PU-GNN: A Positive-Unlabeled Learning Method for Polypharmacy Side-Effects Detection Based on Graph Neural Networks

Abstract

The simultaneous use of multiple drugs, known as polypharmacy, heightens the risks of harmful side effects due to drug-drug interactions. Predicting these interactions is crucial in drug research due to the rising prevalence of polypharmacy. Researchers employ a graphical structure to model these interactions, representing drugs and side effects as nodes and their interactions as edges. This creates a multipartite graph that encompasses various interactions such as protein-protein interactions, drug-target interactions, and side effects of polypharmacy. In this study, a method named PU-GNN, based on graph neural networks, is introduced to predict drug side effects. The proposed method involves three main steps: (1) drug features extraction using a novel biclustering algorithm, (2) reducing uncertainity in input data using a positive-unlabeled learning algorithm, and (3) prediction of drug’s polypharmacies by utilizing a graph neural network. Performance evaluation using 5-fold cross-validation reveals that PU-GNN surpasses other methods, achieving high scores of 0.977, 0.96, and 0.949 in the AUPR, AUC, and F1 measures, respectively.