ExplainableFold: Understanding AlphaFold Prediction with Explainable AI
Abstract
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.
Cite
@article{arxiv.2301.11765,
title = {ExplainableFold: Understanding AlphaFold Prediction with Explainable AI},
author = {Juntao Tan and Yongfeng Zhang},
journal= {arXiv preprint arXiv:2301.11765},
year = {2023}
}
Comments
This work has been accepted for presentation at the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023)