Citation: CONG Yong, XUE Ying. Quantitative Structure-Activity Relationship Study of the Non-Nucleoside Inhibitors of HCV NS5B Polymerase by Machine Learning Methods[J]. Acta Physico-Chimica Sinica, ;2013, 29(08): 1639-1647. doi: 10.3866/PKU.WHXB201305171 shu

Quantitative Structure-Activity Relationship Study of the Non-Nucleoside Inhibitors of HCV NS5B Polymerase by Machine Learning Methods

CONG Yong ¹ ,
XUE Ying ^1,2,

1.
1 College of Chemistry, Key Laboratory of Green Chemistry and Technology, Ministry of Education, Sichuan University, Chengdu 610064, P. R. China;
2 State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, P. R. China

Received Date: 1 February 2013
Available Online: 17 May 2013
Fund Project: 国家自然科学基金(21173151) (21173151)西华大学先进科学计算省重点实验室开放基金(szjj2011-029)资助项目 (szjj2011-029)

The quantitative structure-activity relationship (QSAR) approach was used to predict the activity of two different scaffolds (benzoisothiazole and benzothiazine) of 89 non-nucleoside inhibitors of hepatitis c virus (HCV) NS5B polymerase. Two selection methods, linear stepwise regression analysis (LSRA) and genetic al rithm-partial least squares (GA-PLS), were used to select appropriate descriptor subsets for QSAR modeling with linear models. The genetic al rithm-support vector machine (GA-SVM) approach was first used to build nonlinear models with six LSRA- and seven GA-PLS-selected descriptors. Three QSAR models built with the six LSRA-selected descriptors gave correlation coefficients of 0.958-0.962 for the training set. GA-SVM provided the highest prediction accuracy of the models of 0.962. Three QSAR models built with the seven GA-PLS-selected descriptors gave correlation coefficients of 0.918-0.960 for the training set, of which the partial least squares (PLS) model was the best (0.960). The investigated models gave satisfactory prediction results and can be extended to other QSAR studies.
- HCV NS5B polymerase,
- Non-nucleoside Inhibitor,
- Linear stepwise regression analysis,
- Partial least square,
- Genetic al rithm,
- Support vector machine
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沈阳化工大学材料科学与工程学院沈阳 110142

Quantitative Structure-Activity Relationship Study of the Non-Nucleoside Inhibitors of HCV NS5B Polymerase by Machine Learning Methods

南开大学师唯/华北电力大学（保定）刘景维：二维配位聚合物中有序的亲锂冠醚位点用于无枝晶锂沉积

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通讯作者: 陈斌, bchen63@163.com