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Simulation of ^13C NMR chemical shifts of carbinol carbon atoms using quantitative structure-spectrum relationships

ISSN号：1009-2501
期刊名称：《中国临床药理学与治疗学》
时间：0
分类：O657.2[理学—分析化学;理学—化学] O562.3[理学—原子与分子物理;理学—物理]
作者机构：[1]School of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China, [2]Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation,Changsha University of Science and Technology, Changsha 410004, China
相关基金：Projects（20775010, 21075011） supported by the National Natural Science Foundation of China; Project（2008AA05Z405） supported by the National High-tech Research and Development Program of China; Project（09JJ3016） supported by the Natural Science Foundation of Hunan Province, China; Project（09C066） supported by the Scientific Research Fund of Hunan Provincial Education Department, China; Project（2010CL01） supported by the Foundation of Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation, China

作者：戴益民[1,2], 黄可龙[1], 李浔[2], 曹忠[2], 朱志平[2], 杨道武[2]

关键词：核磁共振光谱, 原子光谱, 碳原子, 甲醇, 模拟, 模型预测, 化学, 定量构谱关系, carbinol carbon atom, ^13C nuclear magnetic resonance, chemical shift, topological indices, quantitative structure-spectroscopy relationship

中文摘要：

一个量的结构光谱关系(QSSR ) 模型被开发模仿 13C 为 55 白酒的甲醇碳原子的原子磁性的回声(NMR ) 系列。用多重线性回归，建议模型包含了完全从混合物的分子的结构提取的四个描述符。最后的模型的统计结果显示出那 R 2=0.982 4 并且 S=0.869 8 (在 R 是的地方，关联系数和 S 是标准差) 。测试它的预兆的能力，模型进一步被用来预言没在发达模型被包括的另外的九混合物的甲醇碳原子的 13C NMR 系列。分别地，平均相对错误为训练集合和预兆的集合是 0.94% 和 1.70% 。模型是统计上重要的并且为由 leave-one-out (厕所) 测试了交叉验证的数据变化显示出好稳定性。有另外的途径的比较也揭示这个方法的好性能。

英文摘要：

A quantitative structure-spectrum relationship （QSSR） model was developed to simulate ^13C nuclear magnetic resonance （NMR） spectra of carbinol carbon atoms for 55 alcohols. The proposed model, using multiple linear regression, contained four descriptors solely extracted from the molecular structure of compounds. The statistical results of the final model show that R2= 0.982 4 and S=0.869 8 （where R is the correlation coefficient and S is the standard deviation）. To test its predictive ability, the model was further used to predict the ^13C NMR spectra of the carbinol carbon atoms of other nine compounds which were not included in the developed model. The average relative errors are 0.94% and 1.70%, respectively, for the training set and the predictive set. The model is statistically significant and shows good stability for data variation as tested by the leave-one-out （LOO） cross-validation. The comparison with other approaches also reveals good performance of this method.

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