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

Autor: Yingzhu Li, Junko Kato-Weinstein, Julian Ivanov, Dana Albaiu, Qiongqiong Zhou, Linda V. Garner, Polshakov Dmitrii Arkadyevich, Yi Deng, Roger Granet, Christopher Aultman, Cynthia Liu, Wilson Jeffrey Michael
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: ACS Omega, Vol 5, Iss 42, Pp 27344-27358 (2020)
ACS Omega
ISSN: 2470-1343
Popis: 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.
Databáze: OpenAIRE