  This paper presents ExplainableFold, an explainable AI framework for protein
structure prediction. Despite the success of AI-based methods such as AlphaFold
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 ExplainableFold 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.
