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.

COVID-19 大流行已在全球造成数百万人死亡和巨大的社会困境。迫切需要治疗解决方案，但从头开发药物仍然是一个漫长的过程。一种有前途的替代方案是计算药物再利用，它可以通过快速计算机模拟对现有化合物进行优先排序分析。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操作的预测。一些预测的药物，靶向病毒蛋白和病理宿主通路正在进行临床试验。在这里，我们回顾了这项工作，突出了具有高预测疗效的药物并对它们的作用机制进行了分类。我们讨论了已发表方法的优点和局限性，并概述了未来可能的方向。最后，我们整理了一份 COVID-19 数据门户和其他可用于加速未来研究的存储库列表。