ArticleBioinformatics (Oxford, England)2024
AbFlex: designing antibody complementarity determining regions with flexible CDR definition.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 2026Review
- GeoGAD: geometry-aware antibody design framework for complementarity-determining region precision engineering.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Enhancing antibody-antigen interaction prediction with atomic flexibility.PLoS computational biology · 2025Article
- AntiBMPNN: Structure-Guided Graph Neural Networks for Precision Antibody Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores.Journal of cheminformatics · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationAntibodies are proteins that the immune system produces in response to foreign pathogens. Designing antibodies that specifically bind to antigens is a key step in developing antibody therapeutics. The complementarity determining regions (CDRs) of the antibody are mainly responsible for binding to the target antigen, and therefore must be designed to recognize the antigen.
resultsWe develop an antibody design model, AbFlex, that exhibits state-of-the-art performance in terms of structure prediction accuracy and amino acid recovery rate. Furthermore, >38% of newly designed antibody models are estimated to have better binding energies for their antigens than wild types. The effectiveness of the model is attributed to two different strategies that are developed to overcome the difficulty associated with the scarcity of antibody-antigen complex structure data. One strategy is to use an equivariant graph neural network model that is more data-efficient. More importantly, a new data augmentation strategy based on the flexible definition of CDRs significantly increases the performance of the CDR prediction model. AVAILABILITY AND IMPLEMENTATION: The source code and implementation are available at https://github.com/wsjeon92/AbFlex.
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Registered trials
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