Quantitative Structure–Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections

作者：Julian Ivanov, Dmitrii Polshakov, Junko Kato-Weinstein, Qiongqiong Angela Zhou, Yingzhu Li, Roger Granet, L. Garner, Yi Deng, Cynthia Liu, Dana Albaiu, J. M. WILSON, Christopher Aultman · 发表于：ACS Omega · 年份：2020 · DOI：10.1021/acsomega.0c03682 · 被引用次数：54 · 研究领域：Computational Drug Discovery Methods、SARS-CoV-2 and COVID-19 Research、RNA and protein synthesis mechanisms

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.