ExplainableFold: Understanding AlphaFold Prediction with Explainable AI


Juntao Tan, Yongfeng Zhang


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.

In Proceedings of the 29TH ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Juntao Tan
PhD candidate

My research interests are mainly on Explainable AI, Recommender System, and some other subfields of AI and Machine Learning.