Davood Malekzadeh
Department of Chemistry, Payame Noor University Tehran, Iran.
[ 1 ] - QSAR Study of 17β-HSD3 Inhibitors by Genetic Algorithm-Support Vector Machine as a Target Receptor for the Treatment of Prostate Cancer
The 17β-HSD3 enzyme plays a key role in treatment of prostate cancer and small inhibitorscan be used to efficiently target it. In the present study, the multiple linear regression (MLR),and support vector machine (SVM) methods were used to interpret the chemical structuralfunctionality against the inhibition activity of some 17β-HSD3inhibitors. Chemical structuralinformation were described thro...
[ 2 ] - QSAR studies and application of genetic algorithm - multiple linear regressions in prediction of novel p2x7 receptor antagonists’ activity
Quantitative structure-activity relationship (QSAR) models were employed for prediction the activity of P2X7 receptor antagonists. A data set consisted of 50 purine derivatives was utilized in the model construction where 40 and 10 of these compounds were in the training and test sets respectively. A suitable group of calculated molecular descriptors was selected by employing stepwise multiple ...
[ 3 ] - QSAR Study of 17β-HSD3 Inhibitors by Genetic Algorithm-Support Vector Machine as a Target Receptor for the Treatment of Prostate Cancer
The 17β-HSD3 enzyme plays a key role in treatment of prostate cancer and small inhibitorscan be used to efficiently target it. In the present study, the multiple linear regression (MLR),and support vector machine (SVM) methods were used to interpret the chemical structuralfunctionality against the inhibition activity of some 17β-HSD3inhibitors. Chemical structuralinformation were described thro...
[ 4 ] - A comparative QSAR study of aryl-substituted isobenzofuran-1(3H)-ones inhibitors
A comparative workflow, including linear and non-linear QSAR models, was carried out to evaluate the predictive accuracy of models and predict the inhibition activity of a series of aryl-substituted isobenzofuran-1(3H)-ones. The data set consisted of 34 compounds was classified into the training and test sets, randomly. Molecular descriptors were selected using the genetic algorithm (GA) as a f...
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