Graph Representation Learning for Predicting Diverse Sources of Drug Interactions.

Drug treatment strategies to reduce dose-related hazards is a tried-and-true method for preventing drug resistance and enhancing the efficiency of the monotherapy. Except when certain drugs pile up. Most adverse medication effects are induced by antagonistic drug-drug interactions. New medications and monitoring patients' use of more effective medication combination therapies require precise Drug-Drug Interaction (DDI) prediction. Several machine learning-based DDI prediction methods exist. This wide range of strategies uses drug-related and substancerelated traits covertly. Graph embeddings and deep learning are applied to benchmark datasets to overcome this. The Simplified Molecular Input Line Entry System (SMILE) method is introduced for preprocessing, and the GCNet is applied for DDI prediction. Moreover, the graph is also constructed based on that the similarity is identified using link prediction. The proposed method provides an accuracy range of 0.934, Mean Squared Error (MSE) of 0.082, and Root Mean Squared Error (RMSE) of 0.352, which assists in more effectively reducing adverse drug reactions. [ABSTRACT FROM AUTHOR]