ArticleNature communications2023
The RESP AI model accelerates the identification of tight-binding antibodies.
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 30 papers.
What it found
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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.
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Who cites it
30 citing papers in PubMed, 47 citations in OpenAlex.
- Antibody Affinity Maturation by Yeast Mating against a Diverse Antigen Library Enhances Antibody Resistance to Viral Mutations.ACS pharmacology & translational science · 2026Article
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability.Nature biotechnology · 2026Article
- Detection of Protein-Protein Interactions in Escherichia coli With Single Molecule Sensitivity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 2026Review
- DGSite: A Well-Calibrated Deep Learning Framework for Protein-RNA Binding Site Prediction.ACS omega · 2026Article
- Advances in Therapeutic Antibody Discovery and Development Targeting G Protein-Coupled Receptors.Pharmacology research & perspectives · 2026Review
- Technological advancements in antibody-based therapeutics for treatment of diseases.Journal of biomedical science · 2025Review
- Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects.Biosensors · 2025Review
- RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Using extension-based mRNA display to design antibody-like proteinogenic peptides for human PD-L1.Protein science : a publication of the Protein Society · 2025Article
- Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning.Journal of biomedical science · 2025Review
- An expandable synthetic library of human paired antibody sequences.PLoS computational biology · 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
- AI-driven antibody design with generative diffusion models: current insights and future directions.Acta pharmacologica Sinica · 2025Review
- Rapid restoration of potent neutralization activity against the latest Omicron variant JN.1 via AI rational design and antibody engineering.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Integrating Computational Design and Experimental Approaches for Next-Generation Biologics.Biomolecules · 2024Review
- TUnA: an uncertainty-aware transformer model for sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representation trained on over 3 million human B-cell receptor sequences to encode antibody sequences. We then develop a variational Bayesian neural network to perform ordinal regression on a set of the directed evolution sequences binned by off-rate and quantify their likelihood to be tight binders against an antigen. Importantly, this model can assess sequences not present in the directed evolution library and thus greatly expand the search space to uncover the best sequences for experimental evaluation. We demonstrate the power of this pipeline by achieving a 17-fold improvement in the K
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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.