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.

The photophysical properties and electronic structures of ((2E, 2’E)-1, 1’-[chalcogen bis (4, 1-phenylene)] bis [3-(4-chlorophenyl) prop-2-en-1-one] derivatives as hole-transporting materials for organic light-emitting diodes (OLEDs). Quantum chemical investigations

In order to propose new organic materials for organic light-emitting diodes (OLEDs) applications, The quantum chemical calculations have been performed on four molecules M0 ((2E, 2’E)-1, 1’ (selenobis (4, 1phenylene)) bis (bis (3-(4-chlorophenyl) prop-2en-1-one)), M1 ((2E, 2’E)-1, 1’ (thiobis (4, 1phenylene)) bis (bis (3-(4-chlorophenyl) prop-2en-1-one)), M2 ((2E, 2’E)-1, 1’ (oxybis (4, 1phenylene)) bis (bis (3-(4-chlorophenyl) prop-2en-1-one)), M3 ((2E, 2’E)-1, 1’ (azanediylbis (4, 1phenylene)) bis (bis (3-(4-chlorophenyl) prop-2en-1-one)).The principal objective of this work is to study the effect of Chalcogen (O, S, and Se) and nitrogen (N) on geometrical, electronic, optical, and charge transfer properties of these compounds by setting their ionization potentials (IP), their electron affinities (EA), their chemical reactivity indices, their reorganization energies, their electrostatic potential as well as the nonlinear optical (NLO) properties. The geometry of these studied compounds was obtained after optimization in their fundamental states by using the functional density theory (DFT) with the B3LYP method and the basis set 6-311G (d, p).  The studied parameters determined from the most stable conformation of each studied molecule. The time-dependent density theory method TD-DFT-B3LYP 6-311G (d, p) was used for the study of absorption. The results of the theoretical calculations show that the mentioned parameters above are affected by the change of atoms O, S, Se, and NH. The smaller hole and electron reorganization energies of these molecules suggest possible use in OLEDs.

Interactions between (4Z)-hex-4-en-1-ol and 2-methylbutyl 2-methylbutanoate with olfactory receptors using computational methods

The first step in the perception of an odor is the activation of one or more olfactory receptors (ORs) following binding of the odorant molecule to the OR. The compounds (4Z)-hex-4-en-1-ol and 2-methylbutyl 2-methylbutanoate are two important odorants molecules known as food flavor. In this research, we investigate the potential targets for this two molecules and try to interpret the type of binding with different ORs models and their relationship with the retention/release property. We used the SWISS-MODEL modelling server to predict the three-dimensional (3D) structure of the ORs. We then used the Autodock vina and Autodock tools to predict the binding site and binding energy for the ligands to these receptors. The results indicate that the molecule (4Z)-hex-4-en-1-ol has given more hydrogen bonds with the majority of these receptors and the 2-methylbutyl 2-methylbutanoate molecule mainly has given Pi bonds interaction type

Reverse Docking on Five Original PPO Structures: Plant, Bacterial, and Human

Protoporphyrinogen oxidase has known remarkable interest in biochemical studies, it is considered a perfect target for the development of new herbicides. PPO herbicides have been developed for more than forty years, and research on this enzyme remains until today, to find new more effective herbicides. In this work, we investigated the inhibitory activity of a compound derived from N-phenylphthalimide with the highest inhibitory activity among a series of 29 molecules, on five PPO structures from various origins, including Plant origin, bacterial, and human, we have based on Reverse Docking, to know the affinity between the inhibitor and the five targets, and the different ligand-receptor interactions. As well as Molecular Dynamics. Interesting results have been obtained, which may help us to discover new targets concerning herbicides

In silico design of new pyrimidine-2,4-dione derivatives as promising inhibitors for HIV Reverse Transcriptase-associated RNase H using 2D-QSAR modeling and (ADME/Tox) properties

