13C NMR chemical shift prediction of diverse chemical compounds

被引:4
|
作者
Xia, Y. [1 ]
Zhang, H. [2 ]
机构
[1] Hunan Univ Technol, Sch Packaging & Mat Engn, China Key Lab Adv Packaging Mat & Technol Hunan P, Zhuzhou, Peoples R China
[2] Chinese Mech Engn Soc, Beijing, Peoples R China
关键词
C-13 NMR chemical shifts; DFT; complete set of descriptors; genetic algorithm; MLR; SVM; QSAR; CLASSIFICATION; INHIBITORS; COMPLEXES; MODELS; SVM;
D O I
10.1080/1062936X.2019.1619621
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Selection of key descriptors is very important in QSPR analysis. Presence of noise in the subset of descriptors reduces the quality of predictions. A complete set is considered as perfect when it does not include irrelevant or redundant elements. This paper reports complete sets of descriptors used to develop QSPR models for 1786 C-13 NMR chemical shifts (delta(C) parameters) of carbon atoms in 125 diverse chemical compounds. PBE1PBE/6-311G(2d,2p) and B3LYP/6-31G(d) basis sets were used for quantum chemistry calculations after the molecular structures were optimized with semi-empirical AM1 and B3LYP/6-31G(d). The two complete sets consisting of magnetic shielding elements (sigma(XX), sigma(YY), sigma(ZZ)) and the chemical shift principal values (sigma(11), sigma(22), sigma(33)) were used as the inputs for support vector machine (SVM) models of delta(C) parameters. The four SVM models obtained have the mean root mean square (rms) errors of about 4.5-4.6 ppm. The results suggest that SVM models are accurate and acceptable compared with previous models, although our models are based on a relatively large set of compounds. Our approach is valuable in the selection of important descriptors for QSPR studies of delta(C) parameters.
引用
收藏
页码:477 / 490
页数:14
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