Go to ICLR 2026 Workshop GEM homepageAntigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design
Keywords: Multi-modal Language Model, Generative Model, Antibody Design
TL;DR: We introduce an antigen-specific antibody multimodal foundation model for unified antibody design.
Abstract: Antibodies play a key role in immune recognition by binding specific antigens. 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 $\textbf{A}$ntigen-specific $\textbf{A}$ntibody $\textbf{M}$ultimodal $\textbf{F}$oundation $\textbf{M}$odel 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 (AlphaFold3) 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://anonymous.4open.science/r/AAMFM.
Presenter: ~Zichen_Wang15
Format: Maybe: the presenting author will attend in person, contingent on other factors that still need to be determined (e.g., visa, funding).
Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.
Submission Number: 12
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