SSLDTI: A novel method for drug-target interaction prediction based on self-supervised learning

Abstract

Many computational methods have been proposed to identify potential drug-target interactions (DTIs) to expedite drug development. Graph neural network (GNN) methods are considered to be one of the most effective approaches. However, shallow GNN methods can only aggregate local information from nodes. Also, deep GNN methods may result in over-smoothing while obtaining long-distance neighbourhood information. As a result, existing GNN methods struggle to extract the complete features of the graph. Additionally, the number of known DTIs is insufficient, and there are far more unknown drug-target pairs than known DTIs, leading to class imbalance. This article proposes a model that combines graph autoencoder and self-supervised learning to accurately encode multilevel features of graphs using only a small number of labelled samples. We introduce a positive sample compensation coefficient to the objective function to mitigate the impact of class imbalance. Experiments on two datasets demonstrated that our model outperforms the four baseline methods, and the new DTIs predicted by the SSLDTI model were verified by the DrugBank database.