ArticleBioinformatics advances2022
AbLang: an antibody language model for completing antibody sequences.
Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers.
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Who cites it
113 citing papers in PubMed.
- Article
- Prediction of antibody non-specificity using protein language models and biophysical parameters.mAbs · 2026Article
- Article
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- Phys-AbGAT: a physics-informed multi-task graph attention network for robust antibody-antigen binding affinity prediction.Bioinformatics (Oxford, England) · 2026Article
- AbAgKer: a unified semi-supervised framework for antigen-antibody binding affinity and kinetics prediction.Bioinformatics (Oxford, England) · 2026Article
- CLDN18.2 antibody design with protein language models: A deep learning optimization framework.PLoS computational biology · 2026Article
- Multi-objective antibody design and optimization using machine learning.Nature reviews bioengineering · 2026Article
- Mixture diffusion model for multimodal antibody design.Briefings in bioinformatics · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Evidence-aware comparison of sequence-centric machine learning for antibody discovery and optimization.Briefings in bioinformatics · 2026Article
- AbTune: layer-wise selective fine-tuning of protein language models for antibodies.Briefings in bioinformatics · 2026Article
- OpenGerminal: an open-source implementation of the Germinal antibody design pipeline.bioRxiv : the preprint server for biology · 2026Article
- Efficient generation of epitope-targeted antibodies with Germinal.Nature biotechnology · 2026Article
- Heavy-chain immune repertoire sequencing enables language-model prediction of antigen-specific antibodies.Research square · 2026Article
- Article
- ANARCII enables alignment-free antigen receptor numbering using a generalised language model.Communications biology · 2026Article
- Evaluating Expert Specialization in Mixture-of-Experts Antibody Language Models.bioRxiv : the preprint server for biology · 2026Article
- Predicting antibody-antigen affinity with a dual-level representation model.Bioinformatics (Oxford, England) · 2026Article
- AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026Review
53 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: General protein language models have been shown to summarize the semantics of protein sequences into representations that are useful for state-of-the-art predictive methods. However, for antibody specific problems, such as restoring residues lost due to sequencing errors, a model trained solely on antibodies may be more powerful. Antibodies are one of the few protein types where the volume of sequence data needed for such language models is available, e.g. in the Observed Antibody Space (OAS) database. Results: Here, we introduce AbLang, a language model trained on the antibody sequences in the OAS database. We demonstrate the power of AbLang by using it to restore missing residues in antibody sequence data, a key issue with B-cell receptor repertoire sequencing, e.g. over 40% of OAS sequences are missing the first 15 amino acids. AbLang restores the missing residues of antibody sequences better than using IMGT germlines or the general protein language model ESM-1b. Further, AbLang does not require knowledge of the germline of the antibody and is seven times faster than ESM-1b. Availability and implementation: AbLang is a python package available at https://github.com/oxpig/AbLang. Supplementary information: Supplementary data are available at
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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.