Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
Drug Discov Today. 2021 Dec;26(12):2800-2815. doi: 10.1016/j.drudis.2021.07.026. Epub 2021 Jul 30.
The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.
新型冠状病毒肺炎大流行已在全球范围内导致数百万人死亡和社会重大痛苦。急需治疗方法，但从头开发新药仍然是一个漫长的过程。一种很有前途的替代方法是计算药物再利用，它可以通过快速的计算机分析来优先考虑现有化合物。最近基于分子对接、机器学习和网络分析的努力已经产生了可行的预测。一些针对病毒蛋白和病理宿主途径的预测药物正在进行临床试验。在这里，我们回顾了这项工作，强调了具有高预测疗效的药物，并对其作用机制进行了分类。我们讨论了已发表方法的优缺点，并概述了可能的未来方向。最后，我们整理了一份可用于加速未来研究的新型冠状病毒肺炎数据门户和其他存储库的列表。
