ReviewBriefings in bioinformatics2022
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 citing papers in PubMed.
- Article
- Trial Watch - bispecific T cell engagers and higher-order multispecific immunotherapeutics.Oncoimmunology · 2026Review
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- Context-aware multi-property antibody predictor: a novel framework integrating text and protein language models.NPJ systems biology and applications · 2026Article
- Computational models for the classification of antibody specificity using heavy chain features.PloS one · 2026Article
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- Optimizing the breadth of SARS-CoV-2-neutralizing antibodies in vivo and in silico.Human vaccines & immunotherapeutics · 2025Review
- Article
- Elucidating the Mechanisms of Chrysanthemum Action on Atopic Dermatitis via Network Pharmacology and Machine Learning.International journal of molecular sciences · 2025Article
- RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Integrative and Emerging Models in Antibody Research: A Comprehensive Review.Antibody therapeutics · 2025Review
- Enhancing antibody-antigen interaction prediction with atomic flexibility.PLoS computational biology · 2025Article
- Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development.MedComm · 2025Review
- 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
- A synthetic heavy chain variable domain antibody library (VHL) provides highly functional antibodies with favorable developability.Protein science : a publication of the Protein Society · 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
- Nanobody engineering: computational modelling and design for biomedical and therapeutic applications.FEBS open bio · 2025Review
- Competitive Epitope Binning Using HT-SPR.Methods in molecular biology (Clifton, N.J.) · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
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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.