ISSN

0974-4150 (Online)
0974-4169 (Print)


Author(s): Auteur Hamada Hakim

Email(s): hakimannaba2178@yahoo.fr , hakim.hamada@univ-oeb.dz

DOI: 10.52711/0974-4150.2022.00076   

Address: Auteur Hamada Hakim*
Materials and Environment Analytical Sciences Laboratory, Larbi Ben M'hidi University - Oum El Bouaghi B.P. 358 route de Constantine, 04000 Oum el Bouaghi, Algeria.
*Corresponding Author

Published In:   Volume - 15,      Issue - 6,     Year - 2022


ABSTRACT:
Toxicity data for the 50% growth inhibitory concentration against Tetrahymena pyriformis pCIC50 = -logCIC50 for 85 phenols substituted were obtained experimentally. Log (CIC50)-1 along with the hydrophobicity, the logarithm of the 1-octanol/water partition coefficient (log Kow), and R2u (GETAWAY descriptors). The entire data set was randomly split into a training set (60chemicals) used to establish the QSAR model, and a test set (25 chemicals) for statistical external validation The descriptors models were selected from an extensive set of several descriptors (topological, geometrical and quantum). Quantitative structure-activity/property (QSAR / The values of the statistical parameters obtained from the multiple linear regression analysis (R²=95.5%, Q²=95.01%, S=0.157, F=604.34, P=0, SDEC=0.153, SDEP=0.161, Q²ext=95.96%, SDEPext=0.153) testify to the good fit of the model.


Cite this article:
Auteur Hamada Hakim. Predictive QSAR models for the toxicity of Phenols. Asian Journal of Research in Chemistry. 2022; 15(6):433-8. doi: 10.52711/0974-4150.2022.00076

Cite(Electronic):
Auteur Hamada Hakim. Predictive QSAR models for the toxicity of Phenols. Asian Journal of Research in Chemistry. 2022; 15(6):433-8. doi: 10.52711/0974-4150.2022.00076   Available on: https://ajrconline.org/AbstractView.aspx?PID=2022-15-6-8


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