Tahar Lakhlifi

Quantitative structure–activity relationship studies of dibenzo[a,d]cycloalkenimine derivatives for non-competitive antagonists of N-methyl-d-aspartate based on density functional theory with electronic and topological descriptors

Abstract To establish a quantitative structure–activity relationship for non-competitive antagonists of the N-methyl-d-aspartate receptor, 48 substituted dibenzo[a,d]cycloalkenimine derivatives were analyzed by principal components, a descendant multiple regression analyses, multiple non-linear regression and an artificial neural network. We propose non-linear and linear quantitative structure–activity models and interpret the activity of the compounds by the multivariate statistical analysis. Density functional theory with Beckes three-parameter hybrid function and Lee–Yang–Parr exchange correlation functional calculations were performed to define the structure, chemical reactivity and properties of the study compounds. The topological and the electronic descriptors were computed with ACD/ChemSketch and Gaussian 03W programs, respectively. The study shows that multiple regression and multiple non-linear regression analyses predict activity; however, predictions made with a 6-2-1 artificial neural network model were more accurate. This model gave statistically significant results and showed good stability to data variation in leave-one-out cross-validation.

QSAR analysis of the toxicity of phenols and thiophenols using MLR and ANN

Abstract This study gives a quantitative structure–activity relationship (QSAR) analysis of toxicity of phenols and thiophenols to Photobacterium phosphoreum, which is an important indicator for water quality. The chemical structures of 51 phenols and thiophenols have been characterized by electronic and physic-chemical descriptors. The present study was performed using principal components analysis (PCA), multiple regression analysis (MLR) and artificial neural network (ANN). The quantitative model was accordingly proposed and the toxicity of the compounds was interpreted based on the multivariate statistical analysis.

QSPR studies of 9-aniliioacridine derivatives for their DNA drug binding properties based on density functional theory using statistical methods: Model, validation and influencing factors

Abstract As a continuation of our research on the development and optimization of the biological activities/proprieties of acridine derivatives, a series of 31 molecules based on 9-aniliioacridines (25 training set and 6 test set) were subjected to 3D quantitative structure propriety relationship QSPR analyses for their drug-DNA binding proprieties using multiple linear regression (MLR) and multiple non-linear regression (MNLR). Quantum chemical calculations using density functional theory (B3LYP/6-31G (d) DFT) methods was performed on the studied compounds and used to calculate the electronic and quantum chemical parameters. The models were used to predict the association constant of the DNA drug binding of the test set compounds, and the agreement between the experimental and predicted values was verified. The descriptors determined by QSPR studies were used for the study and design of new compounds. The statistical results indicate that the predicted values were in good agreement with the experimental results (r = 0.935 and r = 0.936 for MLR and MNLR, respectively). To validate the predictive power of the resulting models, the external validation multiple correlation coefficients were 0.932 and 0.939 for the MLR and the MNLR, respectively. These results show that both models possess a favourable estimation stability and good prediction power.

The inhibitory activity of aldose reductase of flavonoid compounds: Combining DFT and QSAR calculations

Abstract The DFT-B3LYP method, with the base set 6-31G (d), was used to calculate several quantum chemical descriptors of 44 substituted flavonoids. The best descriptors were selected to establish the quantitative structure activity relationship (QSAR) of the inhibitory activity against aldose reductase using principal components analysis (PCA), multiple regression analysis (MLR), nonlinear regression (RNLM) and an artificial neural network (ANN). We propose a quantitative model according to these analyses, and we interpreted the activity of the compounds based on the multivariate statistical analysis.

Investigation of Antileishmanial Activities of Acridines Derivatives against Promastigotes and Amastigotes Form of Parasites Using Quantitative Structure Activity Relationship Analysis

In a search of newer and potent antileishmanial (against promastigotes and amastigotes form of parasites) drug, a series of 60 variously substituted acridines derivatives were subjected to a quantitative structure activity relationship (QSAR) analysis for studying, interpreting, and predicting activities and designing new compounds by using multiple linear regression and artificial neural network (ANN) methods. The used descriptors were computed with Gaussian 03, ACD/ChemSketch, Marvin Sketch, and ChemOffice programs. The QSAR models developed were validated according to the principles set up by the Organisation for Economic Co-operation and Development (OECD). The principal component analysis (PCA) has been used to select descriptors that show a high correlation with activities. The univariate partitioning (UP) method was used to divide the dataset into training and test sets. The multiple linear regression (MLR) method showed a correlation coefficient of 0.850 and 0.814 for antileishmanial activities against promastigotes and amastigotes forms of parasites, respectively. Internal and external validations were used to determine the statistical quality of QSAR of the two MLR models. The artificial neural network (ANN) method, considering the relevant descriptors obtained from the MLR, showed a correlation coefficient of 0.933 and 0.918 with 7-3-1 and 6-3-1 ANN models architecture for antileishmanial activities against promastigotes and amastigotes forms of parasites, respectively. The applicability domain of MLR models was investigated using simple and leverage approaches to detect outliers and outsides compounds. The effects of different descriptors in the activities were described and used to study and design new compounds with higher activities compared to the existing ones.

