Enhancing Interpretability and Explainability of Protein Function Prediction ML Model Using Explainable AI

Predicting protein function from amino acid sequences remains a central challenge in bioinformatics, with significant implications for drug discovery, disease diagnosis, and biotechnology. This systematic review traces the evolution of computational approaches for protein function prediction (PFP), from early homology‐based methods to contemporary deep learning (DL) architectures. While DL models, including protein language models (PLM), have achieved state‐of‐the‐art predictive performance, their architectural complexity often results in diminished interpretability, obscuring the causal biological mechanisms underlying functional annotations. To address this opacity, Explainable Artificial Intelligence (XAI) frameworks are increasingly deployed. This review distinguishes between interpretability (the inherent transparency of a model) and explainability (post hoc methods that clarify model decisions). We systematically survey recent XAI approaches applied to protein sequence analysis, providing a comparative analysis of model‐specific and model‐agnostic techniques, including SHAP, LIME, attention mechanisms, and gradient‐based methods. A key contribution is the critical evaluation of how these explanations are validated against biological ground truths, using known functional residues and structural data. We identify a central research gap: the lack of biologically grounded validation for post hoc explanations. The review concludes that while current XAI methods show strong promise, future progress depends on developing biologically informed frameworks capable of explaining how and why specific functions are predicted, integrating multi‐modal data, and establishing standardized evaluation benchmarks.