ArticleNature communications2023
Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Article
- Multidimensional maturation of antibody variable domains with machine-learning assistance.mAbs · 2026Article
- Computational design and translational development of TCR-mimic scFv antibodies for peptide-HLA neoantigen targeting.npj drug discovery · 2026Review
- Antibody Affinity Maturation by Yeast Mating against a Diverse Antigen Library Enhances Antibody Resistance to Viral Mutations.ACS pharmacology & translational science · 2026Article
- Multiobjective VScience advances · 2026Article
- A systematic evaluation framework for universal antibody-antigen binding affinity prediction and candidate recommendation.iScience · 2026Article
- Heavy-chain immune repertoire sequencing enables language-model prediction of antigen-specific antibodies.Research square · 2026Article
- Comparative analysis of anti-MICA scFv affinities: Insights from three label-free biophysical methods and biological validation.Biotechnology reports (Amsterdam, Netherlands) · 2026Article
- From Single Cells to Silicon: Emerging Technologies Transforming Monoclonal Antibody Discovery.Antibodies (Basel, Switzerland) · 2026Review
- Rapid directed evolution guided by protein language models and epistatic interactions.Science (New York, N.Y.) · 2026Article
- De novo design of functional nucleic acids of aptamers.Nature computational science · 2026Article
- Phage Display Technology: Design, Library Construction, and Panning Strategies for Antibody Development.Biomolecules & therapeutics · 2026Review
- Interpretable machine learning rationalizes carbonic anhydrase inhibition via conformal and counterfactual prediction.Scientific reports · 2026Article
- Machine learning enables efficient and effective affinity maturation of nanobodies.bioRxiv : the preprint server for biology · 2026Article
- AI-Driven De Novo Binder Design: From Structure Prediction to Closed-Loop Optimization.Computational and structural biotechnology journal · 2026Review
- Explore antibody repertoire in the era of AI.Acta biochimica et biophysica Sinica · 2025Article
- A New Pulsed Neutron-Gamma Density Logging Method Based on Gamma-Ray Spectra and Machine Learning.ACS omega · 2025Article
- Ion channel inhibition by targeted recruitment of NEDD4-2 with divalent nanobodies.Nature communications · 2025Article
- Artificial intelligence-driven computational methods for antibody design and optimization.mAbs · 2025Review
- Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an end-to-end Bayesian, language model-based method for designing large and diverse libraries of high-affinity single-chain variable fragments (scFvs) that are then empirically measured. In a head-to-head comparison with a directed evolution approach, we show that the best scFv generated from our method represents a 28.7-fold improvement in binding over the best scFv from the directed evolution. Additionally, 99% of designed scFvs in our most successful library are improvements over the initial candidate scFv. By comparing a library's predicted success to actual measurements, we demonstrate our method's ability to explore tradeoffs between library success and diversity. Results of our work highlight the significant impact machine learning models can have on scFv development. We expect our method to be broadly applicable and provide value to other protein engineering tasks.
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