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Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Review Article

Deep Learning in the Quest for Compound Nomination for Fighting COVID-19

Author(s): Maria Mernea, Eliza C. Martin, Andrei-José Petrescu* and Speranta Avram*

Volume 28 , Issue 28 , 2021

Published on: 13 January, 2021

Page: [5699 - 5732] Pages: 34

DOI: 10.2174/0929867328666210113170222

open access plus

Abstract

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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