使用多元线性分析和支持向量机预测HIV-1整合酶ST抑制剂的生物活性

Prediction of bioactivity of HIV-1 integrase ST inhibitors by multilinear regression analysis and support vector machine

Xuan, S.Y.; Wu, Y.B.; Chen, X.F.; Liu, J.; Yan, A.X.*
Bioorganic & Medicinal Chemistry Letters, 2013,23(6), 1648-1655.

    在这项研究中,我们建立了四个HIV-1整合酶转移(ST)过程抑制剂活性的定量预测模型。这些模型包含了551个使用放射性标记方法测试得到的抑制剂。用20个MOE描述符对这些分子进行表征。采用两种方法,将所有抑制剂划分为训练集和测试集:(1)采用Kohonen的自组织映射(SOM)法;(2)通过随机选择。对于每个训练集和测试集,分别使用多线性回归(MLR)分析和支持向量机(SVM)建模。对于SOM划分得到的测试集,相关系数(rs)大于0.91,对于随机划分得到的测试集,rs大于0.86。

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    In this study, four computational quantitative structure–activity relationship models were built to predict the biological activity of HIV-1 integrase strand transfer (ST) inhibitors. 551 Inhibitors whose bioactivities were detected by radiolabeling method were collected. The molecules were represented with 20 selected MOE descriptors. All inhibitors were divided into a training set and a test set with two methods: (1) by a Kohonen’s self-organizing map (SOM); (2) by a random selection. For every training set and test set, a multilinear regression (MLR) analysis and a support vector machine (SVM) were used to establish models, respectively. For the test set divided by SOM, the correlation coefficients (rs) were over 0.91, and for the test set split randomly, the rs were over 0.86.

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QSAR Models performance:   Dataset (551 HIV-1 Integrase ST inhibitors)

Model Name Algorithm Descriptors Spliting method Training set numbers Training set r Training set RMSE Training set MAE Test set numbers Test set r Test set RMSE Test set MAE
Model 1A MLR 20 MOE descriptors Kohonen’s self-organizing map (SOM) 355 0.89 0.58 0.48 196 0.91 0.41 0.44
Model 2A MLR 20 MOE descriptors Random 368 0.91 0.54 0.44 183 0.86 0.47 0.52
Model 1B SVM 20 MOE descriptors Kohonen’s self-organizing map (SOM) 355 0.97 0.21 0.13 196 0.93 0.36 0.39
Model 2B SVM 20 MOE descriptors Random 368 0.99 0.21 0.13 183 0.90 0.41 0.44

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