ArticleBioinformatics (Oxford, England)2022
DLAB: deep learning methods for structure-based virtual screening of antibodies.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed, 97 citations in OpenAlex.
- Article
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Artificial intelligence-driven computational methods for antibody design and optimization.mAbs · 2025Review
- Technologies for Monoclonal Antibody Discovery and Development.International journal of molecular sciences · 2025Review
- Integrative and Emerging Models in Antibody Research: A Comprehensive Review.Antibody therapeutics · 2025Review
- RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- NanoDeNovo: De Novo Design of Anti-Poliovirus I Sabin Strain Nanobodies by Semi-Automated Computational Pipeline.International journal of molecular sciences · 2025Article
- Antibody-Drug Conjugates in Breast Cancer: Navigating Innovations, Overcoming Resistance, and Shaping Future Therapies.Biomedicines · 2025Review
- Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery.EJNMMI research · 2025Article
- Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development.MedComm · 2025Review
- NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores.Journal of cheminformatics · 2025Article
- Bio-Inspired Mamba for Antibody-Antigen Interaction Prediction.Biomolecules · 2025Article
- Nanobodies: From Discovery to AI-Driven Design.Biology · 2025Review
- 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
- RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery.bioRxiv : the preprint server for biology · 2025Article
- Protein recognition methods for diagnostics and therapy.BBA advances · 2025Review
- Computational electrostatic engineering of nanobodies for enhanced SARS-CoV-2 receptor binding domain recognition.Frontiers in molecular biosciences · 2025Article
- The Application of Machine Learning on Antibody Discovery and Optimization.Molecules (Basel, Switzerland) · 2024Review
- Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.BMC bioinformatics · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
motivationAntibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in vivo and in vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders.
resultsWe demonstrate that DLAB can be used both to improve antibody-antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody-antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies. AVAILABILITY AND IMPLEMENTATION: The DLAB source code and pre-trained models are available at https://github.com/oxpig/dlab-public. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
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.