QSAR studies on 1-phenylbenzimidazoles as inhibitors of the platelet-derived growth factor

The selective synthesis of N-arylbenzene-1,2-diamines or 1-arylbenzimidazoles by irradiating 4-methoxy-4'-substituted-azobenzenes in different solvents

The solvent-controllable photoreaction of 4-methoxyazobenzenes to afford 1-aryl-1H-benzimidazoles or N-arylbenzene-1,2-diamines has been studied. The irradiation of 4-methoxyazobenzenes in DMF containing 0.5 M hydrochloric acid provided N²-aryl-4-methoxybenzene-1,2-diamines as the major product, while irradiation in acetal containing 0.16 M hydrochloric acid led to 1-aryl-6-methoxy-2-methyl-1H-benzimidazoles as the major product. A possible reaction mechanism explaining the selectivity was also discussed.

A solvent-controllable photoreaction involving 4-methoxyazobenzenes has been developed to synthesize 1-aryl-1H-benzimidazoles or N-arylbenzene-1,2-diamines in moderate to good yields.

Identification of Natural Compounds against Neurodegenerative Diseases Using In Silico Techniques

The aim of this study was to identify new potentially active compounds for three protein targets, tropomyosin receptor kinase A (TrkA), N-methyl-d-aspartate (NMDA) receptor, and leucine-rich repeat kinase 2 (LRRK2), that are related to various neurodegenerative diseases such as Alzheimer's, Parkinson's, and neuropathic pain. We used a combination of machine learning methods including artificial neural networks and advanced multilinear techniques to develop quantitative structure⁻activity relationship (QSAR) models for all target proteins. The models were applied to screen more than 13,000 natural compounds from a public database to identify active molecules. The best candidate compounds were further confirmed by docking analysis and molecular dynamics simulations using the crystal structures of the proteins. Several compounds with novel scaffolds were predicted that could be used as the basis for development of novel drug inhibitors related to each target.

Involvement of conformational isomerism in the complexity of the crystal network of 1-(4-nitrophenyl)-1H-1,3-benzimidazole derivatives driven by C-H...A (A = NO2, Npy and π) and orthogonal Npy...NO2 and ONO...Csp2 interactions

A detailed structural analysis of the benzimidazole nitroarenes 1-(4-nitrophenyl)-1H-1,3-benzimidazole, C13H9N3O2, (I), 1-(4-nitrophenyl)-2-phenyl-1H-1,3-benzimidazole, C19H13N3O2, (II), and 2-(3-methylphenyl)-1-(4-nitrophenyl)-1H-1,3-benzimidazole, C20H15N3O2, (III), has been performed. They are nonplanar structures whose crystal arrangement is governed by Csp2-H...A (A = NO2, Npy and π) hydrogen bonding. The inherent complexity of the supramolecular arrangements of compounds (I) (Z' = 2) and (II) (Z' = 4) into tapes, helices and sheets is the result of the additional participation of π-πNO2 and n-π* (n = O and Npy; π* = Csp2 and NNO2) interactions that contribute to the stabilization of the equi-energetic conformations adopted by each of the independent molecules in the asymmetric unit. In contrast, compound (III) (Z' = 1) is self-paired, probably due to the effect of the steric demand of the methyl group on the crystal packing. Theoretical ab initio calculations confirmed that the presence of the arene ring at the benzimidazole 2-position increases the rotational barrier of the nitrobenzene ring and also supports the electrostatic nature of the orthogonal ONO...Csp2 and Npy...NO2 interactions.

π-stacking and C—X...D(X= H, NO2;D= O, π) interactions in the crystal network of both C—H...N and π-stacked dimers of 1,2-bis(4-bromophenyl)-1H-benzimidazole and 2-(4-bromophenyl)-1-(4-nitrophenyl)-1H-benzimidazole

Molecules of 1,2-bis(4-bromophenyl)-1H-benzimidazole, C19H12Br2N2, (I), and 2-(4-bromophenyl)-1-(4-nitrophenyl)-1H-benzimidazole, C19H12BrN3O2, (II), are arranged in dimeric units through C—H...N and parallel-displaced π-stacking interactions favoured by the appropriate disposition of N- and C-bonded phenyl rings with respect to the mean benzimidazole plane. The molecular packing of the dimers of (I) and (II) arises by the concurrence of a diverse set of weak intermolecular C—X...D(X= H, NO2;D= O, π) interactions.

