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

Research Bioinformatics AI

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Merged summary

TL;DR - AAMFM jointly models antibody sequences, structures, and antigen context to design antibodies for specific targets. It matters because antigen and epitope conditioning may improve the functional relevance of generated antibodies.

  • Uses a cross-modal adapter to incorporate antigen interfaces and epitope annotations.
  • Represents antibody-antigen interactions in a shared latent space.
  • Applies Calibrated Direct Preference Optimization using structural-prior preference signals.
  • The authors report state-of-the-art functional antibody design performance and provide open-source code.

Sources (1)

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

arXiv q-bio.BM Xiaoliang Shi, Zichen Wang, Runze Ma, Zhongyue Zhang, Shuangjia Zheng 2026-07-22 arXiv:2607.20057
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-10 02:54:44.724899 UTC

TL;DR - AAMFM jointly models antibody sequences, structures, and antigen context to design antibodies for specific targets. It matters because antigen and epitope conditioning may improve the functional relevance of generated antibodies.

  • Uses a cross-modal adapter to incorporate antigen interfaces and epitope annotations.
  • Represents antibody-antigen interactions in a shared latent space.
  • Applies Calibrated Direct Preference Optimization using structural-prior preference signals.
  • The authors report state-of-the-art functional antibody design performance and provide open-source code.
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