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DeepRank-Ab: a scoring function for antibody-antigen complexes based on geometric deep learning
Xiaotong Xu1, Ilaria Coratella1, Victor Reys1
1Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, CH Utrecht, The Netherlands.
Communications Biology
|June 2, 2026
Summary
DeepRank-Ab, a new deep learning scoring function, significantly improves the accuracy of modeling antibody-antigen interactions. It outperforms existing methods like AlphaFold3 in identifying correct complex structures.
Area of Science:
- Structural biology
- Immunology
- Computational chemistry
Background:
- Accurate modeling of antibody-antigen interactions is crucial for understanding immune responses and designing therapeutics.
- Current computational methods, including AI-based approaches like AlphaFold3, face challenges in generating and ranking near-native conformations of these complexes.
Purpose of the Study:
- To develop a novel, highly accurate scoring function for antibody-antigen complex modeling.
- To address the limitations of existing methods in predicting the structural accuracy of antibody-antigen interfaces.
Main Methods:
- Developed DeepRank-Ab, a geometric deep learning-based scoring function specifically designed for antibody-antigen interfaces.
- Created a large, diverse benchmark dataset of approximately 2.3 million decoys from 1,442 complexes for training and evaluation.
- Systematically evaluated various graph representations, structural/energetic features, and sampling strategies, identifying atom-level representations with Voronoi-based surface decomposition and antibody-specific features as optimal.
Main Results:
- DeepRank-Ab consistently outperformed AlphaFold3, HADDOCK, and other state-of-the-art scoring functions across multiple independent test sets.
- Achieved a 35.5% increase in AlphaFold3's Top1 success rate and more than doubled the mean Top1 DockQ score.
- Demonstrated strong generalization capabilities, achieving a 100% Top5 success rate on external antibody-antigen CAPRI targets.
Conclusions:
- DeepRank-Ab represents a significant advancement in scoring antibody-antigen complex models.
- The method substantially improves the identification of near-native antibody-antigen conformations, offering a powerful tool for structural biology and drug discovery.
