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

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

Volume: 28 Issue: 28

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

Affiliation:

• Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independenței 296, 060031, Bucharest,Romania

• Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, Splaiul Independenţei 91-95, 050095 Bucharest,Romania

Keywords: SARS-CoV-2, deep learning, drug-target interactions, virtual screening, drug design, drug repurposing.

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.

Cite this article as:

Mernea Maria, Martin C. Eliza, Petrescu Andrei-José*, Avram Speranta*, Deep Learning in the Quest for Compound Nomination for Fighting COVID-19, Current Medicinal Chemistry 2021; 28 (28) . https://dx.doi.org/10.2174/0929867328666210113170222