In this research, Quantitative Structure–Activity Relationship (QSAR) studies have been used to predict activities of organochlorine pesticides. Firstly, the chemical structure of molecules was drawn with the Gauss view 05 program and optimized at Hartree–Fo More
In this research, Quantitative Structure–Activity Relationship (QSAR) studies have been used to predict activities of organochlorine pesticides. Firstly, the chemical structure of molecules was drawn with the Gauss view 05 program and optimized at Hartree–Fock level of theory and 6-31G* basis sets using Gaussian 09 software. The physiochemical properties namely octanol-water partition coefficient (logP) and toxicity (log LD50) are taken from the scientific web book. The dragon software has been used for the calculation of molecular descriptors. The suitable descriptors were selected with the aid of the genetic algorithm (GA) and backward techniques. At the next step, the relationship between molecular descriptors and the activities was investigated by multiple linear regression (MLR) method. In order to build and test QSAR models, a data set of organochlorine pesticides was randomly separated into 2 groups: training (80%) and test (20%) sets.
The models were evaluated with regression parameters: correlation coefficient (R), squared regression coefficient (R2), adjusted correlation coefficient (R2 adj) and root mean squared error (RMSE).
For the predictive ability and verification of the models are discussed by using Leave-One-Out (LOO)
cross-validation and external test set. The external prediction accuracy of the obtained models was examined using the above regression parameters. Results of validations and high statistical quality of models indicate that generated GA-MLR models are reasonable QSAR models. These models help to delineate the important descriptors responsible for predicting their activities.
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Quinoline alkaloids and their derivatives have wide medical and agricultural applications. In this research, a quantitative structure- property relationship (QSPR) has been employed to predict the octanol-water partition coefficient (logP) of 76 quinoline alkaloid campt More
Quinoline alkaloids and their derivatives have wide medical and agricultural applications. In this research, a quantitative structure- property relationship (QSPR) has been employed to predict the octanol-water partition coefficient (logP) of 76 quinoline alkaloid camptothecin (CPT) derivatives as antitumor potencies using GA-MLR method and molecular descriptors.
The Gauss View 05 software was used for drawing chemical structure of the studied compounds. The geometry optimizations of the studied compounds were done by the Gaussian 09W software at B3YLP density functional theory (DFT) with 6-311G (d,p) basis set.
Molecular descriptors for each of optimized structures were calculated by Dragon software in different category.
In order to reduce and select the best descriptors, the Genetic Algorithm technique and stepwise multiple linear regression method was used.
The pearson coefficient correlation (PCC) and the variance inflation factor (VIF) statistics were used to test the multicollinearity of descriptors in the best model.
The different types of internal and external validations were used to evaluate predictive model performance. The best QSPR model is obtained with R2 value of 0.901, Q2LOO =0.919, and RMSE=0.706.
The results of statistical parameters and validations of the GA-MLR model generated were found to be satisfactory. The model revealed that octanol-water partition coefficient of CPT derivatives is influence by ATS8e (2D-autocorrelation) descriptor. This information could be used to design novel quinoline alkaloid camptothecin (CPT) derivatives as insecticide agents.
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