Journal
FRONTIERS IN MICROBIOLOGY
Volume 12, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2021.739684
Keywords
deep learning; database; drug discovery; antibiotics; antimalarial drug; drug repurposing; SARS-CoV-2
Categories
Funding
- Natural Science Foundation of Shenzhen City [JCYJ20180306172131515]
- Shenzhen Science and Technology Program the university stable support program [20200821222112001]
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Deep learning has greatly accelerated the drug discovery process, particularly in combating the spread of infectious diseases and identifying drug candidates against SARS-CoV-2. Research has successfully utilized deep learning to identify a variety of potential drugs for SARS-CoV-2.
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.
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