QSAR and classification models of a novel series of COX-2 selective inhibitors: 1, 5-diarylimidazoles based on support vector machines

H. X. Liu, R. S. Zhang, X. J. Yao, M. C. Liu, Z. D. Hu, B. T. Fan

研究成果: Article同行評審

41 引文 斯高帕斯(Scopus)

摘要

The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitation and classification models which can be used as a potential screening mechanism for a novel series of COX-2 selective inhibitors. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The Heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modelling results in a nonlinear, seven-descriptor model based on SVMs with root mean-square errors of 0.107 and 0.136 for training and prediction sets, respectively. The best classification results are found using SVMs: the accuracy for training and test sets is 91.2% and 88.2%, respectively. This paper proposes a new and effective method for drug design and screening.

原文English
頁(從 - 到)389-399
頁數11
期刊Journal of Computer-Aided Molecular Design
18
發行號6
DOIs
出版狀態Published - 6月 2004
對外發佈

指紋

深入研究「QSAR and classification models of a novel series of COX-2 selective inhibitors: 1, 5-diarylimidazoles based on support vector machines」主題。共同形成了獨特的指紋。

引用此