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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.19391 |
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| _version_ | 1866910846449352704 |
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| author | Wu, Jiayang Zhang, Xingyi Dong, Xiangyu Xie, Kun Liu, Ziqi Gan, Wensheng Wang, Sibo Song, Le |
| author_facet | Wu, Jiayang Zhang, Xingyi Dong, Xiangyu Xie, Kun Liu, Ziqi Gan, Wensheng Wang, Sibo Song, Le |
| contents | Antibody co-design represents a critical frontier in drug development, where accurate prediction of both 1D sequence and 3D structure of complementarity-determining regions (CDRs) is essential for targeting specific epitopes. Despite recent advances in equivariant graph neural networks for antibody design, current approaches often fall short in capturing the intricate interactions that govern antibody-antigen recognition and binding specificity. In this work, we present Igformer, a novel end-to-end framework that addresses these limitations through innovative modeling of antibody-antigen binding interfaces. Our approach refines the inter-graph representation by integrating personalized propagation with global attention mechanisms, enabling comprehensive capture of the intricate interplay between local chemical interactions and global conformational dependencies that characterize effective antibody-antigen binding. Through extensive validation on epitope-binding CDR design and structure prediction tasks, Igformer demonstrates significant improvements over existing methods, suggesting that explicit modeling of multi-scale residue interactions can substantially advance computational antibody design for therapeutic applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19391 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards More Accurate Full-Atom Antibody Co-Design Wu, Jiayang Zhang, Xingyi Dong, Xiangyu Xie, Kun Liu, Ziqi Gan, Wensheng Wang, Sibo Song, Le Biomolecules Machine Learning Antibody co-design represents a critical frontier in drug development, where accurate prediction of both 1D sequence and 3D structure of complementarity-determining regions (CDRs) is essential for targeting specific epitopes. Despite recent advances in equivariant graph neural networks for antibody design, current approaches often fall short in capturing the intricate interactions that govern antibody-antigen recognition and binding specificity. In this work, we present Igformer, a novel end-to-end framework that addresses these limitations through innovative modeling of antibody-antigen binding interfaces. Our approach refines the inter-graph representation by integrating personalized propagation with global attention mechanisms, enabling comprehensive capture of the intricate interplay between local chemical interactions and global conformational dependencies that characterize effective antibody-antigen binding. Through extensive validation on epitope-binding CDR design and structure prediction tasks, Igformer demonstrates significant improvements over existing methods, suggesting that explicit modeling of multi-scale residue interactions can substantially advance computational antibody design for therapeutic applications. |
| title | Towards More Accurate Full-Atom Antibody Co-Design |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2502.19391 |