ExplainableFold: Understanding AlphaFold Prediction with Explainable AI
This addresses the need for interpretability in AI-based protein structure prediction for researchers, though it is incremental as it builds on existing methods like AlphaFold.
The paper tackled the problem of understanding AlphaFold's protein structure predictions by developing ExplainableFold, an explainable AI framework that generates counterfactual explanations, resulting in near-experimental understanding of amino acid effects on 3D structure.
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.