Quantitative structure-retention relationship study of some phenol derivatives in gas chromatography

Predicting retention times of naturally occurring phenolic compounds in reversed-phase liquid chromatography: a Quantitative Structure-Retention Relationship (QSRR) approach

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance.

Modeling of 13C NMR chemical shifts of benzene derivatives using the RC–PC–ANN method: A comparative study of original molecular descriptors and multivariate image analysis descriptors

The primary goal of a quantitative structure–property relationship study is to identify a set of structurally based numerical descriptors that can be mathematically linked to a property of interest. In this work, two main groups of descriptors have been used to predict 13C NMR chemical shifts of ipso, ortho, meta, and para positions in a series of 113 monosubstituted benzenes. First, two groups of descriptors — original molecular descriptors (constitutional, topological, electronic, and geometrical) and multivariate image analysis (MIA) descriptors — were calculated. Then, calculated descriptors were subjected to principal component analysis and the most significant principal components were extracted. Finally, more correlated principal components were used as inputs of artificial neural networks. The results obtained using the rank correlation–principal component–artificial neural network (RC–PC–ANN) modeling method show high ability to predict 13C NMR chemical shifts. Also, comparison of the results indicates that MIA descriptors show better ability to predict 13C NMR chemical shifts than the original molecular descriptors.