Docking and QSAR comparative studies of polycyclic aromatic hydrocarbons and other procarcinogen interactions with cytochromes P450 1A1 and 1B1

To obtain chemical clues on the process of bioactivation by cytochromes P450 1A1 and 1B1, some QSAR studies were carried out based on cellular experiments of the metabolic activation of polycyclic aromatic hydrocarbons and heterocyclic aromatic compounds by those enzymes. Firstly, the 3D structures of cytochromes 1A1 and 1B1 were built using homology modelling with a cytochrome 1A2 template. Using these structures, 32 ligands including heterocyclic aromatic compounds, polycyclic aromatic hydrocarbons and corresponding diols, were docked with LigandFit and CDOCKER algorithms. Binding mode analysis highlighted the importance of hydrophobic interactions and the hydrogen bonding network between cytochrome amino acids and docked molecules. Finally, for each enzyme, multilinear regression and artificial neural network QSAR models were developed and compared. These statistical models highlighted the importance of electronic, structural and energetic descriptors in metabolic activation process, and could be used for virtual screening of ligand databases. In the case of P450 1A1, the best model was obtained with artificial neural network analysis and gave an r (2) of 0.66 and an external prediction [Formula: see text] of 0.73. Concerning P450 1B1, artificial neural network analysis gave a much more robust model, associated with an r (2) value of 0.73 and an external prediction [Formula: see text] of 0.59.

QSAR prediction of D2 receptor antagonistic activity of 6-methoxy benzamides

Quantitative structure-activity relationship (QSAR) method was used to predict the pIC(50) value of 58 derivatives of 6-methoxy benzamides in this work. The artificial neural network (ANN) and multiple linear regressions (MLR) were used to construct the non-linear and linear QSAR models, respectively. The standard errors in the prediction of pIC(50) for training, internal and external test sets, are; 0.280, 0.446 and 0.382 by MLR model and are; 0.175, 0.326 and 0.296 by ANN model, respectively. Also these models were further examined by cross-validation methods which produce the statistics of Q(2)=0.8340 and SPRESS=0.322 for MLR model and Q(2)=0.8055 and SPRESS=0.219 for ANN model.

QSPR modeling of flash points: An update

Quantitative structure-property relationship (QSPR) models for the flash points of 758 organic compounds are developed using geometrical, topological, quantum mechanical and electronic descriptors calculated by CODESSA PRO software. Multilinear regression models link the structures to their reported flash point values. We also report a nonlinear model based on an artificial neural network. The results are discussed in the light of the main factors that influence the property under investigation and its modeling.

QSAR modeling of anti-invasive activity of organic compounds using structural descriptors

The anti-invasive activity of 139 compounds was correlated by an artificial neural network approach with descriptors calculated solely from the molecular structures using CODESSA Pro. The best multilinear regression method implemented in CODESSA Pro was used for a pre-selection of descriptors. The resulting nonlinear (artificial neural network) QSAR model predicted the exact class for 66 (71%) of the training set of 93 compounds and 32 (70%) of validation set of 46 compounds. The standard deviation ratios for the both training and validation sets are less than unity, indicating a satisfactory predictive capability for classification of the nature of the anti-invasive activity data. The proposed model can be used for the prediction of the anti-invasive activity of novel classes of compounds enabling a virtual screening of large databases of anticancer drugs.

Neural Networks Convergence Using Physicochemical Data

An investigation of the neural network convergence and prediction based on three optimization algorithms, namely, Levenberg-Marquardt, conjugate gradient, and delta rule, is described. Several simulated neural networks built using the above three algorithms indicated that the Levenberg-Marquardt optimizer implemented as a back-propagation neural network converged faster than the other two algorithms and provides in most of the cases better prediction. These conclusions are based on eight physicochemical data sets, each with a significant number of compounds comparable to that usually used in the QSAR/QSPR modeling. The superiority of the Levenberg-Marquardt algorithm is revealed in terms of functional dependence of the change of the neural network weights with respect to the gradient of the error propagation as well as distribution of the weight values. The prediction of the models is assessed by the error of the validation sets not used in the training process.

QSAR study of antiplatelet agents

A QSAR methodology that involves multilinear (Hansch-type) and nonlinear (ANN backpropagation) approaches was developed to correlate the antiplatelet activity of 60 benzoxazinone derivatives against factor Xa. The statistical characteristics provided by multilinear model (R2 = 0.821) indicated satisfactory stability and predictive ability, while the ANN predictive ability is somewhat superior (R2 = 0.909). The multilinear model provided insight into the main factors that modulate the inhibitory activity of the investigated compounds.