药物重新定位
重新调整用途
图形
计算机科学
药物发现
虚拟筛选
2019年冠状病毒病(COVID-19)
药品
计算生物学
医学
药理学
生物信息学
疾病
理论计算机科学
生物
传染病(医学专业)
病理
生态学
作者
Fan Yang,Shuaijie Zhang,Wei Pan,Ruiyuan Yao,Weiguo Zhang,Yanchun Zhang,Guoyin Wang,Qianghua Zhang,Yunlong Cheng,Jihua Dong,Chunyang Ruan,Lizhen Cui,Hao Wu,Fuzhong Xue
摘要
Abstract Background Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. Motivation Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage. Method We initially employed the virtual screening method to construct the ‘Herb-Compound’ and ‘Compound-Protein’ docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the ‘Herb-Compound-Protein’ heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies. Results There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.
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