Docking and QSAR studies of β-phenylethylidenehydrazine derivatives as a Gamma-aminobutyric acid aminotransferase inhibitor

Abstract

b-Phenylethylidenehydrazine (PEH) derivatives have been recognized as Gamma-aminobutyric acid aminotransferase (GABA-AT) inhibitors. In this research a group of newly synthesized of PEH analogs, possessing a variety of substituents (Me, OMe, Cl, and CF 3 ) at the 2-, 3-, and 4-position of the phenyl ring, were subjected to docking study and quantitative structure activity relationship (QSAR) analysis. PEH analogs were built by HYPERCHEM program, and conformational studies were performed through semi-empirical method followed by PM3 method. QSAR descriptors were obtained from the EDRAGON and HYPERCHEM, and equations were derived from multilinear regression (MLR) method. The sums of the JGI2, H6m, and E2s were identified as the most significant descriptors. This simple equation can be used to estimate the GABA-AT inhibitory activity for new derivatives of this series of compounds. Docking study was performed by using AutoDock4 program on the all compounds. The obtained results show that the phenyl ring is inserted into the lipophilic pocket and that the NHNH 2 moiety is situated in a mainly polar region of the enzyme. These computational studies can offer some useful references for understanding the action mechanism and performing the molecular design or modification of this series of GABA-AT inhibitors.

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

“…The DFT (density functional theory) method was also applied for complete geometric optimization of the structures. The 2D molecular structure of the selected calpeptin derivatives were evaluated using ChemDraw (Ultra 16.0) software, and then the 3D structures of the molecules were drawn by Chem3D (Pro16.0) software …”

A perspective on the growth of chemometrics in Iran: a glance into activities between 2005 and 2012

“…There are also different reports in literature on the QSAR/QSPR studies of different compounds by Iranian chemometricians [124][125][126][127][128][129][130][131]. Fatemi and Gharaghani proposed a novel QSAR model for the prediction of the apoptosis-inducing activity of 4-aryl-4H-chromenes based on support vector machine [132].…”

LM-ANN-based QSAR model for the prediction of pEC50 for a set of potent NNRTI using the mixture of ligand–receptor interaction information and drug-like indexes

“…The results from the established QSAR models help researchers to find a quantitative relationship between structures and biological activities that leads to the design of new compounds with remarkable biological activities with no need for any experimental studies (Muhammad et al 2018;Ojha Lokendra et al 2013). In recent years, the three-dimensional (3D) structures for a wide variety of receptors have become available and also the computational methods have greatly improved; thus, the use of descriptors containing information about the interactions of ligands with the active site of receptors has been highly suggested in the QSAR studies (Amini et al 2016;Chakraborty et al 2014;Chen and Chen 2012;Coi and Bianucci 2013;Davood and Iman 2011;Ebrahimi and Khayamian 2014;Ebrahimi et al 2012;Garg et al 2010;Gharaghani et al 2013;Rasouli and Davood 2018;Safarizadeh and Garkani-Nejad 2019;Sheikhpour et al 2017;Singla et al 2011;Zheng et al 2014). Among the computational chemistry methods, molecular docking is a powerful tool that provides the LR interaction information (Gharaghani et al 2013) through the different computational software such as Dock and AutoDock (Kramer et al 1999;Morris et al 1998).…”

“…The descriptors computed in this way are in fact the same as the structural descriptors; however, because they are calculated from the modified structure of the docked ligand, they have to some extent the interaction information but do not fully reflect the LR interactions. The other way is the extraction of MDDs as the interactions information from the best conformation of LR complex after successful docking of each ligand in the active site of the receptor (Amini et al 2016;Chakraborty et al 2014;Chen and Chen 2012;Coi and Bianucci 2013;Davood and Iman 2011;Ebrahimi and Khayamian 2014;Ebrahimi et al 2012;Garg et al 2010;Gharaghani et al 2013;Rasouli and Davood 2018;Safarizadeh and Garkani-Nejad 2019;Sheikhpour et al 2017;Singla et al 2011;Zheng et al 2014). The MDDs computed in this way are of the LR binding energy and enter the LR interaction information into the QSAR models, successfully.…”