The main target of present QSAR modeling is to pave the way for the development of new pyrimidine-2,4-dione derivatives and predict their HIV reverse transcriptase-associated RNase H inhibitory activity. To accelerate this process, linear and non-linear models of thirty-nine pyrimidine-2,4-dione derivatives have been constructed by exploiting PCA, MLR, and MNLR statistical techniques available in the XLSTAT software, as well as the (DFT/ Beck3LYP/6-31G (d,p)) approach. Among the 16 quantum and physicochemical descriptors measured, only four optimal molecular descriptors have been employed to perform QSAR models, i.e., density, number of H-bond acceptors, octanol/water partition coefficient, and LUMO energy. The Loo/cross-validation procedure, the Y-scrambling test, Golbraikh-Tropsha’s criteria and the applicability area have all been utilized to evaluate the linear model's performance accuracy. Likewise, the nonlinear model's predictive power has been measured internally through the Loo/cross-validation procedure with coefficient R_(CV(LOO))^2  and externally through test set compounds with external prediction coefficient R_pred^2. Herein, both MLR and MNLR models which exhibited excellent performance and met OECD criteria were exploited to predict inhibitory activities. By analyzing the structural characteristics of the studied compounds encoded in the afore-mentioned descriptors along with their effects on pIC50 inhibitory activity, we have been able to design eleven new chemical inhibitors. All of these inhibitors with new substituents displayed significantly higher HIV RT-associated RNase H inhibitory activities than the existing ones, as well as satisfactory results in silico ADME/Toxicity assessments

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

Combined 3D-QSAR Modeling and Molecular Docking Study on metronidazole-triazole-styryl hybrids as antiamoebic activity

A series of twenty-two metronidazole-triazole-styryl hybrids as antiamoebic agents were studied based on the combination of 3D-QSAR and surflex-docking. The CoMFA and CoMSIA models were carried out using eighteen compounds in the training set and four compounds in the test set gives Q2 values of 0.684 and 0.664 respectively, and R2 values of 0.882 and 0.894 respectively. The adapted alignment method with the suitable parameters resulted in reliable models. Based on contour maps produced by the CoMFA and CoMSIA, we suggested new compounds with high predicted activities, Surflex-docking revealed the important interactions between the ligand and receptor. Therefore, it confirmed the stability of predicted molecules in the receptor with PDB: 4CCQ

2D-QSAD study of the anticancer activity of naphthoquinone derivatives against cancer cell line T47D (breast ductal carcinoma)

In the current work, a series of naphthoquinone derivatives against cancer cell line T47D (breast ductal carcinoma) have been studied by 2D-QSAR. The analysis performed in this work is done through principal component analysis (PCA) and multiple linear regression (RLM). The data of 25 compounds are randomly divided into two groups. One group is a learning set composed of 20 compounds, and a test set composed of 5 compounds. The original series contains 28 molecules, we eliminated 3 because the activity of those 3 molecules isn’t defined and that we only work with 25 molecules. The best model established by multiple linear regression (R2=0.98, Q2cv=0.99, R2test=0.81) showed very satisfactory results. The proposed QSAR model was verified by internal and external tests, such as Y randomization test and Golbraikh-Tropsha standar

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.

In silico design of new α-glucosidase inhibitors through 3D-QSAR study, molecular docking modeling and ADMET analysis

α-Glucosidase enzyme is a therapeutic target for diabetes mellitus and its inhibitors shown a crucial importance in the treatment of this disease. Twenty oxindole based oxadiazole molecules were studied based on the combination between 3D-QSAR and molecular docking approaches in order to develop new α-glucosidase inhibitors with high predicted activities. The proposed CoMFA and CoMSIA models exhibited important Q2 values (0.544 and 0.605 respectively) and significant R2 values (0.977 and 0.935 respectively). The CoMFA and CoMSIA models were undergone to an external validation to test their proficiency; the produced R2test values are 0.950 and 0.804, respectively. Moreover, the contour maps produced by CoMFA and CoMSIA models have been exploited to determine the main groups influencing (decreasing or increasing) the α-glucosidase inhibitory activity. Therefore, two new oxindole based oxadiazole molecules with significant activities were proposed and designed. In a similar vein, molecular docking simulation was conducted to scrutinize the binding interactions between oxindole based oxadiazole molecules and α-glucosidase receptor (PDB code: 3A4A). Finally yet importantly, ADMET properties were predicted to assess the oral bioavailability of the proposed new compounds and examine their toxicity