Author(s):
Khadidja Amirat, Nadia Ziani, Souhaila Meneceur, Fatiha Mebarki, Abderrhmane Bouafia
Email(s):
abdelrahmanebouafia@gmail.com
DOI:
10.52711/0974-4150.2023.00011 
Address:
Khadidja Amirat1,2,3, Nadia Ziani1,4, Souhaila Meneceur1,6, Fatiha Mebarki1,5, Abderrhmane Bouafia6*
1Renewable Energy Development Unit in Arid Zones (UDERZA), Hamma Lakhdar University of El-Oued,
El-Oued, 39000, Algeria.
2Department of Chemistry, University of Sétif 1, Ferhat Abbas, El Bez, Setif, 19000, Algeria.
3Water Treatment and Industrial Waste Recovery Laboratory, Faculty of Sciences,
Department of Chemistry, Badji Mokhtar Annaba University, BP 12, 23000, Algeria Faculty of Sciences,
4Department of Chemistry, Faculty of Sciences, Badji Mokhtar Annaba University, BP 12, 23000, Algeria.
5Material Sciences Department, University of Tamanrasset, Algeria.
6Department of Process Engineering and Petrochemistry, Faculty of Technology, University of El Oued,
39000 El-Oued, Algeria.
*Corresponding Author
Published In:
Volume - 16,
Issue - 1,
Year - 2023
ABSTRACT:
A structure / lethal dose 50 (pCIC50) relationship was researched for a set of phenols while favoring a hybrid genetic algorithm (GA) / multiple linear regression (MLR) approaches to the structural parameters being computed with (E-calc) which calcula the Kier–Hall Electrotopological state indices (E- state) and Hyperchem software. Among the more than 100 simple models with two explanatory variables acquired, we chose the model with the best values of the prediction parameter (Q2) and the coefficient of determination (R2). The reliability of the proposed model has also been illustrated using various techniques of evaluation: leave-many out, cross-validation, randomization test, and validation by the test set.
pCIC50 = - 0.0835 ± (0.07006) +0.112 ± (0.007408 (logkow)2 - 0.116 ± (0.01797) s-CH3
ntot = 81 ; S= 0.3296 log unit ; Q2(%) = 74.26 ; R2 (%)= 79.24 ; F= 118.3193; P=0,000.
Cite this article:
Khadidja Amirat, Nadia Ziani, Souhaila Meneceur, Fatiha Mebarki, Abderrhmane Bouafia. Modeling of Aquatic Toxicity of a Set of Phenols in Silico. Asian Journal of Research in Chemistry. 2023; 16(1):65-70. doi: 10.52711/0974-4150.2023.00011
Cite(Electronic):
Khadidja Amirat, Nadia Ziani, Souhaila Meneceur, Fatiha Mebarki, Abderrhmane Bouafia. Modeling of Aquatic Toxicity of a Set of Phenols in Silico. Asian Journal of Research in Chemistry. 2023; 16(1):65-70. doi: 10.52711/0974-4150.2023.00011 Available on: https://www.ajrconline.org/AbstractView.aspx?PID=2023-16-1-11
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