Predicting adaptive immune receptor specificities by machine learning is a data generation problem.

Determining the specificity of adaptive immune receptors-B cell receptors (BCRs), their secreted form antibodies, and T cell receptors (TCRs)-is critical for understanding immune responses and advancing immunotherapy and drug discovery. Immune receptors exhibit extensive diversity in their variable domains, enabling them to interact with a plethora of antigens. Despite the significant progress made by AI tools such as AlphaFold in predicting protein structures, challenges remain in accurately modeling the structure and specificity of immune receptors, primarily due to the limited availability of high-quality crystal structures and the complexity of immune receptor-antigen interactions. In this perspective, we highlight recent advancements in sequence-based and structure-based data generation for immune receptors, which are crucial for training machine learning models that predict receptor specificity. We discuss the current bottlenecks and potential future directions in generating and utilizing high-dimensional datasets for predicting and designing the specificity of antibodies and TCRs.