Strategies for drug discovery and repositioning are urgently need with respect to COVID-19. Here we present REDIAL-2020, a suite of computational models for estimating small molecule activities in a range of SARS-CoV-2-related assays. Models were trained using publicly available, high-throughput screening data and by employing different descriptor types and various machine learning strategies. Here we describe the development and use of eleven models that span across the areas of viral entry, viral replication, live virus infectivity, in vitro infectivity and human cell toxicity. REDIAL-2020 is available as a web application through the DrugCentral web portal (http://drugcentral.org/Redial). The web application also provides similarity search results that display the most similar molecules to the query, as well as associated experimental data. REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment.

就 COVID-19 而言，迫切需要药物发现和重新定位的策略。在这里，我们介绍了 REDIAL-2020，这是一套计算模型，用于估计一系列 SARS-CoV-2 相关分析中的小分子活性。使用公开可用的高通量筛选数据并使用不同的描述符类型和各种机器学习策略对模型进行训练。在这里，我们描述了跨越病毒进入、病毒复制、活病毒感染性、体外感染性和人类细胞毒性等领域的 11 个模型的开发和使用。REDIAL-2020 可通过 DrugCentral 门户网站 (http://drugcentral.org/Redial) 作为网络应用程序使用。该网络应用程序还提供相似性搜索结果，显示与查询最相似的分子，以及相关的实验数据。