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.
Abstract (translated)
本论文介绍了ExplainableFold,一个可解释的人工智能框架,用于蛋白质结构预测。尽管AI方法如AlphaFold在这个领域的成功,但它们的预测背后的原因仍然难以明确,因为深度学习模型具有黑盒性质。为了解决这一问题,我们提出了一个基于生物学原则的反事实学习框架,用于生成反事实解释,以进行干燥实验室实验。我们的实验结果证明了ExplainableFold能够生成AlphaFold预测的高质量解释,提供了接近实验理解的幅度对氨基酸对三维蛋白质结构的影响。这个框架有潜力促进更深入的理解蛋白质结构。
URL
https://arxiv.org/abs/2301.11765
