ArticlePatterns (New York, N.Y.)2025
Focused learning by antibody language models using preferential masking of non-templated regions.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Evaluating Expert Specialization in Mixture-of-Experts Antibody Language Models.bioRxiv : the preprint server for biology · 2026Article
- Article
- Nucleotide context models outperform protein language models for predicting antibody affinity maturation.PLoS computational biology · 2025Article
- Nucleotide context models outperform protein language models for predicting antibody affinity maturation.bioRxiv : the preprint server for biology · 2025Article
- Data-optimal scaling of paired antibody language models.bioRxiv : the preprint server for biology · 2025Article
- Separating selection from mutation in antibody language models.bioRxiv : the preprint server for biology · 2025Article
- A curriculum learning approach to training antibody language models.PLoS computational biology · 2025Article
- A Sitewise Model of Natural Selection on Individual Antibodies via a Transformer-Encoder.Molecular biology and evolution · 2025Article
- Focused learning by antibody language models using preferential masking of non-templated regions.Patterns (New York, N.Y.) · 2025Article
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Abstract
Existing antibody language models (AbLMs) are pre-trained using a masked language modeling (MLM) objective with uniform masking probabilities. While these models excel at predicting germline residues, they often struggle with mutated and non-templated residues, which concentrate in the complementarity-determining regions (CDRs) and are crucial for antigen binding specificity. Here, we demonstrate that preferential masking of the primarily non-templated CDR3 is a compute-efficient strategy to enhance model performance. We pre-trained two AbLMs using either uniform or preferential masking and observed that the latter improves residue prediction accuracy in the highly variable CDR3. Preferential masking also improves antibody classification by native chain pairing and binding specificity, suggesting improved CDR3 understanding and indicating that non-random, learnable patterns help govern antibody chain pairing. We further show that specificity classification is largely informed by residues in the CDRs, demonstrating that AbLMs learn meaningful patterns that align with immunological understanding.
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