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Modeling of Log Kow of a Series of PAHs using Computational Chemistry


Affiliations
1 1Department of Material Sciences, Faculty of Science and Technology, Amine Elokkal Elhadj Moussa Eg Akhamouk University – Tamanrasset 11000, Algeria., India
2 Process Engineering Department, Faculty of Technology, Hamma Lakhdar University-El oued 3900, Algeria., India
3 Process Engineering Department, Faculty of Technology, Hamma Lakhdar University-El oued 3900, Algeria ., India
     

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The importance of Chemometrics Methods in Modeling (in QSAR analysis) of the mathematical model’s study of large datasets of molecules with huge numbers of physicochemical and structural parameters quantitative structure-Toxicity relationship (QSTR) are mainly based on multiple regression analysis in QSAR analysis The study of Least Square in deriving QSTR models for datasets of Quantitative Structure-Toxicity relationship on Log k ow (Octanol-water partition coefficient) for 16 Hydrocarbons compounds has been using the software Hyperchem 6.3 for computing descriptors and MINITAB 16 for data modeling. A three -descriptors model [two electronics molecules’ descriptors (QSER descriptor), HOMO (is Highest occupied molecular orbital) and LUMO (Lowest unoccupied molecular orbital), one QSAR descriptor 𝐸𝐻 (Hydration Energy) by Least Squares with correlation coefficient r=0.868, S=0.635, R 2 = 75.4%, R 2 ajd=73.7% and Durbin-Watson statistic =1.85277 and graphical analysis by diagram of goodness of fit and line plot. The results statistical of new model after removing the aberrant compounds (Toxicity compounds) shows high Coefficient of correlation r=0.9581, S=0.4316, determination coefficient R 2 =91. 8%, ajustemed R 2 ajd = 89.3%, Durbin-Watson statistic D=2.373, Three explanatory Variable model selected is robust and has good fitness. Two influential compounds detected and important the model and absence aberant compounds of the studied sample.

Keywords

Hydrocarbons (PAHs), toxicity 𝐿𝑜𝑔 𝑘𝑜𝑤,HOMO, LUMO,𝐸𝐻,Multiple linearregression, Aberant compounds.
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  • Modeling of Log Kow of a Series of PAHs using Computational Chemistry

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Authors

Fatiha Mebarki
1Department of Material Sciences, Faculty of Science and Technology, Amine Elokkal Elhadj Moussa Eg Akhamouk University – Tamanrasset 11000, Algeria., India
Souhaila Meneceur
Process Engineering Department, Faculty of Technology, Hamma Lakhdar University-El oued 3900, Algeria., India
Abderrhmane Bouafia
Process Engineering Department, Faculty of Technology, Hamma Lakhdar University-El oued 3900, Algeria ., India

Abstract


The importance of Chemometrics Methods in Modeling (in QSAR analysis) of the mathematical model’s study of large datasets of molecules with huge numbers of physicochemical and structural parameters quantitative structure-Toxicity relationship (QSTR) are mainly based on multiple regression analysis in QSAR analysis The study of Least Square in deriving QSTR models for datasets of Quantitative Structure-Toxicity relationship on Log k ow (Octanol-water partition coefficient) for 16 Hydrocarbons compounds has been using the software Hyperchem 6.3 for computing descriptors and MINITAB 16 for data modeling. A three -descriptors model [two electronics molecules’ descriptors (QSER descriptor), HOMO (is Highest occupied molecular orbital) and LUMO (Lowest unoccupied molecular orbital), one QSAR descriptor 𝐸𝐻 (Hydration Energy) by Least Squares with correlation coefficient r=0.868, S=0.635, R 2 = 75.4%, R 2 ajd=73.7% and Durbin-Watson statistic =1.85277 and graphical analysis by diagram of goodness of fit and line plot. The results statistical of new model after removing the aberrant compounds (Toxicity compounds) shows high Coefficient of correlation r=0.9581, S=0.4316, determination coefficient R 2 =91. 8%, ajustemed R 2 ajd = 89.3%, Durbin-Watson statistic D=2.373, Three explanatory Variable model selected is robust and has good fitness. Two influential compounds detected and important the model and absence aberant compounds of the studied sample.

Keywords


Hydrocarbons (PAHs), toxicity 𝐿𝑜𝑔 𝑘𝑜𝑤,HOMO, LUMO,𝐸𝐻,Multiple linearregression, Aberant compounds.

References