Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2

Yang Zhang; Taoyu Ye; Hui Xi; Mario Juhas; Junyi Li

doi:10.3389/fmicb.2021.739684

Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2

Front Microbiol. 2021 Oct 28:12:739684. doi: 10.3389/fmicb.2021.739684. eCollection 2021.

Authors

Yang Zhang¹, Taoyu Ye¹, Hui Xi¹, Mario Juhas², Junyi Li³

Affiliations

¹ College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
² Medical and Molecular Microbiology Unit, Department of Medicine, Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland.
³ School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.

Abstract

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

Keywords: SARS-CoV-2; antibiotics; antimalarial drug; database; deep learning; drug discovery; drug repurposing.

Publication types

Review