ArticleFrontiers in immunology2022
AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 52 citations in OpenAlex.
- Evidence-aware comparison of sequence-centric machine learning for antibody discovery and optimization.Briefings in bioinformatics · 2026Article
- Nanoyeast-based impedimetric biosensor with mutated single chain antigen-binding fragment anchoring for SARS-CoV-2 detection.Biomedical microdevices · 2026Article
- Antibody-antigen neutralization prediction by integrating structural information distillation and physicochemical constraints.Briefings in bioinformatics · 2026Article
- Advances in Therapeutic Antibody Discovery and Development Targeting G Protein-Coupled Receptors.Pharmacology research & perspectives · 2026Review
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes.Nature cancer · 2025Article
- Advances in Techniques for the Structure and Functional Optimization of Therapeutic Monoclonal Antibodies.Biomedicines · 2025Review
- NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores.Journal of cheminformatics · 2025Article
- Insights into next-generation immunotherapy designs and tools: molecular mechanisms and therapeutic prospects.Journal of hematology & oncology · 2025Review
- Intestinal antibody repertoire is altered by diabetes and varies depending on the pathogenesis.Biochemistry and biophysics reports · 2025Article
- RLEAAI: improving antibody-antigen interaction prediction using protein language model and sequence order information.Briefings in bioinformatics · 2025Article
- Design of nanobody targeting SARS-CoV-2 spike glycoprotein using CDR-grafting assisted by molecular simulation and machine learning.PLoS computational biology · 2025Article
- DeepInterAware: Deep Interaction Interface-Aware Network for Improving Antigen-Antibody Interaction Prediction from Sequence Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- ABTrans: A Transformer-based Model for Predicting Interaction between Anti-Aβ Antibodies and Peptides.Interdisciplinary sciences, computational life sciences · 2025Article
- Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin.Computational and structural biotechnology journal · 2025Article
- Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies.PNAS nexus · 2024Article
- NABP-BERT: NANOBODY®-antigen binding prediction based on bidirectional encoder representations from transformers (BERT) architecture.Briefings in bioinformatics · 2024Article
- AntiBinder: utilizing bidirectional attention and hybrid encoding for precise antibody-antigen interaction prediction.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in antibody therapeutics is the experimental identification of antibody-antigen interactions, which is generally time-consuming, costly, and laborious. Although some computational methods have been proposed to screen potential antibodies, the dependence on 3D structures still limits the application of these methods. Methods: Here, we developed a deep learning-assisted prediction method (i.e., AbAgIntPre) for fast identification of antibody-antigen interactions that only relies on amino acid sequences. A Siamese-like convolutional neural network architecture was established with the amino acid composition encoding scheme for both antigens and antibodies. Results and Discussion: The generic model of AbAgIntPre achieved satisfactory performance with the Area Under Curve (AUC) of 0.82 on a high-quality generic independent test dataset. Besides, this approach also showed competitive performance on the more specific SARS-CoV dataset. We expect that AbAgIntPre can serve as an important complement to traditional experimental methods for antibody screening and effectively reduce the workload of antibody design. The web server of AbAgIntPre is freely available at http://www.zzdlab.com/AbAgIntPre.
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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.