Machine learning for predicting drug–drug interactions: Graph neural networks and beyond

Identification of interacting drugs before application would be imperative to mitigate the serious risk represented by drug-drug interactions for patient health. Machine learning-based methods are increasingly recognized by regulatory agencies as tools with a central role in drug development, including the identification of novel interactions. In recent years graph- and hypergraph neural networks delivered promising performance improvements compared to non-graph-based methods on the field. In this primer, we discuss recent developments of graph- and hypergraph neural networks and highlight the potential of incorporating protein and metabolite data into the identification task to provide a new, more comprehensive, systems biology-based perspective on drug-drug interactions.