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

https://doi.org/10.1016/j.artmed.2022.102418Get rights and content

Highlights

  • Integrate SARS-CoV-2 interaction data with drug–protein and proteinprotein interaction.

  • Proposed deep learning model suggests drugs using links in the integrated network.

  • The model utilized variational graph autoencoder and node2vec to learn integrated network.

  • Results shows excellent levels of accuracy in predicting molecular interfaces.

  • The model establishes novel host-directed therapy (HDT) options.

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 has not been explored so far. We focus on hitherto unknown links between drugs and human proteins that play key roles in the replication cycle of SARS-CoV-2. Thereby, we establish novel host-directed therapy (HDT) options whose utmost plausibility is confirmed by realistic simulations. As a consequence, many of the predicted links are likely to be crucial for the virus to thrive on the one hand, and can be targeted with existing drugs on the other hand.

Keywords

COVID-19
Variational graph autoEncoder
Node2Vec
Molecular interaction network
Host directed therapy

Availability

All codes and datasets are given in the github link: https://github.com/sumantaray/Covid19.

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