Zhang Yang, Ye Taoyu, Xi Hui, Juhas Mario, Li Junyi
College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Medical and Molecular Microbiology Unit, Department of Medicine, Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland.
Front Microbiol. 2021 Oct 28;12:739684. doi: 10.3389/fmicb.2021.739684. eCollection 2021.
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.
深度学习显著加速了药物发现过程,并为全球阻止传染病传播的努力做出了贡献。除了提高针对广泛病原体的抗菌化合物筛选效率外,深度学习还具有高效且可靠地识别针对严重急性呼吸综合征冠状病毒2(SARS-CoV-2)的候选药物的潜力。因此,深度学习已成功用于识别多种针对SARS-CoV-2的潜在药物,包括阿扎那韦、瑞德西韦、克力芝、依那普利拉、维奈克拉、泊沙康唑、达可他韦、奥比他韦、托瑞米芬、氯硝柳胺、地塞米松、吲哚美辛、普拉曲沙、阿奇霉素、巴马汀和柳杉酚。本综述讨论了基于深度学习的SARS-CoV-2药物发现的最新进展和未来前景。