Asian Journal of Research in Chemistry
  • Year: 2022
  • Volume: 15
  • Issue: 6

Predictive QSAR Models for the Toxicity of Phenols

Materials and Environment Analytical Sciences Laboratory, Larbi Ben M’hidi University - Oum El BouaghiB.P. 358 route de Constantine, 04000Oum el Bouaghi, Algeria.

*Corresponding Author E-mail: hakimannaba2178@yahoo.fr, hakim.hamada@univ-oeb.dz

Online Published on 20 March, 2023.

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.

Keywords

Getaway descriptors, QSAR, Hydrophobicity, External validation, Toxicity topological