Reza Aalizadeh

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A linear quantitative structure–activity relationship (QSAR) model is presented for modeling and predicting of the IKKβ inhibitory activities. A data set containing 62 IKKβ inhibitors with known inhibitory activities was used. The whole data set was divided into a training set and a test set on the basis of K-means clustering technique. Multiple linear(More)
In this study, the quantitative structure–activity relationship (QSAR) model for some pyrazole/imidazole amide derivatives as mGlu5 inhibitors was developed. The data set was split into the training and test subsets, randomly. The most relevant variables were selected using the genetic algorithm (GA) variable selection method. Multiple linear regression(More)
In this work, a quantitative structure–activity relationship study was developed to predict the NaV1.7 antagonist activities. A data set consisted of 36 compounds with known NaV1.7 antagonist activities was split into two subsets of training set and test set using hierarchical clustering technique. To select the most respective descriptors among the pool of(More)
  • Hamid Rafiei, Marziyeh Khanzadeh, Shahla Mozaffari, Mohammad Hassan Bostanifar, Zhila Mohajeri Avval, Reza Aalizadeh +1 other
  • 2016
Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors . A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the(More)
In the present work, a molecular modeling study was carried out using 2D and 3D quantitative structure-activity relationships for the various series of compounds known as B-Raf $$^{\mathrm{V600E}}$$ V 600 E inhibitors. For 2D-QSAR analysis, a linear model was developed by MLR based on GA-OLS with appropriate results $$(Q^{2}_{\mathrm{LOO}}= 0.796,(More)
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