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Computational QSAR models with high-dimensional descriptor selection improve antitumor activity design of ARC-111 analogues
ZHOU Wei 1,DAI Zhijun 2,CHEN Yuan 1,YUAN Zheming 2 *
1.Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha 410128, China
2. College of Bio-Safety Science & Technology, Hunan Agricultural University, Changsha 410128, China
*Correspondence author
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Funding: The Science Foundation for Distinguished Young Scholars of Hunan Province, China （No.No. 10JJ1005）, The Specialized Research Fund for the Doctoral Program of Higher Education of China （No.No. 20114320120005）
Opened online: 2 March 2012
Accepted by: none
Citation: ZHOU Wei,DAI Zhijun,CHEN Yuan.Computational QSAR models with high-dimensional descriptor selection improve antitumor activity design of ARC-111 analogues[OL]. [ 2 March 2012] http://en.paper.edu.cn/en_releasepaper/content/4468276
 
 
ARC-111 has potent topoisomerase I-targeting activity and pronounced antitumor activity. To design ARC-111 analogues with improved efficiency, we performed analyses on the quantitative structure-activity relationship (QSAR) of 22 ARC-111 analogues assessed in P388 tumor cells. First, the support vector regression (SVR) models were constructed and optimized based on literature descriptors (the low-dimensional descriptor space) and the worst descriptor elimination multi-round (WDEM) method. The optimized SVR model had greater generalization ability than multiple linear regression (MLR) and stepwise linear regression (SLR) in the independence test, which indicated that our nonlinear WDEM method could remove redundant descriptors more effectively, and our optimized SVR was a more powerful modeling technique. Second, to identify more accessible and effective descriptors, our modeling descriptors with clear meanings were selected from a large number of descriptors calculated by the software PCLIENT. Through the high-dimensional descriptor selection nonlinear (HDSN) method and the WDEM method, seven independent variable combinations with tens of descriptors were selected out of 2,923 descriptors. The seven corresponding SVR models performed better in the independent test, compared to MLR and SLR. The evaluation measures supported the excellent predictive power of the new models. According to the interpretability analysis of the SVR model, the regression significance of the model and the importance of single indicator were evaluated based on F-tests. Our work offers some useful theories for understanding the function mechanism and finds parameters for designing ARC-111 analogues with enhanced antitumor activity.
Keywords:ARC-111 analogues; P388 tumor cells; Quantitative structure-activity relationship (QSAR); Support vector regression (SVR); Descriptor selection
 
 
 

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