Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jalan Salemba Raya number 4, Jakarta, 10430, Indonesia.
Bioinformatics Core Facilities - IMERI, Faculty of Medicine, Universitas Indonesia, Jalan Salemba Raya number 6, Jakarta, 10430, Indonesia.
BMC Complement Med Ther. 2022 Aug 3;22(1):207. doi: 10.1186/s12906-022-03686-y.
The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches.
We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control.
From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4'-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside.
Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4'-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.
印度尼西亚的 COVID-19 病例数量持续增长。这一现象促使研究人员寻找具有预防或治疗作用的替代药物。由于印度尼西亚药用植物具有丰富的生物多样性,一种替代方法是研究草药的潜力,以支持 COVID 治疗。本研究旨在使用机器学习和药效团模型方法鉴定印度尼西亚草药中的潜在化合物候选物。
我们使用了三种具有不同决策过程的分类方法:支持向量机 (SVM)、多层感知器 (MLP) 和随机森林 (RF)。对于药效团建模方法,我们对 SARS-CoV-2 (3CLPro) 的主要蛋白酶的 3D 结构进行了基于结构的分析,并重新利用了从文献中确定的针对 SARS、MERS 和 SARS-CoV-2 的药物作为配体的方法中的数据集。最后,我们使用分子对接分析了来自印度尼西亚草药数据库 (HerbalDB) 的 14 种先导化合物与 3CLpro 之间的相互作用,洛匹那韦作为阳性对照。
从分子对接分析中,我们发现了六种可能作为 SARS-CoV-2 抑制剂的主要蛋白酶的潜在化合物:橙皮苷、山奈酚-3,4'-二甲醚(Ermanin);杨梅素-3-葡萄糖苷、矢车菊素 3-(4'-阿拉伯糖苷);槲皮素 3-(2G-鼠李糖苷rutinoside);和山奈酚 3-甘露糖-(1-2)-阿拉伯糖苷。
我们使用机器学习和药效团建模方法进行的分层虚拟筛选提供了更客观和优化的虚拟筛选,并避免了结果的主观决策。来自筛选的草药化合物,即橙皮苷、山奈酚-3,4'-二甲醚(Ermanin);杨梅素-3-葡萄糖苷、矢车菊素 3-(4'-阿拉伯糖苷);槲皮素 3-(2G-鼠李糖苷rutinoside);和山奈酚 3-甘露糖-(1-2)-阿拉伯糖苷,是 SARS-CoV-2 的潜在抗病毒候选物。含有这些化合物的辣木和番石榴可能是 COVID-19 草药预防的另一种选择。