Quantitative structure–property relationship studies for direct photolysis rate constants and quantum yields of polybrominated diphenyl ethers in hexane and methanol

2009 ◽  
Vol 72 (5) ◽  
pp. 1587-1593 ◽  
Author(s):  
Lei Fang ◽  
Jun Huang ◽  
Gang Yu ◽  
Xue Li
Keyword(s):  
Rate Constants ◽  
Polybrominated Diphenyl Ethers ◽  
Quantum Yields ◽  
Quantitative Structure ◽  
Structure Property ◽  
Direct Photolysis ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Diphenyl Ethers

A Quantitative Structure-Property Relationship Study on Photodegradation of Polybrominated Diphenyl Ethers

2012 ◽  
Vol 546-547 ◽  
pp. 48-53 ◽  
Author(s):  
Li Ya Fu ◽  
Jin Luo ◽  
Ji Wei Hu
Keyword(s):  
Polybrominated Diphenyl Ethers ◽  
Quantitative Structure ◽  
Structure Property ◽  
Molecular Parameters ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Photodegradation Rate ◽  
Mechanical Methods ◽  
Structure Property Relationship ◽  
Diphenyl Ethers

Quantitative structure-property relationship (QSPR) models were developed in the present work for photodegradation rate constants (kp) of fifteen individual polybrominated diphenyl ethers (PBDEs) in methanol/water (8:2) by UV light in the sunlight region. The molecular descriptors used in the QSPR models were calculated by the two semi-empirical quantum mechanical methods, RM1 and PM6, respectively. Both multiple linear regression (MLR) and artificialneural network (ANN) were applied in this study. The statistic qualities of the MLR models based on the molecular parameters obtained by RM1 and PM6 calculations were both good with the R values of 0.987 and 0.990, respectively. The QSPR model built by the ANN method with the molecular parameters calculated with PM6 is slightly better than that with RM1.

A Quantitative Structure-Property Relationship Study on Reaction Rate Constants for Reductive Debromination of Polybrominated Diphenyl Ethers by Zero-Valent Iron

2012 ◽  
Vol 550-553 ◽  
pp. 2668-2675 ◽  
Author(s):  
Ji Wei Hu ◽  
Yuan Zhuang ◽  
Jin Luo ◽  
Xiong Hui Wei
Keyword(s):  
Rate Constants ◽  
Polybrominated Diphenyl Ethers ◽  
Reaction Rate ◽  
Zero Valent Iron ◽  
Quantitative Structure ◽  
Structure Property ◽  
Ann Model ◽  
Reaction Rate Constants ◽  
Structure Property Relationship ◽  
Diphenyl Ethers

A quantitative structure property relationship (QSPR) study was performed in this work to develop models for predicting reaction rate constants for reductive debromination of polybrominated diphenyl ethers (PBDEs) by zero-valent iron (ZVI). Both multiple linear regression (MLR) and artificial neural network (ANN) methods were employed for QSPR studies based on the experimental kinetic data of the fourteen PBDE congeners. Both the developed MLR and ANN models could give satisfactory prediction abilities, and the performance of the ANN model seems slightly better than that of the MLR model. In addition, energy of lowest unoccupied molecular orbital (ELUMO) and total energy (TE) were found to be the two relatively important variables in the ANN model via the assessment using both the Garson’s algorithm and connection weight approach.

Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives

2018 ◽  
Vol 21 (7) ◽  
pp. 533-542 ◽  
Author(s):  
Neda Ahmadinejad ◽  
Fatemeh Shafiei ◽  
Tahereh Momeni Isfahani
Keyword(s):  
Thermodynamic Properties ◽  
Predictive Ability ◽  
Total Sample ◽  
Basis Sets ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Chemical Structures ◽  
Property Relationship ◽  
Structure Property Relationship

Aim and Objective: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors. Materials and Methods: Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree–Fock level of theory and 3-21G basis sets by Gaussian 09. Results: The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets. Conclusion: The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.

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