Prediction of chromatographic properties for a group of natural phenolic derivatives by molecular topology

García-March, F. J.; Antón-Fos, G.M.; Pérez-Giménez, Facundo; Salabert-Salvador, Ma Teresa; Cercós-del-Pozo, R. A.; Julián-Ortiz, Jesús Vicente de

doi:10.1016/0021-9673(95)00450-5

Cited by 25 publications

(13 citation statements)

References 15 publications

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“…In view of the fact that some chemicals have very little testing data and also experimental determination of k 0 is time-consuming and requires high purity of sample, it would be desirable if we could develop quantitative structureproperty relationship (QSPR) models only from theoretically derived molecular descriptors that can calculate/ predict molecular properties directly from their chemical structure. QSPRs have been used to obtain simple models to explain separation mechanism, predict retention behavior, and characterize the physicochemical properties of solute in thin layer chromatography (TLC) [7], gas chromatography (GC) [8], and high-performance liquid chromatography (HPLC) [9]. Also there are some reports on QSPR studies in CE [10,11].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network prediction of retention factors of some benzene derivatives and heterocyclic compounds in micellar electrokinetic chromatography

Golmohammadi

Fatemi

2005

Electrophoresis

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A 5-4-1 artificial neural network (ANN) was constructed and trained for prediction of the retention factors of some benzene derivatives and heterocyclic compounds in micellar electrokinetic chromatography (MEKC) based on quantitative structure-property relationship (QSPR). The inputs of this network are theoretically derived descriptors that were chosen by the stepwise variable selection techniques. These descriptors are: molecular surface area, maximum value of electron density on atom in molecule, path four connectivity index, average molecular weight, and sum of atomic polarizability which were selected by using stepwise multiple linear regression as a feature selection technique. The standard errors of training, test, and validation sets for the ANN model are 0.091, 0.119, and 0.114, respectively. Results obtained showed that nonlinear model can simulate the relationship between the structural descriptors and the retention factors of the molecules in data set accurately. Also the appearance of these descriptors in QSPR models reveals the role of electronic and steric interactions in solute retention in MEKC.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network prediction of retention factors of some benzene derivatives and heterocyclic compounds in micellar electrokinetic chromatography

Golmohammadi

Fatemi

2005

Electrophoresis

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“…Reliable correlations can be found by heuristic multiple linear regression (MLR) techniques [11] or by non-linear techniques such as genetic function approximation [12] and artificial neural networks (ANN) [13]. QSPRs have been used extensively to explain separation mechanisms, predict retention behavior and characterize the physicochemical properties of solutes in TLC [14], GC [15], and HPLC [16,17]. Also there are some reports about applications of QSPR in CE [18 -20].…”

Section: Introductionmentioning

confidence: 99%

Quantitative structure migration relationship modeling of migration factor for some benzene derivatives in micellar electrokinetic chromatography

Fatemi

Shamseddin

Malekzadeh

2009

J of Separation Science

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Multiple linear regression (MLR) and artificial neural network (ANN) were used to predict the migration factors of benzene derivatives in MEKC. Some topological and electronic descriptors were calculated for each solute in the data set, and then the stepwise MLR method was used to select more significant descriptors and MLR model development. The selected descriptors are: Kier & Hall index (order1), relative negative charge surface area, HA dependent HDSA-2/TMSA, C component of moment of inertia, Y component of dipole moment and SDS to decanol ratio in mobile phase. In the next step these descriptors were used as input of an ANN. After optimization and training of ANN it was used to predict the migration factors of external test set as well as internal and training sets. The root mean square errors for ANN predicted migration factors of training, internal and external test set were 0.110, 0.231 and 0.228, respectively, while these values are 0.200, 0.240 and 0.247 for the MLR model, respectively. Comparison between these values and other statistical parameters for these two models revealed that there were not any significant differences between ANN and MLR in prediction of solute migration factors in MEKC.

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“…Recent studies have shown that molecular connectivity satisfactorily predicts chromatographic parameters such as the retention times in gas-liquid chromatography [ 10] and RF values in thin layer chromatography (TLC) [10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of chromatographic properties of organophosphorus Insecticides by molecular connectivity

et al. 2000

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SummaryA study is reported of the relationship between the RF values for a group of organophosphorus insecticides obtained by thin layer chromatography and a series of topological descriptors. By using multivariate regression, the corresponding connectivity functions were obtained, which had been selected on the basis of their respective statistical parameters: multiple correlation coefficient (r), standard error of estimate (s), F-Snedecor values and statistical significance (Student's t). Regression analysis of the connectivity functions can predict the elution behaviour of any structurally similar derivative of this group of compounds with different stationary and mobile phases. Stability studies of the prediction models selected were carried out and yielded good stability in all cases. These results demonstrate the ability of Molecular Topology to identify and predict different structural features that determine the RF values of organophosphorus insecticides obtained by TLC.

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Prediction of chromatographic properties for a group of natural phenolic derivatives by molecular topology

Cited by 25 publications

References 15 publications

Artificial neural network prediction of retention factors of some benzene derivatives and heterocyclic compounds in micellar electrokinetic chromatography

Artificial neural network prediction of retention factors of some benzene derivatives and heterocyclic compounds in micellar electrokinetic chromatography

Quantitative structure migration relationship modeling of migration factor for some benzene derivatives in micellar electrokinetic chromatography

Prediction of chromatographic properties of organophosphorus Insecticides by molecular connectivity

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