Explainable Graph Neural Networks have been developed and applied to drug-protein binding prediction to identify the key chemical structures in a drug that have active interactions with the target proteins. However, the key structures identified by the current explainable Graph Neural Network (GNN) models are typically chemically invalid. Furthermore, a threshold must be manually selected to pinpoint the key structures from the rest. To overcome the limitations of the current explainable GNN models, we propose SLGNN, which stands for using Sparse Learning to Graph Neural Networks. It relies on using a chemical-substructure-based graph to represent a drug molecule. Furthermore, SLGNN incorporates generalized fused lasso with message-passing algorithms to identify connected subgraphs that are critical for the drug-protein binding prediction. Due to the use of the chemical-substructure-based graph, it is guaranteed that any subgraphs in a drug identified by SLGNN are chemically valid structures. These structures can be further interpreted as the key chemical structures for the drug to bind to the target protein. Our code is available at https://github.com/yw109iu/Explainable_GNN. We test SLGNN and the state-of-the-art competing methods on three real-world drug-protein binding datasets. We have demonstrated that the key structures identified by our SLGNN are chemically valid and have more predictive power.
Keywords: Graph Neural Network; drug–protein binding prediction; interpretable model; sparse learning.