A clustering and graph deep learning-based framework for COVID-19 drug
  repurposing
Chaarvi Bansal Rohitash Chandra Vinti Agarwal P. R. Deepa
Drug repurposing (or repositioning) is the process of finding new therapeutic
uses for drugs already approved by drug regulatory authorities (e.g., the Food
and Drug Administration (FDA) and Therapeutic Goods Administration (TGA)) for
other diseases. This involves analyzing the interactions between different
biological entities, such as drug targets (genes/proteins and biological
pathways) and drug properties, to discover novel drug-target or drug-disease
relations. Artificial intelligence methods such as machine learning and deep
learning have successfully analyzed complex heterogeneous data in the
biomedical domain and have also been used for drug repurposing. This study
presents a novel unsupervised machine learning framework that utilizes a
graph-based autoencoder for multi-feature type clustering on heterogeneous drug
data. The dataset consists of 438 drugs, of which 224 are under clinical trials
for COVID-19 (category A). The rest are systematically filtered to ensure the
safety and efficacy of the treatment (category B). The framework solely relies
on reported drug data, including its pharmacological properties,
chemical/physical properties, interaction with the host, and efficacy in
different publicly available COVID-19 assays. Our machine-learning framework
reveals three clusters of interest and provides recommendations featuring the
top 15 drugs for COVID-19 drug repurposing, which were shortlisted based on the
predicted clusters that were dominated by category A drugs. The anti-COVID
efficacy of the drugs should be verified by experimental studies. Our framework
can be extended to support other datasets and drug repurposing studies, given
open-source code and data availability.