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Complete sets of descriptors for the prediction of 13 C NMR chemical shifts of quinoline derivatives
Author(s) -
Yu Xinliang,
Dang Limin
Publication year - 2019
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3107
Subject(s) - quantitative structure–activity relationship , support vector machine , chemical shift , molecular descriptor , artificial intelligence , quinoline , pattern recognition (psychology) , chemistry , root mean square , partial least squares regression , mathematics , computer science , machine learning , organic chemistry , physics , quantum mechanics
Nowadays, there is no criterion to judge whether a descriptor subset has redundant structure information or not, although some criteria can be used to validate the quality of quantitative structure‐activity relationship (QSAR) models. This paper reports complete sets of descriptors used for selecting the subset of descriptors for QSAR. Four complete sets of descriptors calculated with B3LYP/6‐31G(d) and PBE1PBE/6‐311G(2d,2p) approaches were used to develop four QSAR models for 269 13 C nuclear magnetic resonance (NMR) chemical shifts ( δ C parameters) of carbon atoms in 26 quinoline derivatives. Four QSAR models for δ C , parameters were constructed with support vector machine (SVM) algorithm by applying genetic algorithm (GA) to optimize SVM parameters C and γ . The four SVM models have root‐mean‐square (RMS) error range of 2.0 ppm to 2.7 ppm. Compared with previous QSAR models for 13 C NMR chemical shifts, the prediction results are accurate, which suggest that applying complete sets of descriptors for QSAR models is successful.