AbSpecAlign: Specificity Reward Alignment for Antigen-Conditioned Antibody Generation

Designing antibodies that recognize a target antigen while avoiding non-cognate antigens remains a central challenge in computational antibody engineering. Existing antigen-conditioned generative models can jointly design antibody sequences and structures, but they are typically trained to match structural data distributions and lack an explicit alignment mechanism for antigen-specific preferences. We present AbSpecAlign, a plug-and-play specificity-reward alignment framework for antibody design. AbSpecAlign introduces a Bidirectional Multimodal Specificity Scorer (BiMSS), which integrates antibody and antigen sequence representations with SE(3)-equivariant structural embeddings to produce a contrastive reward over cognate versus non-cognate antigen pairs. We further introduce a GRPO-based post-training strategy that uses group-relative specificity rewards to align pre-trained antibody generators toward higher-scoring antigen-conditioned designs while maintaining structural regularity. To the best of our knowledge, AbSpecAlign is among the first frameworks to apply GRPO-style group-relative reward alignment to antigen-conditioned antibody sequence--structure co-design with an explicit off-target specificity reward. Experiments on RAbD CDR design benchmarks show that AbSpecAlign improves most sequence-recovery and structural-fidelity metrics when combined with diffusion-based antibody generators. Experiment results suggest that GRPO-based reward alignment with contrastive specificity signals provides an effective post-training mechanism for antigen-conditioned antibody generation, while prospective experimental validation remains necessary.