ArticleBioinformatics (Oxford, England)2020
Antibody complementarity determining region design using high-capacity machine learning.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
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Who cites it
79 citing papers in PubMed.
- Multidimensional maturation of antibody variable domains with machine-learning assistance.mAbs · 2026Article
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Machine learning enables efficient and effective affinity maturation of nanobodies.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Analysing open-source protein folding models for nanobody binding prediction.Frontiers in bioinformatics · 2026Article
- Article
- Technologies for Monoclonal Antibody Discovery and Development.International journal of molecular sciences · 2025Review
- RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Production Technologies for Recombinant Antibodies: Insights into Eukaryotic, Prokaryotic, and Transgenic Expression Systems.Biochemical genetics · 2025Review
- Deep learning in next-generation vaccine development for infectious diseases.Molecular therapy. Nucleic acids · 2025Review
- Using extension-based mRNA display to design antibody-like proteinogenic peptides for human PD-L1.Protein science : a publication of the Protein Society · 2025Article
- Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes.Nature cancer · 2025Article
- PRO-LDM: A Conditional Latent Diffusion Model for Protein Sequence Design and Functional Optimization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Advances in Techniques for the Structure and Functional Optimization of Therapeutic Monoclonal Antibodies.Biomedicines · 2025Review
- Article
- Recent advances in antibody optimization based on deep learning methods.Journal of Zhejiang University. Science. B · 2025Review
- Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning.Journal of biomedical science · 2025Review
- 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
19 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
motivationThe precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties.
resultsHere, we present Ens-Grad, a machine learning method that can design complementarity determining regions of human Immunoglobulin G antibodies with target affinities that are superior to candidates derived from phage display panning experiments. We also demonstrate that machine learning can improve target specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data. AVAILABILITY AND IMPLEMENTATION: Sequencing data of the phage panning experiment are deposited at NIH's Sequence Read Archive (SRA) under the accession number SRP158510. We make our code available at https://github.com/gifford-lab/antibody-2019. 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.