ExplainableFold: Understanding AlphaFold Prediction with Explainable AI

TL;DR

ExplainableFold is an AI framework that provides interpretable explanations for AlphaFold's protein structure predictions using counterfactual learning, enhancing understanding of amino acid effects on 3D structures.

Contribution

This work introduces a novel counterfactual explanation framework for AlphaFold, bridging the gap between black-box predictions and biological interpretability.

Findings

Generated high-quality explanations for AlphaFold predictions

Enabled near-experimental understanding of amino acid effects

Facilitated deeper insights into protein structure prediction

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.