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This paper introduces AAMFM, a novel Antigen-specific Antibody Multimodal Foundation Model that effectively models the interactions between antibodies and antigens by learning unified representations conditioned on antigen context. By integrating detailed antigen information, such as geometric interfaces and epitope annotations, AAMFM enables joint modeling of antibody-antigen interactions in a shared latent space. The model's performance is further enhanced through fine-tuning with Calibrated Direct Preference Optimization, resulting in state-of-the-art outcomes in functional antibody design, which is crucial for advancing targeted therapeutic strategies.
AAMFM achieves unprecedented accuracy in functional antibody design by effectively pairing antibody sequences with antigen contexts, setting a new benchmark in the field.
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. To address these limitations, we introduce AAMFM, an Antigen-specific Antibody Multimodal Foundation Model that learns unified representations of antibody sequences and structures conditioned on antigen context. AAMFM incorporates rich antigen information including geometric interfaces and epitope annotations via a cross-modal adapter, enabling joint modeling of antibody-antigen interactions in a shared latent space. To further guide the model toward functional relevance, we fine-tune AAMFM using Calibrated Direct Preference Optimization (Cal-DPO), leveraging preference signals extracted from a strong structural prior to align learning with binding-specific objectives. Extensive experiments demonstrate that AAMFM achieves state-of-the-art performance in functional antibody design, revealing its potential for antigen-specific antibody engineering. Our code is available at https://github.com/XL-S224/AAMFM.
