  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.
