Related papers: AbFlow : End-to-end Paratope-Centric Antibody Design by Interaction Enhanced Flow Matching

AbFlow : End-to-end Paratope-Centric Antibody Design by Interaction Enhanced Flow Matching

URL: http://arxiv.org/abs/2602.07084v1
Date: Fri, 06 Feb 2026 07:47:59 GMT
Title: AbFlow : End-to-end Paratope-Centric Antibody Design by Interaction Enhanced Flow Matching
Authors: Wenda Wang, Yang Zhang, Zhewei Wei, Wenbing Huang,
Abstract summary: AbFlow is a flow-matching framework that leverages optimal transport to design full-atom antibodies end-to-end.<n>It produces superior antigen-antibody complexes, especially at the contact interface, and markedly improves the binding affinity of generated antibodies.
Score: 41.128304772190454
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Antigen-antibody binding is a critical process in the immune response. Although recent progress has advanced antibody design, current methods lack a generative framework for end-to-end modeling of full-atom antibody structures and struggle to fully exploit antigen-specific geometric information for optimizing local binding interfaces and global structures. To overcome these limitations, we introduce AbFlow, a flow-matching framework that leverages optimal transport to design full-atom antibodies end-to-end. AbFlow incorporates an extended velocity field network featuring an equivariant Surface Multi-channel Encoder, which uses surface-level antigen interaction data to refine the antibody structure, particularly the CDR-H3 region. Extensive experiments in paratoep-centric antibody design, multi-CDRs and full-atom antibody design, binding affinity optimization, and complex structure prediction show that AbFlow produces superior antigen-antibody complexes, especially at the contact interface, and markedly improves the binding affinity of generated antibodies.

Related papers

Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding [13.315597171727095]
We propose textbfAbMEGD, an end-to-end framework for antibody sequence and structure co-design.<n>AbMEGD combines atomic-level geometric features with residue-level embeddings, capturing local atomic details and global sequence-structure interactions.<n>Experiments using the SAbDab database demonstrate a 10.13% increase in amino acid recovery, 3.32% rise in improvement percentage, and a 0.062AA reduction in root mean square deviation.
arXiv Detail & Related papers (2025-06-26T02:45:38Z)
dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen [52.809470467635194]
Development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies.<n>Existing computational methods in antibody design often overlook crucial conformational changes that antigens undergo during the binding process.<n>We introduce dyAb, a flexible framework that incorporates AlphaFold2-driven predictions to model pre-binding antigen structures.
arXiv Detail & Related papers (2025-03-01T03:53:18Z)
Towards More Accurate Full-Atom Antibody Co-Design [44.06939390661133]
Co-design represents a critical frontier in drug development, where accurate prediction of structure of complementarity-determining regions is essential for targeting specific equivariants.<n>Despite recent advances in graph neural networks for antibody design, current approaches often fall short in capturing the intricate interactions that govern antibody-antigen recognition and binding specificity.<n>We present Igformer, a novel end-to-end framework that addresses these limitations through personalized antibody-antigen binding interfaces.
arXiv Detail & Related papers (2025-02-11T13:33:28Z)
Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization [61.06622479173572]
We propose a novel Relation-Aware Design (RAAD) framework, which models antigen-antibody interactions for co-designing sequences and structures of antigen-specific CDRs.<n> Furthermore, we propose a new evaluation metric to better measure antibody specificity and develop a contrasting specificity-enhancing constraint to optimize the specificity of antibodies.
arXiv Detail & Related papers (2024-12-14T03:00:44Z)
Efficient Antibody Structure Refinement Using Energy-Guided SE(3) Flow Matching [16.192361788505558]
FlowAB is a novel antibody structure refinement method based on energy-guided flow matching. It achieves new state-of-the-art performance on the antibody structure prediction task when used in conjunction with an appropriate prior model.
arXiv Detail & Related papers (2024-10-22T04:13:55Z)
Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization [51.28231365213679]
We tackle antigen-specific antibody sequence-structure co-design as an optimization problem towards specific preferences. We propose direct energy-based preference optimization to guide the generation of antibodies with both rational structures and considerable binding affinities to given antigens.
arXiv Detail & Related papers (2024-03-25T09:41:49Z)
A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design [54.30457372514873]
We propose a hierarchical training paradigm (HTP) for the antibody sequence-structure co-design. HTP consists of four levels of training stages, each corresponding to a specific protein modality. Empirical experiments show that HTP sets the new state-of-the-art performance in the co-design problem.
arXiv Detail & Related papers (2023-10-30T02:39:15Z)
xTrimoABFold: De novo Antibody Structure Prediction without MSA [77.47606749555686]
We develop a novel model named xTrimoABFold to predict antibody structure from antibody sequence. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame-aligned point loss.
arXiv Detail & Related papers (2022-11-30T09:26:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.