期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 62, 期 8, 页码 1988-1997出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.2c00222
关键词
-
类别
资金
- National Institute of Diabetes, Digestive & Kidney Diseases of the National Institutes of Health
- National Center for Advancing Translational Sciences of the National Institutes of Health
This study developed a GCN-based virtual screening workflow that can rapidly identify new small molecule inhibitors against validated drug targets by training the model using information from experimentally identified HSPG and actin inhibitors.
The cell entry of SARS-CoV-2 has emerged as an attractive drug development target. We previously reported that the entry of SARS-CoV-2 depends on the cell surface heparan sulfate proteoglycan (HSPG) and the cortex actin, which can be targeted by therapeutic agents identified by conventional drug repurposing screens. However, this drug identification strategy requires laborious library screening, which is time consuming, and often limited number of compounds can be screened. As an alternative approach, we developed and trained a graph convolutional network (GCN)-based classification model using information extracted from experimentally identified HSPG and actin inhibitors. This method allowed us to virtually screen 170,000 compounds, resulting in similar to 2000 potential hits. A hit confirmation assay with the uptake of a fluorescently labeled HSPG cargo further shortlisted 256 active compounds. Among them, 16 compounds had modest to strong inhibitory activities against the entry of SARS-CoV-2 pseudotyped particles into Vero E6 cells. These results establish a GCN-based virtual screen workflow for rapid identification of new small molecule inhibitors against validated drug targets.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据