论文标题
使用机器学习和虚拟筛选预测SARS-COV-2 RNA依赖性RNA聚合酶的抑制剂
Predicting inhibitors for SARS-CoV-2 RNA-dependent RNA polymerase using machine learning and virtual screening
论文作者
论文摘要
由新确定的SARS-COV-2冠状病毒引起的全球冠状病毒大流行（Covid-19）继续夺取全球成千上万人的生活。特定药物治疗COVID-19的不可用已导致使用各种方法（包括计算分析）进行药物重新定位。这样的分析主要依赖于分子对接，需要可用的靶蛋白的3D结构。在这项研究中，我们利用了一组机器学习算法，并在RNA依赖性RNA聚合酶（RDRP）抑制剂的数据集上训练它们，以基于配体信息的抗病毒和抗炎药进行推理分析。我们还对机器学习模型预测的药物候选物进行了虚拟筛查分析，并将它们停靠在SARS-COV-2 RDRP的活跃部位，这是病毒复制机制的关键组成部分。基于RDRP抑制剂的配体信息，机器学习模型能够鉴定诸如Remdesivir和Baloxavir Maroxil之类的候选者，该分子具有针对新型冠状病毒RDRP的活性的分子。在其他确定的候选药物中，有丙型肝炎病毒（HCV）RDRP酶的非核苷抑制剂和HCV蛋白酶抑制剂paritaprevir和faldaprevir。对这些候选物的进一步分析，使用分子对接针对SARS-COV-2 RDRP进行分子，发现与酶活性位点的结合能低。我们的方法还鉴定出抗炎药lupeol，lifitegrast，ontrafenine，betulinic Acid和Ursolic Acid具有对SARS-COV-2 RDRP的潜在活性。我们建议，这项研究的结果被视为进一步验证作为针对COVID-19的潜在治疗选择。
Global coronavirus disease pandemic (COVID-19) caused by newly identified SARS- CoV-2 coronavirus continues to claim the lives of thousands of people worldwide. The unavailability of specific medications to treat COVID-19 has led to drug repositioning efforts using various approaches, including computational analyses. Such analyses mostly rely on molecular docking and require the 3D structure of the target protein to be available. In this study, we utilized a set of machine learning algorithms and trained them on a dataset of RNA-dependent RNA polymerase (RdRp) inhibitors to run inference analyses on antiviral and anti-inflammatory drugs solely based on the ligand information. We also performed virtual screening analysis of the drug candidates predicted by machine learning models and docked them against the active site of SARS- CoV-2 RdRp, a key component of the virus replication machinery. Based on the ligand information of RdRp inhibitors, the machine learning models were able to identify candidates such as remdesivir and baloxavir marboxil, molecules with documented activity against RdRp of the novel coronavirus. Among the other identified drug candidates were beclabuvir, a non-nucleoside inhibitor of the hepatitis C virus (HCV) RdRp enzyme, and HCV protease inhibitors paritaprevir and faldaprevir. Further analysis of these candidates using molecular docking against the SARS-CoV-2 RdRp revealed low binding energies against the enzyme active site. Our approach also identified anti-inflammatory drugs lupeol, lifitegrast, antrafenine, betulinic acid, and ursolic acid to have potential activity against SARS-CoV-2 RdRp. We propose that the results of this study are considered for further validation as potential therapeutic options against COVID-19.
