The current COVID-19 pandemic initiated an unprecedented response from
clinicians and the scientific community in all relevant biomedical fields. It created an incredible
multidimensional data-rich framework in which deep learning proved instrumental
to make sense of the data and build models used in prediction-validation workflows
that in a matter of months have already produced results in assessing the spread of the
outbreak, its taxonomy, population susceptibility, diagnostics or drug discovery and repurposing.
More is expected to come in the near future by using such advanced machine
learning techniques to combat this pandemic. This review aims to unravel just a small
fraction of the large global endeavors by focusing on the research performed on the main
COVID-19 targets, on the computational weaponry used in identifying drugs to combat
the disease, and on some of the most important directions found to contain COVID-19 or
alleviating its symptoms in the absence of specific medication.
Keywords: SARS-CoV-2, deep learning, drug-target interactions, virtual screening, drug design, drug repurposing.
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