  Recent advancements in immune sequencing and experimental techniques are
generating extensive T cell receptor (TCR) repertoire data, enabling the
development of models to predict TCR binding specificity. Despite the
computational challenges due to the vast diversity of TCRs and epitopes,
significant progress has been made. This paper discusses the evolution of the
computational models developed for this task, with a focus on machine learning
efforts, including the early unsupervised clustering approaches, supervised
models, and the more recent applications of Protein Language Models (PLMs). We
critically assess the most prominent models in each category, and discuss
recurrent challenges, such as the lack of generalization to new epitopes,
dataset biases, and biases in the validation design of the models.
  Furthermore, our paper discusses the transformative role of transformer-based
protein models in bioinformatics. These models, pretrained on extensive
collections of unlabeled protein sequences, can convert amino acid sequences
into vectorized embeddings that capture important biological properties. We
discuss recent attempts to leverage PLMs to deliver very competitive
performances in TCR-related tasks. Finally, we address the pressing need for
improved interpretability in these often opaque models, proposing strategies to
amplify their impact in the field.
