IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH
DOI:
https://doi.org/10.18540/jcecvl5iss1pp0049-0062Keywords:
Herbicide, QSAR, Multiple Linear Regression (MLR), Genetic Function Algoeithm (GFA), Applicability Domain, Y- Randomization.Abstract
The study of the quantitative structure-activity relationship (QSAR) was used in a set of data from 43 heterocyclic and phenylic inhibitor compounds in order to establish a correlation between the inhibitory concentrations of the compounds in question and their structures. The optimization method of the density functional theory (DFT) was used to minimize the energy of the 3D structures using the Becke functional hybrid Exchange (B3) parameter with the Lee, Yang, and Parr Functional Correlation (LYP), commonly called the B3LYP functional Hybrid and 6-31G* Basis Set (B3LYP/6-31G*) method, to discover their molecular Quantum descriptors. Five models of QSAR were generated with the technique of genetic function algorithm (GFA). Among the five models generated, model 1 was selected as the best model because of its statistical significance (Friedman's LOF = 0.3008, R2 = 0.9784, R2adj = 0.9739, Qcv2 = 0.9675 and R2pred = 0.7348). The meticulous model was evaluated by means of the Leave One out cross-validation (LOO-CV) approach, external validation of the compounds of the test set, Y -randomization test and applicability domain (Williams Plot). The proposed QSAR model was highly predictive and vigorous with good validation parameters. The molecular descriptors used in the model should be considered of great importance in improving the inhibitory concentrations of the herbicides and also in the conception of new herbicides with a higher concentration of inhibitor.
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