scholarly journals Prediction of the retention of β-diketonato complexes in TLC systems on silica gel by quantitative structure-retention relationships

2010 ◽  
Vol 75 (4) ◽  
pp. 513-521 ◽  
Author(s):  
Rada Baosic ◽  
Ana Radojevic ◽  
Zivoslav Tesic
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Surface Area ◽  
Molecular Orbital ◽  
Silica Gel ◽  
Molecular Descriptors ◽  
Quantitative Structure ◽  
Hydrophilic Lipophilic Balance ◽  
Structure Retention ◽  
Retention Parameters

Quantitative structure-retention relationships for a series of 30 mixed ?-diketonato complexes of cobalt(III), chromium(III) and ruthenium(III) were derived by multiple linear regression analyses using molecular descriptors obtained by quantum chemical calculations. The retention parameters were obtained by thin layer chromatography on silica gel using mono and two-component solvent systems. The molecular descriptors included in the multiple linear regression analysis were molecular weight, molecular volume, surface area, hydrophilic-lipophilic balance, percent hydrophilic surface area, dipole moment, polarizability, refractivity, energy of the highest occupied molecular orbital and energy of the lowest unoccupied molecular orbital. High agreement between the experimental and predicted retention parameters was obtained when polarizability and the hydrophilic-lipophilic balance were used as the molecular descriptors. Comparison of the models with those established on polyacrylonitrile showed that the structure of the sorbent is responsible for the chromatographic behaviour of the same compounds. The presented models can be used for the prediction of the retention of new solutes in screening chromatographic systems.

scholarly journals Chemical composition of the essential oils of Citrus sinensis cv. valencia and a quantitative structure-retention relationship study for the prediction of retention indices by multiple linear regression

2011 ◽  
Vol 76 (12) ◽  
pp. 1627-1637 ◽  
Author(s):  
Aberoomand Azar ◽  
Mehdi Nekoei ◽  
Kambiz Larijani ◽  
Sakineh Bahraminasab
Keyword(s):  
Gas Chromatography ◽  
Chemical Composition ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Retention Indices ◽  
Quantitative Structure ◽  
Cold Press ◽  
Prediction Of Retention ◽  
Structure Retention ◽  
Quantitative Structure Retention Relationship

The chemical composition of the volatile fraction obtained by head-space solid phase microextraction (HS-SPME), single drop microextraction (SDME) and the essential oil obtained by cold-press from the peels of C. sinensis cv. valencia were analyzed employing gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC-MS). The main components were limonene (61.34 %, 68.27 %, 90.50 %), myrcene (17.55 %, 12.35 %, 2.50 %), sabinene (6.50 %, 7.62 %, 0.5 %) and ?-pinene (0 %, 6.65 %, 1.4 %) respectively obtained by HS-SPME, SDME and cold-press. Then a quantitative structure-retention relationship (QSRR) study for the prediction of retention indices (RI) of the compounds was developed by application of structural descriptors and the multiple linear regression (MLR) method. Principal components analysis was used to select the training set. A simple model with low standard errors and high correlation coefficients was obtained. The results illustrated that linear techniques such as MLR combined with a successful variable selection procedure are capable of generating an efficient QSRR model for prediction of the retention indices of different compounds. This model, with high statistical significance (R2 train = 0.983, R2 test = 0.970, Q2 LOO = 0.962, Q2 LGO = 0.936, REP(%) = 3.00), could be used adequately for the prediction and description of the retention indices of the volatile compounds.

