QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines

Xiaojun Yao, Huanxiang Liu, Ruisheng Zhang, Mancang Liu, Zhide Hu, A. Panaye, J. P. Doucet, Botao Fan

研究成果: Article同行評審

63 引文 斯高帕斯(Scopus)

摘要

The least squares support vector machine (LSSVM), as a novel machine learning algorithm, was used to develop quantitative and classification models as a potential screening mechanism for a novel series of 1,4-dihydropyridine calcium channel antagonists for the first time. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modeling results in a nonlinear, seven-descriptor model based on LSSVM with mean-square errors 0.2593, a predicted correlation coefficient (R2) 0.8696, and a cross-validated correlation coefficient (Rcv2) 0.8167. The best classification results are found using LSSVM: the percentage (%) of correct prediction based on leave one out cross-validation was 91.1%. This paper provides a new and effective method for drug design and screening.

原文English
頁(從 - 到)348-356
頁數9
期刊Molecular Pharmaceutics
2
發行號5
DOIs
出版狀態Published - 9月 2005
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