Quantitative structure activity relationship (QSAR) study of octanol-water partition coefficients of some of environmental toxic of petroleum substances

Abstract

Life and its extraction fuels climate change. We performed studies upon an extended series of petroleum hydrocarbons, with octanol-water partition coefficients (log Kow), by using the quantitative structure-activity relationship (QSAR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated and the genetic algorithm (GA) was employed to select those descriptors, resulting in the best-fit models. The partial least squares PLS (PLS) was utilized to construct the linear QSAR model. The best GA-PLS model contains 27 selected descriptors in 10 latent variables space. The R2 and RMSE for training and test sets were (0.827, 0.088) and (0.716, 0.185), respectively. Inspection of the results reveals a higher R2 and lowers the RMSE value parameter for the data set GA-PLS. The GA-PLS linear model has good statistical quality with low prediction error. This is the first research on the QSAR which uses GA-PLS for the presiction octanol-water partition coefficients of some of the environmental toxic of the petroleum substances. KEYWORDS Petroleum substances Genetic algorithm QSAR Graphical AbstractQuantitative structure activity relationship (QSAR) study… 117

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“…Range-scaling transformation (Xa) of 55 nanoparticle compounds are presented in Table 1 [24]. Sample components are separated, identified, and measured by the 4chlorobenzenethiol MPNs stationary phase include complementary separations such as two-dimensional GC and potential utilization within a model system for a micro-fabricated GC (GC).…”

Lactoperoxidase inhibition by tautomeric propylthiouracils

“…Subsequently, in silico molecular docking simulations have been performed employing the genetic algorithm (GA) as implemented in the AutoDock package [22][23][24][25]. The 3D structure of LPO (lactoperoxidase) enzyme, as a representative of peroxidase enzymes in hyperthyroidism, has been obtained from PDB (ID: 5HPW) and its chain A has been prepared for the docking process.…”

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“…(1) In this, is the change in the weight factor for each network node, α is the momentum factor, and F is a weight update function, which indicates how weights are changed during the learning process. The weights of hidden layer were optimized using the Levenberg-Marquardt algorithm, a second derivative optimization method [10].…”

“…The result clearly displayed a significant improvement of the QSRR model consequent to non-linear statistical treatment and a substantial independence of model prediction from the structure of the test molecule. In the above analysis, the descriptive power of a given model has been measured by its ability to predict RT of unknown drug molecules [4][5][6][7].…”

“…Quantitative structure-retention relationships (QSRR) represent statistical models that quantify the relation between the structure of molecules and their chromatographic retention time, allowing the prediction of the RT of novel compounds [5]. QSRR on the RT have been reported for different types of compounds [6,7]. The aim of the present study is estimation of ability optimal descriptors calculated with linear regression (the partial least squares (PLS)) and non-linear regressions (the kernel partial Asian Journal of Nanoscience and Materials least squares (KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN)) in QSRR analysis of retention time (RT) of pesticides and active pharmaceutical compounds in water samples of different types (surface and wastewaters).…”