虚拟筛选
对接(动物)
计算机科学
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
药物发现
冠状病毒
计算生物学
分子动力学
2019年冠状病毒病(COVID-19)
机器学习
化学
生物信息学
生物
计算化学
传染病(医学专业)
医学
护理部
疾病
病理
作者
А.В. Сулимов,İvan Ilin,Anna Tashchilova,O.A. Kondakova,Danil Kutov,В. Б. Сулимов
标识
DOI:10.1080/1062936x.2024.2306336
摘要
The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.
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