title: Predicting retention times of naturally occurring phenolic compounds in reversed-phase liquid chromatography: a Quantitative Structure-Retention Relationship (QSRR) approach.
abstract: 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.
authors.affiliation: Department of Chemistry, University of Sargodha, Sargodha 40100, Pakistan. chankw@ibs.upm.edu.my.
authors.forename: Jamshed
authors.initials: J
authors.lastname: Akbar
authors.forename: Shahid
authors.initials: S
authors.lastname: Iqbal
authors.forename: Fozia
authors.initials: F
authors.lastname: Batool
authors.forename: Abdul
authors.initials: A
authors.lastname: Karim
authors.forename: Kim Wei
authors.initials: KW
authors.lastname: Chan
mesh_terms: D056148:Chromatography, Reverse-Phase; D008956:Models, Chemical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010636:Phenols / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D010636:Phenols