ExplainableFold: Understanding AlphaFold Prediction with Explainable AI

Juntao Tan, Yongfeng Zhang
1/27/2023
cs.AIcs.LG

No official code found. Best implementation: rutgerswiselab/ExplainableFold (14 stars, last commit 2023-10-05).

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.

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Code Implementations(1)

rutgerswiselab/ExplainableFoldCommunity100%
142Shell, PythonApr 4, 20233 years ago

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Cite this paper

@article{tan2023explainablefold,
  title  = {ExplainableFold: Understanding AlphaFold Prediction with Explainable AI},
  author = {Juntao Tan and Yongfeng Zhang},
  year   = {2023},
  eprint = {2301.11765},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.11765v2}
}

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