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使用机器学习方法对SARS-CoV-2的3CLpro抑制剂进行了SAR和QSAR研究

Y Zhang¹, Y Tian¹, A Yan¹

¹State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China.

SAR and QSAR in environmental research

|July 30, 2024

概括

机器学习模型被开发用于识别新型冠状病毒3C类蛋白酶 (3CLpro) 抑制剂. 使用ECFP_4描述符的深度神经网络模型在预测抗病毒活性方面显示出高准确性.

科学领域:

计算化学和化学信息学
药物发现和药物化学
在药物设计中使用机器学习.

背景情况:

3C类蛋白酶 (3CLpro) 对于新型冠状病毒复制是必不可少的,也是抗病毒药物开发的关键目标.
了解结构-活性关系 (SAR) 对于设计有效的抑制剂至关重要.

研究的目的:

开发和评估用于预测3CLpro抑制活性的机器学习模型.
确定与强效抗病毒化合物相关的关键结构特征.
分析开发的预测模型的应用领域.

主要方法:

使用ECFP_4和MACCS指纹描述器对889种化合物的表征.
使用SVM,RF,XGBoost和DNN算法构建和评估24个分类模型.
使用d_STD-PRO计算对模型适用性领域的分析.
K-表示化合物的聚类和活性子集的SAR分析.
为464种3CLpro抑制剂开发27种定量结构-活性关系 (QSAR) 模型.

主要成果:

基于DNN和ECFP_4的1D_2模型实现了高性能,MCC值为0.796 (交叉验证) 和0.722 (测试集).
在QSAR模型中,测试组的RMSE最低值为0.509.
SAR分析揭示了结构碎片和抑制活动之间的关系.

结论:

机器学习,特别是带有ECFP_4描述符的DNN,显示出在识别新型3CLpro抑制剂方面显著的前景.
开发的模型和SAR洞察力可以指导针对冠状病毒的新抗病毒剂的设计.
定量结构-活动关系 (QSAR) 建模为预测化合物活动提供了强大的框架.

关键词:

3CLpro 抑制剂的使用这就是SARS-CoV-2病毒.机器学习是机器学习.量化结构与活动关系 (QSAR)结构-活动关系 (SAR)

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