Building explainable graph neural network by sparse learning for the drug-protein binding prediction

TL;DR

This paper introduces SLGNN, a sparse learning-based graph neural network that identifies chemically valid key structures in drugs for protein binding prediction, improving interpretability and predictive power over existing methods.

Contribution

SLGNN employs a chemical-substructure graph and generalized fused lasso to ensure chemically valid key structures, enhancing interpretability and accuracy in drug-protein binding prediction.

Findings

SLGNN identifies chemically valid key structures.

Key structures have higher predictive power.

Most binding sites are contained in identified key structures.

Abstract

Explainable Graph Neural Networks (GNNs) 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 GNN models are typically chemically invalid. Furthermore, a threshold needs to be manually selected to pinpoint the key structures from the rest. To overcome the limitations of the current explainable GNN models, we propose our SLGNN, which stands for using Sparse Learning to Graph Neural Networks. Our SLGNN relies on using a chemical-substructure-based graph (where nodes are chemical substructures) to represent a drug molecule. Furthermore, SLGNN incorporates generalized fussed 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 our 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. We demonstrate the explanatory power of our SLGNN by first showing all the key structures identified by our SLGNN are chemically valid. In addition, we illustrate that the key structures identified by our SLGNN have more predictive power than the key structures identified by the competing methods. At last, we use known drug-protein binding data to show the key structures identified by our SLGNN contain most of the binding sites.