ArticleiScience2026
Deep generative modeling captures maturation-dependent pairing patterns in human antibodies.
Article in iScience, 2026. 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.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- H3BERTa: A CDR-H3-specific language model for antibody repertoire analysis.Patterns (New York, N.Y.) · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding antibody heavy-light chain pairing is critical for decoding immune repertoire architecture and designing therapeutic antibodies, yet most sequence databases lack paired chain information. To address this gap, we developed a two-stage deep learning framework. Transformer-based language models were first pre-trained on large corpora of unpaired heavy- and light-chain sequences, then integrated into a sequence-to-sequence model to generate light chains from heavy chain input. Although native light chain recovery was moderate, generated sequences exhibited high germline identity, improved structural quality, and broader framework and complementarity-determining region coverage. Heavy chains from memory B cells generated light chains with more restricted V gene usage, reflecting maturation-dependent selection. Generated
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Registered trials
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