结合机器学习和分子建模方法确定新型SARS-CoV-2抑制剂

IF 1.9 4区 医学 Q3 CHEMISTRY, MEDICINAL
Ersin Güner, Özgür Özkan, Gözde Yalcin-Ozkat, Süreyya Ölgen
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引用次数: 0

摘要

在项目范围内,本研究旨在结合计算方法寻找新的抑制剂。为了设计抑制剂,目的是利用深度学习方法产生类似于RdRp抑制剂药物Favipiravir的分子。方法:为此,采用简化分子输入线输入系统(SMILES)表示,利用训练神经网络(TNN)生成75个与Favipiravir相似的分子。采用分子对接方法研究了分子与病毒RNA依赖性RNA聚合酶(RdRp)的结合特性。为了证实该方法的准确性,还测试了化合物对3CL蛋白酶(3CLpro)的作用,3CL蛋白酶是SARS-CoV-2进展的另一个重要酶。在ChEMBL药物数据库中进行相似性分析,寻找结合能和RMSD值比favipiravir更好的化合物,寻找与RdRp和3CLpro抑制活性相似的结构。结果:相似性搜索发现了新的200个潜在的RdRp和3CLpro抑制剂,其结构与产生的分子相似,这些化合物再次通过分子对接研究评估其受体相互作用。化合物与RdRp蛋白酶的相互作用优于3CLpro。这一结果表明,人工智能正确地产生了类似于favipiravir的结构,更特异性地作为RdRp抑制剂。此外,将Lipinski规则应用于与RdRp相互作用最好的分子,确定了7种化合物作为潜在的候选药物。其中,我们对ChEMBL ID:1193133进行了分子动力学模拟研究,以更好地了解该化合物在受体位点的存在和持续时间。结论:化合物ChEMBL ID:1193133具有良好的均方根偏差(RMSD)、均方根波动(RMSF)、氢键和活性位点剩余时间;因此,人们认为它可能对病毒有活性。该化合物还进行了抗病毒活性测试,并确定它不能延缓病毒感染,尽管它在5mg/mL-1.25mg/mL浓度之间具有细胞毒性。然而,如果可以测试其他化合物,它可能提供一个获得活性的机会,并且化合物也应该针对酶和其他类型的病毒进行测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determination of Novel SARS-CoV-2 Inhibitors by Combination of Machine Learning and Molecular Modeling Methods.

Introduction: Within the scope of the project, this study aimed to find novel inhibitors by combining computational methods. In order to design inhibitors, it was aimed to produce molecules similar to the RdRp inhibitor drug Favipiravir by using the deep learning method.

Methods: For this purpose, a Trained Neural Network (TNN) was used to produce 75 molecules similar to Favipiravir by using Simplified Molecular Input Line Entry System (SMILES) representations. The binding properties of molecules to Viral RNA-dependent RNA polymerase (RdRp) were studied by using molecular docking studies. To confirm the accuracy of this method, compounds were also tested against 3CL protease (3CLpro), which is another important enzyme for the progression of SARS-CoV-2. Compounds having better binding energies and RMSD values than favipiravir were searched with similarity analysis on the ChEMBL drug database in order to find similar structures with RdRp and 3CLpro inhibitory activities.

Results: A similarity search found new 200 potential RdRp and 3CLpro inhibitors structurally similar to produced molecules, and these compounds were again evaluated for their receptor interactions with molecular docking studies. Compounds showed better interaction with RdRp protease than 3CLpro. This result presented that artificial intelligence correctly produced structures similar to favipiravir that act more specifically as RdRp inhibitors. In addition, Lipinski's rules were applied to the molecules that showed the best interaction with RdRp, and 7 compounds were determined to be potential drug candidates. Among these compounds, a Molecular Dynamic simulation study was applied for ChEMBL ID:1193133 to better understand the existence and duration of the compound in the receptor site.

Conclusion: The results confirmed that the ChEMBL ID:1193133 compound showed good Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), hydrogen bonding, and remaining time in the active site; therefore, it was considered that it could be active against the virus. This compound was also tested for antiviral activity, and it was determined that it did not delay viral infection, although it was cytotoxic between 5mg/mL-1.25mg/mL concentrations. However, if other compounds could be tested, it might provide a chance to obtain activity, and compounds should also be tested against the enzymes as well as the other types of viruses.

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来源期刊
Medicinal Chemistry
Medicinal Chemistry 医学-医药化学
CiteScore
4.30
自引率
4.30%
发文量
109
审稿时长
12 months
期刊介绍: Aims & Scope Medicinal Chemistry a peer-reviewed journal, aims to cover all the latest outstanding developments in medicinal chemistry and rational drug design. The journal publishes original research, mini-review articles and guest edited thematic issues covering recent research and developments in the field. Articles are published rapidly by taking full advantage of Internet technology for both the submission and peer review of manuscripts. Medicinal Chemistry is an essential journal for all involved in drug design and discovery.
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