The Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, China.
Shandong University of Traditional Chinese Medicine, Jinan, China.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac124.
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.
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.
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.
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.
2019 年冠状病毒病（COVID-19）的爆发促使人们大力发掘可重新用于治疗的现有药物。药物再利用是一种为已批准或正在研究的药物确定新用途的策略，这些药物的用途超出了原始医疗适应症的范围。
目前针对严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）的药物再利用工作大多仅限于关注化学药物，针对单一 SARS-CoV-2 蛋白的单一药物靶向分析，以及对不同 SARS-CoV-2 感染阶段使用相同治疗方法（相同药物）的一刀切策略。为了淡化这些问题，我们最初将研究重点放在草药上。然后，我们提出了一种异构图嵌入方法，为每个 SARS-CoV-2 蛋白标记候选再利用草药，并采用变分图卷积网络方法推荐精准草药组合作为针对特定感染阶段的潜在候选治疗方法。
我们最初采用虚拟筛选方法，基于 480 种草药、12735 种相关化学化合物和 24 种 SARS-CoV-2 蛋白构建了“草药-化合物”和“化合物-蛋白”对接图。然后，我们通过基于元路径的嵌入方法构建了“草药-化合物-蛋白”异构网络。接着，我们提出了基于异构信息网络的图嵌入方法，分别为靶向 SARS-CoV-2 的结构蛋白、非结构蛋白和辅助蛋白的草药生成候选排名列表。为了获得针对各种 COVID-19 感染阶段的精准合成有效治疗方法，我们采用变分图卷积网络方法生成候选草药组合作为推荐的治疗方法。
有 24 个排名列表，每个列表都包含 24 种 SARS-CoV-2 蛋白对应的前 10 种草药，生成了 20 种草药组合作为针对四个感染阶段的候选特定治疗方法。代码和补充材料可在 https://github.com/fanyang-AI/TCM-COVID19 上免费获取。
