Deep variational graph autoencoders for novel host-directed therapy options against COVID-19.

ABSTRACT: The COVID-19 pandemic has been keeping asking urgent questions with respect to therapeutic options. Existing drugs that can be repurposed promise rapid implementation in practice because of their prior approval. Conceivably, there is still room for substantial improvement, because most advanced artificial intelligence techniques for screening drug repositories have not been exploited so far. We construct a comprehensive network by combining year-long curated drug-protein/protein-protein interaction data on the one hand, and most recent SARS-CoV-2 protein interaction data on the other hand. We learn the structure of the resulting encompassing molecular interaction network and predict missing links using variational graph autoencoders (VGAEs), as a most advanced deep learning technique that

SUBMITTER: Ray S 

PROVIDER: S-EPMC9556806 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature