specific binding landscapes.
arXiv:2607. 05846v1 Announce Type: cross Abstract: Accurate ranking of antibody candidates according to their binding affinity is essential for therapeutic antibody discovery.
By Zhiyuan Chen, Jing Hu, Junzhe Wang, Yueyang Huang, Xinyi Yang, Zhaoyang Wang, Feng Zhu
arXiv:2609.00518v1 Announce Type: new
Abstract: Antibody-specific language models pretrained via masked language modeling (MLM) learn representations that are critical for downstream sequence design...
By Ayan Goel, Thomas A. Walton, Amirali Aghazadeh
The paper introduces a flexible sampling technique for masked language models (MLMs) called stochastic beam search, which leverages MLMs’ efficiency in evaluating the pseudo‑perplexity of a sequence’s 1‑edit neighborhood. This method reframes generation as whole‑sequence evaluation, allowing guidance across multiple optimization objectives. Extensive in‑silico and in‑vitro tests on antibody therapeutics demonstrate that the sampling strategy significantly influences outcomes, highlighting the need for further research in this area.
By Calvin McCarter, Nick Bhattacharya, Sebastian W. Ober, Hunter Elliott
Antibodies are essential proteins that play a central role in immune recognition by binding specific antigen molecules. Although recent protein language models have enabled progress in single-chain protein modeling and generation, they often fall short in antigen-specific antibody design, where effective modeling requires explicit pairing between antibody and antigen, particularly at the epitope level.
arXiv:2607. 20057v1 Announce Type: cross Abstract: Antibodies are essential proteins that play a central role in immune recognition by binding specific antigen molecules.
By Xiaoliang Shi, Zichen Wang, Runze Ma, Zhongyue Zhang, Shuangjia Zheng