A SAR and QSAR study on 3CLpro inhibitors of SARS-CoV-2 using machine learning methods.

IF 2.3 3区 环境科学与生态学 Q3 CHEMISTRY, MULTIDISCIPLINARY
Y Zhang, Y Tian, A Yan
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引用次数: 0

Abstract

The 3C-like Proteinase (3CLpro) of novel coronaviruses is intricately linked to viral replication, making it a crucial target for antiviral agents. In this study, we employed two fingerprint descriptors (ECFP_4 and MACCS) to comprehensively characterize 889 compounds in our dataset. We constructed 24 classification models using machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), extreme Gradient Boosting (XGBoost), and Deep Neural Networks (DNN). Among these models, the DNN- and ECFP_4-based Model 1D_2 achieved the most promising results, with a remarkable Matthews correlation coefficient (MCC) value of 0.796 in the 5-fold cross-validation and 0.722 on the test set. The application domains of the models were analysed using dSTD-PRO calculations. The collected 889 compounds were clustered by K-means algorithm, and the relationships between structural fragments and inhibitory activities of the highly active compounds were analysed for the 10 obtained subsets. In addition, based on 464 3CLpro inhibitors, 27 QSAR models were constructed using three machine learning algorithms with a minimum root mean square error (RMSE) of 0.509 on the test set. The applicability domains of the models and the structure-activity relationships responded from the descriptors were also analysed.

利用机器学习方法对 SARS-CoV-2 的 3CLpro 抑制剂进行 SAR 和 QSAR 研究。
新型冠状病毒的 3C 样蛋白酶(3CLpro)与病毒复制密切相关,因此成为抗病毒药物的关键靶点。在本研究中,我们采用了两种指纹描述符(ECFP_4 和 MACCS)来全面描述数据集中的 889 种化合物。我们利用支持向量机(SVM)、随机森林(RF)、极端梯度提升(XGBoost)和深度神经网络(DNN)等机器学习算法构建了 24 个分类模型。在这些模型中,基于 DNN 和 ECFP_4 的模型 1D_2 取得了最理想的结果,在 5 倍交叉验证中的马修斯相关系数 (MCC) 值为 0.796,在测试集上为 0.722。利用 dSTD-PRO 计算分析了模型的应用领域。利用 K-means 算法对收集到的 889 个化合物进行聚类,并对获得的 10 个子集分析了高活性化合物的结构片段与抑制活性之间的关系。此外,基于 464 个 3CLpro 抑制剂,使用三种机器学习算法构建了 27 个 QSAR 模型,测试集上的最小均方根误差(RMSE)为 0.509。此外,还分析了这些模型的适用域以及从描述符中反应出的结构-活性关系。
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来源期刊
CiteScore
5.20
自引率
20.00%
发文量
78
审稿时长
>24 weeks
期刊介绍: SAR and QSAR in Environmental Research is an international journal welcoming papers on the fundamental and practical aspects of the structure-activity and structure-property relationships in the fields of environmental science, agrochemistry, toxicology, pharmacology and applied chemistry. A unique aspect of the journal is the focus on emerging techniques for the building of SAR and QSAR models in these widely varying fields. The scope of the journal includes, but is not limited to, the topics of topological and physicochemical descriptors, mathematical, statistical and graphical methods for data analysis, computer methods and programs, original applications and comparative studies. In addition to primary scientific papers, the journal contains reviews of books and software and news of conferences. Special issues on topics of current and widespread interest to the SAR and QSAR community will be published from time to time.
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