AI Developments for T and B Cell Receptor Modeling and Therapeutic Design

TL;DR

This paper reviews recent AI-driven advances in modeling T and B cell receptors, emphasizing new methods that improve immune receptor prediction and therapeutic design using sequence data and immune context.

Contribution

It provides a comprehensive survey of recent AI techniques, including protein language models and multimodal integration, applied to immune receptor modeling and therapeutic optimization.

Findings

Emerging AI strategies enhance immune receptor modeling.

Single-cell and repertoire data improve prediction accuracy.

New models are more data-efficient and clinically relevant.

Abstract

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.