Interpretation of compound fragments via attentive recursive tree

The discovery of new drug-like chemicals with desired properties is a challenging and costly process in the pharmaceutical industry. To facilitate this process in the preclinical phase, many different neural network models have been proposed for different tasks (e.g., drug-target affinity prediction, molecular property prediction, target-specific molecule generation). Despite producing successful results, they usually lack interpretability. To comprehend the significance of each fragment in the relevant compounds, we employed the Attention Recursive Tree (AR-Tree) model. Thanks to its task-specific attention mechanism, AR-Tree highlights the significant fragments of compounds by positioning them closer to the root of the tree structure. In this way, the identified significant fragments can be used to design new compounds with desired properties in future research. We experimented with five different classification and four different regression tasks of the MoleculeNet as benchmark tasks. The results of the experiments show that the proposed architecture succeeded in finding chemically meaningful fragments for the corresponding tasks.