ExplainableFold: Understanding AlphaFold Prediction with Explainable AI

This paper presents ExplainableFold (𝑥Fold), which is an Explainable AI framework for protein structure prediction. Despite the success of AI-based methods such as AlphaFold (𝛼Fold) in this field, the underlying reasons for their predictions remain unclear due to the black-box nature of deep learning models. To address this, we propose a counterfactual learning framework inspired by biological principles to generate counterfactual explanations for protein structure prediction, enabling a dry-lab experimentation approach. Our experimental results demonstrate the ability of Ex-plainableFold to generate high-quality explanations for AlphaFold's predictions, providing near-experimental understanding of the effects of amino acids on 3D protein structure. This framework has the potential to facilitate a deeper understanding of protein structures. Source code and data of the ExplainableFold project are available at https://github.com/rutgerswiselab/ExplainableFold .