- 作者: Chunsheng Yina,c, Weimin Guoc, Teng Lina, Shushen Liua,b, Rongqiang Fuc, Zhongxiao Panc and Liansheng Wanga
- 中文摘要:
aSchool of the Environment, Nanjing University, Nanjing, 210093, P. R. China
bCollege of Bioengineering, Chongqing University, Chongqing, 400044, P. R. China
cDepartment of Applied Chemistry, University of Science and Technology of China, Hefei, 230026, P. R. China
A wavelet neural network (WNN) is employed to create a quantitative structure-retention index relationship, which correlates the novel molecular distance edge vector (MDEV)-consisting of ten elements to Gas Chromatographic retention indexes (RIGC) of Alkanes. The RIGC has been calculated by the WNN from the molecular topological descriptors of examined alkanes. In this work, the RIGC estimated and predicted by conventional neural networks (say back propagation neural networks, BP) has also been provided. The excellent predicted results with a correlation of 0.9996 and standard deviation of 5.0598 suggest that the WNN technique is a powerful tool in QSAR/QSPR modeling and superior to the BP neural networks. - 英文摘要: --
- 中文關鍵字: --
- 英文關鍵字: --