scholarly journals QSPR Analysis of Peroxidase Substrates Reactivity

2009 ◽  
Vol 3 (4) ◽  
pp. 255-261
Author(s):  
Irina Romanovskaya ◽  
◽  
Victor Kuz’min ◽  
Olga Oseychuk ◽  
Eugeniy Muratov ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Linear Regression ◽  
Horseradish Peroxidase ◽  
Quantitative Structure ◽  
Structure Property ◽  
Substituted Phenols ◽  
Phenol Derivatives ◽  
Oxidative Reactions ◽  
Structure Property Relationship ◽  
Electronic Parameters

Quantitative structure-property relationship (QSPR) analysis of phenol derivatives reactivity in the horseradish peroxidase catalyzed oxidative reactions was carried out. The statistic models, which describe the substituted phenols reactivity (Кm-1, Vmax) quite adequately, were obtained by multiple linear regression and partial least squares (PLS) methods. The electronic parameters of molecules, their lipophylicity, molecular refraction, and form parameters were used as descriptors for molecular structure. The obtained models allow to predict the reactivity of the new phenolic substrates with satisfactory reliability.

Quantitative Structure-Property Relationship (QSPR) Study on the Complex Compounds Formed From Gadolinium(III) with Dibutyl Dithiophosphate Derivatives Ligands Based on Molecular Descriptors

2021 ◽  
Vol 874 ◽  
pp. 171-181
Author(s):  
Nurdeni ◽  
Atje Setiawan Abdullah ◽  
Budi Nurani Ruchjana ◽  
Anni Anggraeni ◽  
Annisa Nur Falah ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Bond Length ◽  
Bond Angle ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Complex Compounds ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Property Relationship

A study of the quantitative relationship of structure and property (Quantitative Structure Property Relationship (QSPR) has been carried out on complex compounds formed between gadolinium (Gd) and dibutyldithiophosphate (DBDTP) derivative ligands. This study is a part of our laboratory research program on the development of extractant ligands, including DBDTP in extraction for the separation and purification of rare-earth elements (REEs), specifically Gd. Gadolinium has also been a part of the research program about its use in the synthesis of magnetic resonance imaging (MRI) contrast agents, for the diagnosis of various diseases. This chemical calculation research aims to analyze the effect of descriptors in the form of parameters of the physical-chemical properties of bond lengths, bond angles, and bond energies on the stability of Gd complex compounds with DBDTP derivative ligands. To get descriptors PM7 semi-empirical method was used, while for data analysis, Multiple Linear Regression Analysis was used, assuming the model error is normally distributed with zero mean and constant variance. Furthermore, data processing was done using SPSS software. This research was conducted by involving 28 DBDTP derivative ligands and using multiple linear regression analysis. The regression equation is Y ̂ = - 0.966 + 0.586 V1 - 0.014 V2 + 0.000 V3. From the resulted research data it was found that there are three findings, namely: (1) bond length and bond angle have a significant simultaneous effect on stability of Gd complex compounds with DBDTP derivative ligands; (2) bond length and bond angle have a partially significant effect on stability of Gd complex compounds with DBDTP derivative ligands; (3) bond length proved to have a significant dominant effect on stability of Gd complex compounds with DBDTP derivative ligands.

Predicting Gas Chromatographic Retention Time of Polychlorinated Dibenzo-p-Dioxins from Molecular Structure

2002 ◽  
Vol 216 (4) ◽  
Author(s):  
Eduardo J. Delgado ◽  
Adelio Matamala ◽  
Joel B. Alderete
Keyword(s):  
Molecular Structure ◽  
Retention Time ◽  
Chromatographic Retention ◽  
Quantitative Structure ◽  
Structure Property ◽  
Qspr Model ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship

A quantitative structure-property relationship (QSPR) model is developed to correlate the gas chromatographic retention time of polychlorinated dibenzo-

scholarly journals Modeling of linear and nonlinear quantitative structure property relationships of the aqueous solubility of phenol derivatives

2019 ◽  
Vol 84 (6) ◽  
pp. 575-590 ◽  
Author(s):  
Soumaya Kherouf ◽  
Nabil Bouarra ◽  
Amel Bouakkadia ◽  
Djelloul Messadi
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Solubility ◽  
Aqueous Solubility ◽  
Quantitative Structure ◽  
Structure Property ◽  
Ann Model ◽  
Phenol Derivatives ◽  
Descriptor Selection ◽  
Quantitative Structure Property Relationships

Quantitative structure?solubility relationships (QSSR) are considered as a type of Quantitative structure?property relationship (QSPR) study in which aqueous solubility of chemicals are related to chemical structure. In the present work, multiple linear regression (MLR) and artificial neural network (ANN) techniques were used for QSSR studies of the water solubility of 68 phenols (phenol and its derivatives) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and a genetic algorithm (GA), the descriptors that resulted in the best fitted models were selected. After descriptor selection, multiple linear regression (MLR) was used to construct a linear QSSR model. The R2 = 91.0 %, LOO Q2 = 89.33 %, s = 0.340 values of the model developed by MLR showed a good predictive capability for log S values of phenol and its derivatives. The results of MLR model were compared with those of the ANN model. the comparison showed that the R2 = 94.99 %, s = 0.245 of ANN were higher and lower, respectively, which illustrated an ANN presents an excellent alternative to develop a QSSR model for the log S values of phenols to MLR.

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