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

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.