Predicting Carcinogenicity of Anilines by Quantitative Structure-Toxicity Relationship

2014 ◽  
Vol 665 ◽  
pp. 559-562 ◽  
Author(s):  
Zhi Xiang Zhou ◽  
Yang Hua Liu ◽  
Xiao Long Zhang
Keyword(s):  
Molecular Structure ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Molecular Descriptors ◽  
Quantitative Structure ◽  
Test Set ◽  
Proposed Model ◽  
Structure Toxicity Relationship ◽  
Semi Empirical ◽  
Toxicity Relationship

Carcinogenicity is an important toxicological endpoint which poses a great concern being the major determinants of cancers and tumours. Anilines possess such toxic properties as they can form various electrophilic intermediates and adducts with biological systems. In the present work, the molecular descriptors of anilines have been calculated with semi-empirical AM1 and E-dragon methods, and a quantitative structure–toxicity relationships (QSTR) model for carcinogenic potency (pTD50) model of anilines was developed with multiple linear regression (MLR) analysis. The validation results through the test set indicate that the proposed model is robust and satisfactory. The QSTR study suggests that the molecular structure and the electronegativity of chemicals are closely related to the Carcinogenicity.

QSTR Study for the Carcinogenicity of Alkylbenzenes

2014 ◽  
Vol 665 ◽  
pp. 567-570
Author(s):  
Zhi Xiang Zhou ◽  
Yang Hua Liu
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Health Problem ◽  
Molecular Descriptors ◽  
Determinants Of Health ◽  
Quantitative Structure ◽  
Structure Toxicity Relationship ◽  
Semi Empirical ◽  
Toxicity Relationship

Carcinogenicity is an important toxicological endpoint which poses a great concern being the major determinants of health problem, a quantitative structure toxicity relationship (QSTR) study was performed for the prediction of the carcinogenicity of alkylbenzenes. The molecular descriptors of alkylbenzenes have been calculated with semi-empirical AM1 and E-dragon methods, and QSTR model for mice carcinogenic model of alkylbenzenes were developed using multiple linear regression (MLR) analysis.

scholarly journals Estimation of the correlation between the retention of s-triazine derivatives and some molecular descriptors

2011 ◽  
pp. 231-239 ◽  
Author(s):  
Lidija Jevric ◽  
Gordana Koprivica ◽  
Nevena Misljenovic ◽  
Aleksandra Tepic ◽  
Tatjana Kuljanin ◽  
...  
Keyword(s):  
Linear Regression ◽  
Molecular Descriptors ◽  
Reversed Phase ◽  
Quantitative Structure ◽  
Mobile Phases ◽  
Physico Chemical ◽  
Triazine Derivatives ◽  
Structure Retention ◽  
Layer Chromatography ◽  
Quantitative Structure Retention Relationship

In this study, 14 newly synthesized s-triazine derivatives were investigated by means of reversed-phase thin-layer chromatography (TLC) on C-18 stationary and two different mobile phases: acetonitrile-water and methanol-water. Quantitative structure-retention relationship (QSRR) was developed for a series of s-triazine compounds by the multiple linear regression (MLR) analysis. An MLR procedure was used to model the relationships between molecular descriptors and retention of s-triazine derivatives. Physico-chemical molecular descriptors were calculated from the optimized structures. Statistically significant and physically meaningful QSRRs were obtained.

Quantitative Structure-Toxicity Relationship for Predicting Acute Toxicity of Alkylbenzenes

2014 ◽  
Vol 665 ◽  
pp. 571-574 ◽  
Author(s):  
Zhi Xiang Zhou ◽  
Yang Hua Liu
Keyword(s):  
Linear Regression ◽  
Acute Toxicity ◽  
Multiple Linear Regression ◽  
Health Problem ◽  
Molecular Descriptors ◽  
Determinants Of Health ◽  
Quantitative Structure ◽  
Structure Toxicity Relationship ◽  
Semi Empirical ◽  
Toxicity Relationship

Acute toxicity is an important toxicological endpoint which poses a great concern being the major determinants of health problem, a quantitative structure toxicity relationship (QSTR) study was performed for the prediction of the acute toxicity of alkylbenzenes. The molecular descriptors of alkylbenzenes have been calculated with semi-empirical AM1 and E-dragon methods, and QSTR model for mice via the oral LD50 model of alkylbenzenes was developed using multiple linear regression (MLR) analysis.

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