首页> 外文期刊>ACS Omega >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
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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

机译:定量结构 - 活动关系机学习模型及其鉴定病毒3ClPro-和RDRP靶向化合物作为Covid-19和相关病毒感染的潜在治疗方法

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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.
机译:为了回应正在进行的Covid-19大流行,正在全球努力识别潜在的抗SARS-COV-2治疗方法。在这里,我们通过建立机器学习预测模型来促进这些努力,以识别病毒靶标的新型药物候选者3个胰蛋白酶样蛋白酶(3ClPro)和RNA依赖性RNA聚合酶(RDRP)。从CAS数据收集组装化学家策划训练套物质,并与策划生物测定数据集成。应用最佳的分类模型用于筛选一套FDA批准的药物和CAS注册物质,其类似于或与抗病毒剂相关。发现了对3ClPro或RDRP具有潜在活动的许多物质,有些物质通过公布的生物测定研究和/或包含在即将到来或正在进行的Covid-19临床试验中进行验证。本研究进一步支持基于机器学习的预测模型可用于帮助Covid-19和其他疾病的药物发现过程。

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