QSAR study of a series of peptidomimetic derivatives towards MERS-CoV inhibitors

In this study, we report the quantitative structure activity relationships (QSAR) investigation to determine the relationship between the anti-MERS-CoV activity and a set of chemical descriptors computed using ChemSketch, MarvinSketch and ChemOffice software. Herein, the principal components analysis (PCA), multiple linear regression (MLR) and multiple non-linear regression (MNLR) methods were used with the intention to obtain a reliable QSAR model with good predictive capacity. The original data set of 43 peptidomimetic compounds was randomly divided into training and test set of 35 and 8 compounds, respectively. The values obtained by MLR and MNLR for the determination coefficient are 0.777 and 0.813, respectively. The predictive ability of the MLR model was assessed by external validation using the eight compounds of the test set with predicted determination coefficients R2test of 0.655

Density Functional Theory Based Quantitative Structure-Activity Relationship Study of Cycloguanil Derivatives Acting as Plasmodium falciparum.

This work presents a study of quantitative structure-activity relationship (QSAR) on the cycloguanil derivatives which are reported as growth inhibitors of clone of Plasmodium falciparum (T9/94 RC17) which houses A16V+S108T mutant dihydrofolate reductase (DHFR) enzyme. A set of 24 molecule-derived cycloguanil was modeled using the Gauss View software (03) using DFT B3LYP 6,6-31G-31G (d) as a base function. The obtained descriptions are purely electronic. The set constitute the inhibitory activity and the calculated electronic descriptors were statistically processed with principal component analysis (PCA), multiple linear regression (MLR), multiple nonlinear regressions (MNLR) and artificial neural network (ANN). The results obtained by the artificial neural network (ANN) show that the expected activities are in good agreement with the experimental results, with equal correlation coefficient R = 0, 912.To determine the architecture of this network, we varied the number of hidden layers, the number of neurons in the hidden layers, the transfer functions and the pairs of transfer functions. The best results were obtained with a network architecture [3-3-1], activation functions (Tansig-Purelin) and a learning algorithm of Levenberg-Marquardt.

Molecular docking studies for the identifications of novel antimicrobial compounds targeting of staphylococcus aureus

This work  include several advanced molecular docking tools to study the interactions of our newly synthesized 1,3,4-thiadiazole  derivatives in the active site of penicillin binding protein and DNA gyrase against Staphylococcus aureus, the enzymes targeted for antimicrobial agents. Results such as MolDock scores, binding energies, residue binding distances, etc. were identified and discussed in this present research. The molecules with best docking results were selected in order to calculate drug likeness and bioavailability using Molinspiration software. All the compounds obey Lipinski’s rule and its extension and showed drug likeness. The pharmacokinetic parameters study was done using the AdmetSAR to display ADME and toxicity properties of these antimicrobial

3D-QSAR modeling, Molecular Docking and drug-like properties investigations of novel heterocyclic compounds derived from Magnolia Officinalis as Hit Compounds against NSCLC

In this work, we used the CoMSIA approach to develop a 3D-QSAR model for describing the quantitative structure-activity relationship of 51 novel compounds derived from Magnolia officinalis as potential agents against non-small cell lung cancer. The CoMSIA model developed with steric (S), electrostatic (E), hydrophobic (H), donor and acceptor hydrogen bonds (D and A) showed high efficiency in predicting pIC50 activity (R² = 0.81; Q² = 0.51; R2pred= 0.80, SEE=0.03). The predictions of the developed 3D-QSAR model were supported by a molecular docking simulation that was performed on the highest biologically active molecule in the series of molecules studied. In addition, novel molecules designed on the basis of the structural properties predicted by the CoMSIA model and molecular docking studies. In silico drug-like evaluation of novel designed molecules indicated the suitability of compounds T1, T2 and T3 for use as future drugs for the treatment of non-small cell lung cancer. Therefore, the three proposed molecular structures could be adopted as key in the development of new drugs that inhibit lung cancer cell lines by targeting the EGFR tyrosine kinase

Structure-toxicity relationships for phenols and anilines towards Chlorella vulgaris using quantum chemical descriptors and statistical methods.

Quantitative structure–toxicity relationship (QSTR) models are useful to understand how chemical structure relates to the toxicity of natural and synthetic chemicals. The chemical structures of 67 phenols and anilines have been characterized by electronic and physic-chemical descriptors. Density functional theory (DFT) with Beck’s three parameter hybrid functional using the LYP correlation functional (B3LYP/6-31G(d)) calculations have been carried out in order to get insights into the structure chemical and property information for the study compounds. The statistical quality of the MLR and MNLR models was found to be efficient for the predicting of the toxicity, but when compared to the obtained results by ANN model, we realized that the predictions achieved by this latter one were more effective. The results indicated that the developed models could produce satisfactory predictive results for the four different toxicity endpoints with high squared correlation coefficients (R2 ). Leave-one-out cross validation, external validation, Y-randomized validation and application domain analysis demonstrated the accuracy, robustness and reliability of these models. Accordingly.the obtained results suggested that the proposed descriptors could be useful to predict the toxicity of phenols and anilines towards Chlorella vulgaris.

Quantitative Structure–Activity Relationship (QSAR) Studies of Some Glutamine Analogues for Possible Anticancer Activity

A Quantitative Structure–Activity Relationship (QSAR) study was performed to predict an anticancer activity in tumor cells of thirty-six 5-N-substituted-2-(substituted benzenesulphonyl) glutamines compounds using the electronic and topologic descriptors computed respectively, with ACD/ChemSketch and Gaussian 03W programs. The structures of all 36 compounds were optimized using the hybrid Density Functional Theory (DFT) at the B3LYP/6-31G(d) level of theory. In both approaches, 30 compounds were assigned as the training set and the rest as the test set. These compounds were analyzed by the Principal Components Analysis (PCA) method, a descendant Multiple Linear Regression (MLR), Multiple Nonlinear Regression (MNLR) analyses and an Artificial Neural Network (ANN). The robustness of the obtained models was assessed by leave-many-out cross-validation, and external validation through a test set.This study shows that the ANN has served marginally better to predict antitumor activity when compared with the results given by predictions made with MLR and MNL

Electronic and photovoltaic properties of new materials based on imidazo[1,2-a]pyrazine. Computational investigations

A quantum chemical investigation has been performed to explore optical and electronic properties of a series of different compounds based π-conjugated molecular materials with fused rings, on imidazo[1,2-a]pyrazines. Different electron-donor side groups as side-chain substituents were introduced in molecular backbone to investigate their effects on the electronic structure. The HOMO and LUMO energy levels as well energy gap Eg of the studied compounds have been calculated and reported. The obtained data suggest that studied molecules are good candidates for organic solar cells