Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design

• URL: http://arxiv.org/abs/2607.20057v1
• Date: Wed, 22 Jul 2026 11:59:38 GMT
• Title: Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design
• Abstract summary: AAMFM is an Antigen-specific Multimodal Foundation Model that learns unified representations of antibody sequences and structures conditioned on antigen context.<n>AAMFM achieves state-of-the-art performance in functional antibody design, revealing its potential for antigen-specific antibody engineering.
• Score: 20.26254287473861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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