The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.

中文翻译：

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最近基于组学的 COVID-19 药物发现和再利用计算方法

由严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 引起的 2019 年冠状病毒病 (COVID-19) 大流行是全球死亡人数增加的主要原因。尽管许多国家采取了严格的检疫措施，但疫情形势依然严峻。因此，需要利用一切可能的手段来应对这一流行病。因此，研究人员正在与时间赛跑，以开发潜在的治疗方法来治愈或减少不断增加的 COVID-19 感染。计算方法被广泛证明在生物学相关问题（包括疾病的诊断和治疗）方面取得了快速成功。最近几个月的许多努力在对抗 COVID-19 传播的背景下利用了人工智能 (AI) 技术。定期审查和讨论最近的努力可以节省研究人员的时间，并有助于将他们的努力联系起来，以更快、更有效地对抗这一流行病。在这篇综述中，我们讨论了最近使用基于组学的数据并利用人工智能算法和其他计算工具来实现这一目标的有前景的研究。我们回顾了已建立的数据集和开发的方法，这些方法基本上针对新的或重新利用的药物、疫苗接种和诊断。工具和方法因结构、序列或代谢数据等可用信息的详细程度而异。我们讨论了最近使用基于组学的数据并利用人工智能算法和其他计算工具来实现这一目标的有前途的研究。我们回顾了已建立的数据集和开发的方法，这些方法基本上针对新的或重新利用的药物、疫苗接种和诊断。工具和方法因结构、序列或代谢数据等可用信息的详细程度而异。我们讨论了最近使用基于组学的数据并利用人工智能算法和其他计算工具来实现这一目标的有前途的研究。我们回顾了已建立的数据集和开发的方法，这些方法基本上针对新的或重新利用的药物、疫苗接种和诊断。工具和方法因结构、序列或代谢数据等可用信息的详细程度而异。