ArticlemAbs
Predicting antibody binders and generating synthetic antibodies using deep learning.
Article in mAbs. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 43 citations in OpenAlex.
- Multiobjective VScience advances · 2026Article
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- Scaling antibody language models improves structure aware representation for antibody engineering.Communications biology · 2026Article
- Benchmarking antigen-aware inverse folding methods for antibody design.Bioinformatics advances · 2026Article
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Article
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- Review
- 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
- Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances.Molecular biotechnology · 2025Review
- Computational electrostatic engineering of nanobodies for enhanced SARS-CoV-2 receptor binding domain recognition.Frontiers in molecular biosciences · 2025Article
- Competitive Epitope Binning Using HT-SPR.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Applying computational protein design to therapeutic antibody discovery - current state and perspectives.Frontiers in immunology · 2025Review
- Towards AI-designed genomes using a variational autoencoder.Proceedings. Biological sciences · 2024Article
- NABP-BERT: NANOBODY®-antigen binding prediction based on bidirectional encoder representations from transformers (BERT) architecture.Briefings in bioinformatics · 2024Article
- Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.BMC bioinformatics · 2024Article
- Recent Advances in Artificial Intelligence to Improve Immunotherapy and the Use of Digital Twins to Identify Prognosis of Patients with Solid Tumors.International journal of molecular sciences · 2024Review
- Monoclonal antibodies: From magic bullet to precision weapon.Molecular biomedicine · 2024Review
- Antibody design using deep learning: from sequence and structure design to affinity maturation.Briefings in bioinformatics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The antibody drug field has continually sought improvements to methods for candidate discovery and engineering. Historically, most such methods have been laboratory-based, but informatics methods have recently started to make an impact. Deep learning, a subfield of machine learning, is rapidly gaining prominence in the biomedical research. Recent advances in microfluidics technologies and next-generation sequencing have not only revolutionized therapeutic antibody discovery, but also contributed to a vast amount of antibody repertoire sequencing data, providing opportunities for deep learning-based applications. Previously, we used microfluidics, yeast display, and deep sequencing to generate a panel of binder and non-binder antibody sequences to the cancer immunotherapy targets PD-1 and CTLA-4. Here we encoded the antibody light and heavy chain complementarity-determining regions (CDR3s) into antibody images, then built and trained convolutional neural network models to classify binders and non-binders. To improve model interpretability, we performed
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What OpenQuestion holds
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.