ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction
This work addresses interpretable molecular property prediction for chemistry applications, but it appears incremental as it builds on existing techniques.
The authors tackled molecular property prediction by proposing ProGReST, a model combining prototype learning, soft decision trees, and Graph Neural Networks, which achieves competitive results against state-of-the-art methods.
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.