Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.17138 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912857208127488 |
|---|---|
| author | Xie, Linhui Pelissier, Aurelien Shao, Yanjun Martinez, Maria Rodriguez |
| author_facet | Xie, Linhui Pelissier, Aurelien Shao, Yanjun Martinez, Maria Rodriguez |
| contents | Artificial intelligence (AI) is accelerating progress in modeling T and B cell receptors by enabling predictive and generative frameworks grounded in sequence data and immune context. This chapter surveys recent advances in the use of protein language models, machine learning, and multimodal integration for immune receptor modeling. We highlight emerging strategies to leverage single-cell and repertoire-scale datasets, and optimize immune receptor candidates for therapeutic design. These developments point toward a new generation of data-efficient, generalizable, and clinically relevant models that better capture the diversity and complexity of adaptive immunity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17138 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI Developments for T and B Cell Receptor Modeling and Therapeutic Design Xie, Linhui Pelissier, Aurelien Shao, Yanjun Martinez, Maria Rodriguez Biomolecules Artificial intelligence (AI) is accelerating progress in modeling T and B cell receptors by enabling predictive and generative frameworks grounded in sequence data and immune context. This chapter surveys recent advances in the use of protein language models, machine learning, and multimodal integration for immune receptor modeling. We highlight emerging strategies to leverage single-cell and repertoire-scale datasets, and optimize immune receptor candidates for therapeutic design. These developments point toward a new generation of data-efficient, generalizable, and clinically relevant models that better capture the diversity and complexity of adaptive immunity. |
| title | AI Developments for T and B Cell Receptor Modeling and Therapeutic Design |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2601.17138 |