In this work, we propose the novel Prototypical Graph Regression Self-explainable Trees (ProGReST) model, which combines prototype learning, soft decision trees, and Graph Neural Networks. In contrast to other works, our model can be used to address various challenging tasks, including compound property prediction. In ProGReST, the rationale is obtained along with prediction due to the model's built-in interpretability. Additionally, we introduce a new graph prototype projection to accelerate model training. Finally, we evaluate PRoGReST on a wide range of chemical datasets for molecular property prediction and perform in-depth analysis with chemical experts to evaluate obtained interpretations. Our method achieves competitive results against state-of-the-art methods.

翻译：在这项工作中,我们提议了新型的原型图递减自我解释树(ProGREST)模型,该模型将原型学习、软决策树和图形神经网络结合起来。与其他工程不同,我们的模型可用于应对各种具有挑战性的任务,包括复合财产预测。在ProGREST中,由于模型的内在可解释性,其原理与预测一起获得。此外,我们引入了新的图表原型预测,以加速模型培训。最后,我们评估了用于分子财产预测的广泛化学数据集,并与化学专家进行深入分析,以评价获得的解释。我们的方法与最新方法相比,取得了竞争性的结果。