A novel range based QSAR study of human neuropeptide Y (NPY) Y5 receptor inhibitors

Discovery of new Mycobacterium tuberculosis proteasome inhibitors using a knowledge-based computational screening approach

Mycobacterium tuberculosis bacteria cause deadly infections in patients [Corrected]. The rise of multidrug resistance associated with tuberculosis further makes the situation worse in treating the disease. M. tuberculosis proteasome is necessary for the pathogenesis of the bacterium validated as an anti-tubercular target, thus making it an attractive enzyme for designing Mtb inhibitors. In this study, a computational screening approach was applied to identify new proteasome inhibitor candidates from a library of 50,000 compounds. This chemical library was procured from the ChemBridge (20,000 compounds) and the ChemDiv (30,000 compounds) databases. After a detailed analysis of the computational screening results, 50 in silico hits were retrieved and tested in vitro finding 15 compounds with IC₅₀ values ranging from 35.32 to 64.15 μM on lysate. A structural analysis of these hits revealed that 14 of these compounds probably have non-covalent mode of binding to the target and have not reported for anti-tubercular or anti-proteasome activity. The binding interactions of all the 14 protein-inhibitor complexes were analyzed using molecular docking studies. Further, molecular dynamics simulations of the protein in complex with the two most promising hits were carried out so as to identify the key interactions and validate the structural stability.

Antiproliferative novel isoxazoles: modeling, virtual screening, synthesis, and bioactivity evaluation

A series of novel isoxazole derivatives were efficiently synthesized through the adaptation/modification of an in situ synthetic procedure for pyrazoles. All novel compounds were tested against four different cell lines to evaluate their antiproliferative activity. Based on the Hela cells results of this study and previous work, a classification model to predict the anti-proliferative activity of isoxazole and pyrazole derivatives was developed. Random Forest modeling was used in view of the development of an accurate and reliable model that was subsequently validated. A virtual screening study was then proposed for the design of novel active derivatives. Compounds 9 and 11 demonstrated significant cytostatic activity; the fused isoxazole derivative 18 and the virtually proposed compound 2v, were proved at least 10 times more potent as compared to compound 9, with IC50 values near and below 1 μM. In conclusion, a new series of isoxazoles was exploited with some of them exhibiting promising cytostatic activities. Further studies on the substitution pattern of the isoxazole core can potentially provide compounds with cytostatic action at the nM scale. In this direction the in silico approach described herein can also be used to screen existing databases to identify derivatives with anticipated activity.

Docking-based CoMFA and CoMSIA study of azaindole carboxylic acid derivatives as promising HIV-1 integrase inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed based on a series of azaindole carboxylic acid derivatives that had previously been reported as promising HIV-1 integrase inhibitors. Docking studies to explore the binding mode were performed based on the highly active molecule 36. The best docked conformation of molecule 36 was used as template for alignment. The comparative molecular field analysis (CoMFA) model (including steric and electrostatic fields) yielded the cross validation q (2) = 0.655, non-cross validation r (2) = 0.989 and predictive r (2) pred = 0.979. The best comparative molecular similarity indices analysis (CoMSIA) model (including steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields) yielded the cross validation q (2) = 0.719, non-cross validation r (2) = 0.992 and predictive r (2) pred = 0.953. A series of new azaindole carboxylic acid derivatives were designed and the HIV-1 integrase inhibitory activities of these designed compounds were predicted based on the CoMFA and CoMSIA models.

Ligand-based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks

In this work we have developed an in silico model to predict the inhibition of β-amyloid aggregation by small organic molecules. In particular we have explored the inhibitory activity of a series of 62 N-phenylanthranilic acids using Kohonen maps and Counterpropagation Artificial Neural Networks. The effects of various structural modifications on biological activity are investigated and novel structures are designed using the developed in silico model. More specifically a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described. The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence.

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.