Yahya Guzel

Study of the binding affinity for corticosteroid-binding globulin (CBG) using the electron topological method (ETM) as three-dimensional quantitative structure–activity relationship (3D QSAR)

The Electron Topological Method, called ETM, is a descriptor for predicting the biological activities of molecules based on three-dimensional quantitative structure-activity relations (3D QSAR). ETM uses a modified electron topological state index to substitute for electronic properties and a topological distance for the relative distance in the molecule. It is shown that the molecular fragments responsible for this activity possess fixed electronic and geometric characteristics associated with a distinct arrangement and the steric accessibility of an oxygen atom and a group of carbon atoms. After that, it is essential to employ a linear regression analysis technique to derive a 3D QSAR model relating the biological activities to the ETM. The ETM is used to study the 3D QSAR of the corticosteroid-binding globulin (CBG) binding affinity to 31 steroids, and resulting models have a comparable to current 3D methods such as CoMFA. Though the ETM is a descriptor based on 3D topological information obtained by quantum chemical derived descriptors, give the best answer for both the similarity analysis and the statistical fitting.

Amino substituted nitrogen heterocycle ureas as kinase insert domain containing receptor (KDR) inhibitors: Performance of structure–activity relationship approaches

A quantitative structure-activity relationship (QSAR) study was performed on a set of amino-substituted nitrogen heterocyclic urea derivatives. Two novel approaches were applied: (1) the simplified molecular input-line entry systems (SMILES) based optimal descriptors approach; and (2) the fragment-based simplex representation of molecular structure (SiRMS) approach. Comparison with the classic scheme of building up the model and balance of correlation (BC) for optimal descriptors approach shows that the BC scheme provides more robust predictions than the classic scheme for the considered pIC50 of the heterocyclic urea derivatives. Comparison of the SMILES-based optimal descriptors and SiRMS approaches has confirmed good performance of both techniques in prediction of kinase insert domain containing receptor (KDR) inhibitory activity, expressed as a logarithm of inhibitory concentration (pIC50) of studied compounds.

Investigation of Antiulcer Activities of Imidazo[1,2-6alpha;]pyridinyl-2-alkylaminobenzoxazoles and 5,6,7,8-Tetahydroimidazo[1,2-alpha;]pyridinylbenzoxazoles with Electron-Topological (ET) Method

The study presents structure‐activity considerations of a series of imidazo[1,2‐α]pyridiny‐2‐alkylaminobenzoxazoles (I) and 5,6,7,8‐tetahydroimidazo[1,2‐α]pyridinylbenzoxazoles (II) investigated for anti‐stress ulcer activity with the electron‐topological method. A series of 39 compounds including 24 active and 15 weakly active was studied. It is shown that the fragment determined by the electron‐topological method in an active molecule is responsible for anti‐stress ulcer activity. Quantitative structure‐activity relationships with electron topological approach of these compounds are discussed in terms of the statistical program STATGRAF‐7.0.

4D-QSAR Studies Using a New Descriptor of the Klopman Index: Antibacterial Activities of Sulfone Derivatives Containing 1, 3, 4-Oxadiazole Moiety Based on MCET Model

INTRODUCTION In this paper, we have introduced a new atomic descriptor with Klopman index to determine the local reactive sites of the molecular systems during electrophilic, and nucleophilic attacks. This index, similar to other local reactivity descriptors but more advanced, has been used as a realistic descriptor to discover new aspects of molecular structure. METHODS Nonlinear Least Squares (NLLS) methods to define the parameters maximizing the fit between the observed points and the computed simulation results were performed according to the Levenberg- Marquardt (LM) algorithm. We have attempted to demonstrate the structural properties of compounds that contribute not only basic pharmacophore (b-Pha) but also positive (Auxiliary Group- AG) or negative (Anti-Pharmacophore Shielding-APS) due to the new local atomic reactivity. RESULTS AND CONCLUSION In the 4D-QSAR study, nonparametric regression analysis was used to determine the adjustable constants. Using the leave One Out Cross-Validation (LOO-CV), antibacterial activities (pEC50-μM) were predicted as r2 loo-cv (q2) = 0.979, r2 pred (r2) = 0.911, respectively, for 27 training sets and 9 test set compounds. Also, the rm2 (overall) value, which indicates the closeness between the predicted and corresponding observed data, was calculated to be 0.957. The model obtained by the Molecular Conformer Electron Topological (MCET) method was compared with the q2 loo-cv and R2 non-cv values determined by the CoMFA and CoMSIA methods and more satisfactory results were obtained.

Molecular docking and 4D-QSAR studies of metastatic cancer inhibitor thiazoles

By using the molecular docking and 4D-QSAR analysis, it is aimed to find the interaction points in the receptor binding site of transforming growth factor-beta (TGF-beta) used to inhibit invasion and metastasis. To elucidate the interaction points of receptor, different types of local reactive descriptor (LRD) of ligands have been used. Activity values related to interaction energy between the ligand-receptor (L-R) were determined by nonlinear least squares (NLLS) using the Levenberg-Marquardt (LM) algorithm. Using the Molecule Comparative Electron Topology (MCET) method, the 3D pharmacophore model (3D-PhaM) was obtained after alignment and superimposition of the molecules, and also confirmed by molecular docking method. With the leave one out-cross validation (LOO-CV) method, the best predictions are q2 or rCV2 = 0.789 for the 51 compounds in the internal training set and r2 = 0.785 for the 13 compounds in the external test set. Furthermore, the predictive capability of the advanced QSAR model is more precisely calculated with the rm2 metric (rm2 = 0.769).