Author(s):
Nadia Ziani, Khadidja Amirat, Souhaila Meneceur, Fatiha Mebarki, Abderrhmane Bouafia
Email(s):
abdelrahmanebouafia@gmail.com
DOI:
10.52711/0974-4150.2023.00001 
Address:
Nadia Ziani1,5, Khadidja Amirat2,5, Souhaila Meneceur3,5, Fatiha Mebarki4,5, Abderrhmane Bouafia3*
1Faculty of Science, Chemistry Department Badji Mokhtar University Annaba, Annaba, Algeria.
2Faculty of Science, Department of Chemistry University of Sétif 1 - Ferhat Abbas, El Bez, Setif 19000.
3Department of Process Engineering and Petrochemistry, Faculty of Technology, University of El Oued, 39000 El-Oued, Algeria.
4Faculty of Science and Technology, Department of material sciences, Amine Elokkal El hadjMoussa Eg Akhamouk University - Tamanrasset, Algeria.
5Renewable Energy Development Unit in Arid Zones (UDERZA), University of El Oued El-Oued, Algeria.
*Corresponding Author
Published In:
Volume - 16,
Issue - 1,
Year - 2023
ABSTRACT:
EU Directive for the Protection of Laboratory Animals mandates and encourages the use of alternative methods that could substitute, cut down on, and generally improve animal testing. Quantitative structure-activity relationship models (QSAR) as well as in vitro toxicity testing are among the most notable of such. QSARs are defined as computerized mathematical models that can utilize a compound’s (aromatic amine) biological activity—aquatic toxicity—to calculate or provide the experimental descriptors of the chemical structure of this compound. Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) are the approaches we use for the aim of predicting aquatic toxicity. The best models for two descriptors are the electrotopological descriptors derived from E-calc, and the partition coefficient derived by the Hyperchem software, applying a genetic algorithm—variable subset selection procedure. The important values of the statistical parameters obtained by the two approaches were as follows: By MLR: R2= 92.18, Q2 = 90.51, Q2ext= 95.26, F=188.5466, S = 0.1995. By ANN were: Q2 = 94.79, RMSE= 0.16, Q2ext= 91.71, RMSEext=0.18.
Cite this article:
Nadia Ziani, Khadidja Amirat, Souhaila Meneceur, Fatiha Mebarki, Abderrhmane Bouafia. Silico Methodologies Modelling of Aquatic Toxicity in Tetrahymena Pyriformis Via Aromatic Amines. Asian Journal of Research in Chemistry. 2023; 16(1):1-7. doi: 10.52711/0974-4150.2023.00001
Cite(Electronic):
Nadia Ziani, Khadidja Amirat, Souhaila Meneceur, Fatiha Mebarki, Abderrhmane Bouafia. Silico Methodologies Modelling of Aquatic Toxicity in Tetrahymena Pyriformis Via Aromatic Amines. Asian Journal of Research in Chemistry. 2023; 16(1):1-7. doi: 10.52711/0974-4150.2023.00001 Available on: https://www.ajrconline.org/AbstractView.aspx?PID=2023-16-1-1
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