In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics. Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates for the viral targets 3 chymotrypsin-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp). Chemist-curated training sets of substances were assembled from CAS data collections and integrated with curated bioassay data. The best-performing classification models were applied to screen a set of FDA-approved drugs and CAS REGISTRY substances that are similar to, or associated with, antiviral agents. Numerous substances with potential activity against 3CLpro or RdRp were found, and some were validated by published bioassay studies and/or by their inclusion in upcoming or ongoing COVID-19 clinical trials. This study further supports that machine learning-based predictive models may be used to assist the drug discovery process for COVID-19 and other diseases.

中文翻译：

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定量结构-活动关系机器学习模型及其在确定靶向3CLpro和RdRp的化合物作为COVID-19和相关病毒感染的潜在治疗剂中的应用

为了应对持续的COVID-19大流行，全球正在努力确定潜在的抗SARS-CoV-2治疗药物。在这里，我们通过建立机器学习预测模型来确定病毒靶标3胰凝乳蛋白酶样蛋白酶（3CLpro）和RNA依赖性RNA聚合酶（RdRp）的新型候选药物，为这些努力做出了贡献。化学家精心策划的物质训练集是从CAS数据集中收集的，并与精心策划的生物测定数据整合在一起。应用性能最佳的分类模型来筛选一组FDA批准的与抗病毒剂相似或相关的药物和CAS REGISTRY物质。发现了许多对3CLpro或RdRp具有潜在活性的物质，一些已通过已发表的生物测定研究和/或被纳入即将开展或正在进行的COVID-19临床试验中而得到验证。这项研究进一步支持基于机器学习的预测模型可用于协助COVID-19和其他疾病的药物发现过程。