The current COVID-19 pandemic initiated an unprecedented response from clinicians and the scientific community in all relevant biomedical fields. It created an incredible multidimensional data-rich framework in which deep learning proved instrumental to make sense of the data and build models used in prediction-validation workflows that in a matter of months have already produced results in assessing the spread of the outbreak, its taxonomy, population susceptibility, diagnostics or drug discovery and repurposing. More is expected to come in the near future by using such advanced machine learning techniques to combat this pandemic. This review aims to unravel just a small fraction of the large global endeavors by focusing on the research performed on the main COVID-19 targets, on the computational weaponry used in identifying drugs to combat the disease, and on some of the most important directions found to contain COVID-19 or alleviating its symptoms in the absence of specific medication.

当前的 COVID-19 大流行引发了所有相关生物医学领域的临床医生和科学界前所未有的反应。它创建了一个令人难以置信的多维数据丰富的框架，其中深度学习被证明有助于理解数据并构建预测验证工作流程中使用的模型，这些模型在几个月内已经在评估爆发的传播、其分类法方面产生了结果、人口易感性、诊断或药物发现和再利用。通过使用这种先进的机器学习技术来对抗这种流行病，预计在不久的将来会出现更多。本综述旨在通过关注对主要 COVID-19 目标进行的研究，来解开全球大型努力的一小部分，