3D QSAR studies, molecular docking and ADMET evaluation, using thiazolidine derivatives as template to obtain new inhibitors of PIM1 kinase

Proviral Integration site for Moloney murine leukemia virus-1 (PIM1) belongs to the serine/threonine kinase family of Ca2+-calmodulin-dependent protein kinase (CAMK) group, which is involved in cell survival and proliferation as well as a number of other signal transduction pathways. Thus, PIM1 is regarded as a promising target for treatment of cancers. In the present paper, a three-dimensional quantitative structure activity relationship (3D-QSAR) and molecular docking were performed to investigate the binding between PIM1 and thiazolidine inhibitors in order to design potent inhibitors. The comparative molecular similarity indices analysis (CoMSIA) was developed using twenty-six molecules having pIC50 ranging from 8.854 to 6.011 (IC50 in nM). The best CoMSIA model gave significant statistical quality. The determination coefficient (R2) and Leave-One-Out cross-validation coefficient (Q2) are 0.85 and 0.58, respectively. Furthermore, the predictive ability of this model was evaluated by external validation((n = 11, R2test = 0.72, and MAE = 0.170 log units). The graphical contour maps could provide structural features to improve inhibitory activity. Furthermore, a good consistency between contour maps and molecular docking strongly demonstrates that the molecular modeling is reliable. Based on these satisfactory results, we designed several new potent PIM1 inhibitors and their inhibitory activities were predicted by the molecular models. Additionally, those newly designed inhibitors, showed promising results in the preliminary in silico ADMET evaluations, compared to the best inhibitor from the studied dataset. The results expand our understanding of thiazolidines as inhibitors of PIM1 and could be of great help in lead optimization for early drug discovery of highly potent inhibitors.

Predictive modelling of the LD50 activities of coumarin derivatives using neural statistical approaches: Electronic descriptor-based DFT

Abstract A study of structure–activity relationship (QSAR) was performed on a set of 30 coumarin-based molecules. This study was performed using multiple linear regressions (MLRs) and an artificial neural network (ANN). The predicted values of the antioxidant activities of coumarins were in good agreement with the experimental results. Several statistical criteria, such as the mean square error (MSE) and the correlation coefficient (R), were studied to evaluate the developed models. The best results were obtained with a network architecture [8-4-1] (R = 0.908, MSE = 0.032), activation functions (tansig–purelin) and the Levenberg–Marquardt learning algorithm. The model proposed in this study consists of large electronic descriptors that are used to describe these molecules. The results suggested that the proposed combination of calculated parameters may be useful for predicting the antioxidant activities of coumarin derivatives.

Furanone derivatives as new inhibitors of CDC7 kinase: development of structure activity relationship model using 3D QSAR, molecular docking, and in silico ADMET

Cell division cycle 7 (CDC7) is a serine/threonine kinase, which plays a vital role in the replication initiation of DNA synthesis. Overexpression of the CDC7 in various tumor growths and in cell proliferation makes it a promising target for treatment of cancers. To investigate the binding between the CDC7 and furanone inhibitors, and in order to design highly potent inhibitors, a three-dimensional quantitative structure activity relationship (3D-QSAR) with molecular docking was performed. The optimum CoMSIA model showed significant statistical quality on all validation methods with a determination coefficient (R2 = 0.945), bootstrapping R2 mean (BS-R2 = 0.960), and leave-one-out cross-validation (Q2) coefficient of 0.545. The predictability of this model was evaluated by external validation using a test set of nine compounds with a predicted determination coefficient R2test of 0.96, besides the mean absolute error (MAE) of the test set was 0.258 log units. The extracted contour maps were used to identify the important regions, where the modification was necessary to design a new molecule with improved activity. Furthermore, a good consistency between the molecular docking and contour maps strongly demonstrates that the molecular modeling is reliable. Based on those obtained results, we designed several new potent CDC7 inhibitors, and their inhibitory activities were validated by the molecular models. Additionally, those newly designed inhibitors showed promising results in the preliminary in silico ADMET evaluations.

Combining DFT and QSAR computation to predict the interaction of flavonoids with the GABA (A) receptor using electronic and topological descriptors

Abstract To establish a quantitative structure-activity relationship model of the binding affinity constants (−log Ki) of 41 flavonoid derivatives towards the GABA (A) receptor, the DFT-B3LYP method with basis set 6-31G (d) was performed to gain insights into the chemical structure and property information for the studied compounds. The best topological and electronic descriptors were selected. This work was conducted with principal component analysis (PCA), multiple linear regression (MLR), multiple non-linear regression (MNLR) and artificial neural network (ANN). According to these analyses, we propose quantitative models and interpret the activity of the compounds based on multivariate statistical analysis. The statistical results of the MLR, MNLR and ANN indicate that the determination coefficients R2 were 0.896, 0.925 and 0.916, respectively. The results show that the three modelling methods can predict the studied activity well and may be useful for predicting the biological activity of new compounds. The statistical results indicate that the models are statistically significant and stable with data variation in the external validation.

Combining DFT and QSAR studies for predicting psychotomimetic activity of substituted phenethylamines using statistical methods

Abstract The DFT-B3LYP method, with the base set 6-31G (d) was used to calculate electronic and charge descriptors. The present study was performed using principal component analysis (PCA), multiple linear regression analysis (MLR) and non-linear multiple regression analysis (MNLR) to predict unambiguous QSAR models of 46 substituted phenethylamines toward psychotomimetic activity. Results showed that the MLR and MNLR predict activity in a satisfactory manner. But among those models, we concluded that the latter one provides a better agreement between calculated and observed values of psychotomimetic activity. Also it shows very good stability towards data variations for the validation methods.