13C NMR chemical shift prediction of diverse chemical compounds. Issue 7 (3rd July 2019)

• Record Type: Journal Article
• Authors: Xia, Y.; Zhang, H.
• Is Part Of: SAR and QSAR in environmental research. Volume 30:Issue 7(2019)
• Page Start: 477
• Page End: 490
• Publication Date: 2019-07-03
• Subjects: 13C NMR chemical shifts -- DFT -- complete set of descriptors -- genetic algorithm -- MLR -- SVM; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2019.1619621
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: 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 13 C NMR chemical shifts ( δ 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 ( σ XX, σ YY, σ ZZ ) and the chemical shift principal values ( σ 11, σ 22, σ 33 ) were used as the inputs for support vector machine (SVM) models of δ 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 δ C parameters.