ArticleiScience2025
Integrated antibody language model accelerates IgG screening and design for broad-spectrum antiviral therapy.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- An outlook on structural biology after AlphaFold: tools, limits and perspectives.FEBS open bio · 2025Review
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Authors and funding
21 authors.
Funding
Abstract
Identifying highly efficacious, broad-spectrum antibodies against fast-mutating viral variants remains a major challenge in therapeutic development. Here, we developed AbGen, a machine learning-assisted antibody generation pipeline powered by an antibody language model (AbLM), to accelerate antibody screening and re-design. AbLM, pretrained on protein domain sequences and fine-tuned on paired VH-VL sequences, enables the analysis and prediction of neutralization activity against viruses (specifically SARS-CoV-2 in this study), targeting both wild-type (through antigen interaction prediction [docking]) and emerging variants (through Gaussian process regression [Kriging]). Screening over 1300 RBD-binding IgG sequences from convalescent patients, AbGen efficiently prioritized candidates for experimental validation and/or redesign against wild-type, Delta, and Omicron variants, preventing viral infections
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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.