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.

翻译：本文提出ExplainableFold，一个用于蛋白质结构预测的可解释人工智能框架。尽管基于人工智能的方法（如AlphaFold）在该领域取得了成功，但由于深度学习模型的黑箱特性，其预测背后的根本原因仍不明确。为解决这一问题，我们受生物学原理启发，提出一个反事实学习框架，以生成蛋白质结构预测的反事实解释，从而支持干实验室实验方法。我们的实验结果表明，ExplainableFold能够为AlphaFold的预测生成高质量解释，提供近乎实验水平地理解氨基酸对三维蛋白质结构的影响。该框架有潜力促进对蛋白质结构的更深入理解。