Title: Drug repurposing using link prediction on knowledge graphs Authors:  Sarel Cohen - The Academic College of Tel Aviv-Yaffo (Israel) [presenting]
Abstract: The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time-intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on \emph{knowledge graphs}, which condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, a recent study developed the Dr-COVID model. Their work using additional output interpretation strategies is further extended. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. In addition, the implementation of the Dr-COVID model is improved by significantly shortening the inference and pre-processing time by exploiting data parallelism.