ArticleNature communications2022
Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
93 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based meta-analysis of colorectal cancer and inflammatory bowel disease.PloS one · 2023Pooled it
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- Predicting non-specific binding of VHHs using machine learning models with cluster-aware validation.mAbs · 2026Article
- ASD: antigen-specific antibody database.mAbs · 2026Article
- Multiobjective VScience advances · 2026Article
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- A Synthetic Platform for Antibody Junctional Diversification Beyond Natural Constraints.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Humanized Anti-PD-1 Antibodies Generated Using The Conditional Kernel-Elastic Autoencoder.bioRxiv : the preprint server for biology · 2026Article
- Using enantioselective biosensors to evolve asymmetric biocatalysts.Nature chemical biology · 2026Article
- CD98hc-targeted antibody shuttles for central nervous system delivery with broad cross-species reactivity.Nature biomedical engineering · 2026Article
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 2026Review
- Impact of heme on the therapeutic efficacy of anti-CD20 antibodies.The Journal of biological chemistry · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- BCRInsight: an antibody language model to decode biological signals from BCR sequences.Briefings in bioinformatics · 2026Article
- Antibody screening for tumor and immune hotspot targets: The frontier of new methods and technologies.Journal of pharmaceutical analysis · 2026Review
- Characterising nanobody developability to improve therapeutic design using the Therapeutic Nanobody Profiler.Communications biology · 2026Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.
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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.