关键词: 原子电性距离矢量(VAED), ¹³C NMR波谱模拟, γ效应校正, π效应, 碱基分子

Abstract: Abundant structural information can be provided by carbon-13 nuclear magnetic resonance (¹³C NMR) in organic analysis, and recently more and more attentions have been paid on its molecular modelling and quantitative predication which on the basis of the relationship of carbon-13 nuclear magnetic resonance with chemical structure description. By using multiple linear regression(MLR) and factor analysis(FA) methods, quantitative ¹³C NMR models are achieved to express correlation of ¹³C NMR chemical shifts with structural parameters and the chemical shift(CS) of any other compounds is successfully predicted.
The history and progress in quantitative-spectral relationship(QSSR) were crifically reviewed. MLR and FA methods were simply introduced. The Matlab and True Basic programs for quantitative molecular modelling(QMM) were designed by ourselves. The studies of all 20 inartificial amino bases' NMR CS were estimated and predicted with the atom distance-edge vector(ADEV) and γ calibration. The result indicated that between CS and ADEV there exists a simple multiple linear relationship. Another parameter vector called the molecular path vector(VMP) was also used to model the chemical shift sum (CSS) of 150 alkanes, VMP with length 1 though 4 as well correlated with CSS more efficiently. Quantitative structure-toxicity relationship(QSTR) were also developed that link molecular structure of a set of 50 alkylated phenols with their polar narcosis toxicity by employing their electronic, steric and hydrophoibic parameters.

Key words: Nuclear magnetic resonance, Multiple linear regression, Factor analysis, Atom distance edge-vector, γ calibration, Chemical shift sum, Molecular path vector, Quantitative structure-spectral relationship, Quantitative structure-toxicity relationship, Amino acid, Alkanes, Alkylated phenols