ArticleMethods in molecular biology (Clifton, N.J.)2022
Deep Learning in Therapeutic Antibody Development.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Computational electrostatic engineering of nanobodies for enhanced SARS-CoV-2 receptor binding domain recognition.Frontiers in molecular biosciences · 2025Article
- Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.Bioengineering (Basel, Switzerland) · 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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning applied to antibody development is in its adolescence. Low data volumes and biological platform differences make it challenging to develop supervised models that can predict antibody behavior in actual commercial development steps. But successes in modeling general protein behaviors and early antibody models give indications of what is possible for antibodies in general, particularly since antibodies share a common fold. Meanwhile, new methods of data collection and the development of unsupervised and self-supervised deep learning methods like generative models and masked language models give the promise of rich and deep data sets and deep learning architectures for better supervised model development. Together, these move the industry toward improved developability , lower costs, and broader access of biotherapeutics .
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Identifiers
34731481What 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.