Towards the explainability of protein language models

Abstract

Artificial intelligence models are transforming protein research, enabling advances in areas ranging from structure prediction to the design of functional enzymes. However, these models operate as black boxes, and their underlying working principles remain unclear. Here we survey emerging applications of explainable artificial intelligence (XAI) to protein language models and describe the potential of XAI in protein research. We organize existing work around four points in a typical modelling pipeline: the data used for training; the user-provided inputs; the internal model architecture; and input–output relationships. Across these contexts, we highlight methods and applications of XAI. In addition, from published studies we distil five potential roles for XAI in protein research: Evaluator, Multitasker, Engineer, Coach and Teacher, with the evaluator role being the only one widely adopted so far. While our analysis focuses on protein language models, our categorization is broadly applicable to any other architecture. We conclude by highlighting critical areas of application for the future and outlining a path to advance the interpretability of protein artificial intelligence. Hunklinger and Ferruz provide an overview of explainable artificial intelligence methods for protein language models.