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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*   Pages 1 - 32 ( 32 )

Abstract:


The current COVID-19 pandemic gave rise to an unprecedented response from clinicians and the scientific community in all relevant biomedical fields. This 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, in diagnostics or drug discovery and repurposing. More is expected to come in the near future from using such advanced machine learning techniques in combating this pandemic. This review is aimed to uncover just a small fraction of this large global endeavor 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 in confronting COVID-19 or alleviating its symptoms in the absence of vaccines or specific medication.

Keywords:

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

Affiliation:

Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, Splaiul Independenţei 91-95, 050095 Bucharest, Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independenței 296, 060031, Bucharest, Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independenței 296, 060031, Bucharest, Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, Splaiul Independenţei 91-95, 050095 Bucharest



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