ArticleBMC bioinformatics2023
DG-Affinity: predicting antigen-antibody affinity with language models from sequences.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Phys-AbGAT: a physics-informed multi-task graph attention network for robust antibody-antigen binding affinity prediction.Bioinformatics (Oxford, England) · 2026Article
- A systematic evaluation framework for universal antibody-antigen binding affinity prediction and candidate recommendation.iScience · 2026Article
- AbAgKer: a unified semi-supervised framework for antigen-antibody binding affinity and kinetics prediction.Bioinformatics (Oxford, England) · 2026Article
- Predicting the evolutionary and functional landscapes of viruses with a unified nucleotide-protein language model: LucaVirus.National science review · 2026Article
- Comparative analysis of anti-MICA scFv affinities: Insights from three label-free biophysical methods and biological validation.Biotechnology reports (Amsterdam, Netherlands) · 2026Article
- Antibody-antigen neutralization prediction by integrating structural information distillation and physicochemical constraints.Briefings in bioinformatics · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Advances in Therapeutic Antibody Discovery and Development Targeting G Protein-Coupled Receptors.Pharmacology research & perspectives · 2026Review
- Structure-Guided Engineering of High-Affinity Antibodies Against Zika Virus Using Deep Learning and Molecular Dynamics.Chemistry & biodiversity · 2026Article
- Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes.Nature cancer · 2025Article
- Bio-Inspired Mamba for Antibody-Antigen Interaction Prediction.Biomolecules · 2025Article
- Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin.Computational and structural biotechnology journal · 2025Article
- PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery.Scientific data · 2024Article
- Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies.PNAS nexus · 2024Article
- AntiBinder: utilizing bidirectional attention and hybrid encoding for precise antibody-antigen interaction prediction.Briefings in bioinformatics · 2024Article
- Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.BMC bioinformatics · 2024Article
- rAbDesFlow: a novel workflow for computational recombinant antibody design for healthcare engineering.Antibody therapeutics · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundAntibody-mediated immune responses play a crucial role in the immune defense of human body. The evolution of bioengineering has led the progress of antibody-derived drugs, showing promising efficacy in cancer and autoimmune disease therapy. A critical step of this development process is obtaining the affinity between antibodies and their binding antigens.
resultsIn this study, we introduce a novel sequence-based antigen-antibody affinity prediction method, named DG-Affinity. DG-Affinity uses deep neural networks to efficiently and accurately predict the affinity between antibodies and antigens from sequences, without the need for structural information. The sequences of both the antigen and the antibody are first transformed into embedding vectors by two pre-trained language models, then these embeddings are concatenated into an ConvNeXt framework with a regression task. The results demonstrate the superiority of DG-Affinity over the existing structure-based prediction methods and the sequence-based tools, achieving a Pearson's correlation of over 0.65 on an independent test dataset.
conclusionsCompared to the baseline methods, DG-Affinity achieves the best performance and can advance the development of antibody design. It is freely available as an easy-to-use web server at https://www.digitalgeneai.tech/solution/affinity .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.