QSAR studies of N1-(5-chloro-2-pyridyl)-2-{[4-(alkyl methyl)benzoyl]amino}-5-chlorobenzamide analogs

Qualitative and quantitative pharmacophore-similarity assessment of anthranilamide-based factor Xa inhibitors: applications on similar molecules with identical biological endpoints

It is a conventional practice to exclude molecules with identical biological endpoints to avoid bias in the resulting hypothesis model. Despite the diverse chemical functionalities, the receptor interactions of such molecules are often unexplored. The present study motivates the selection of these molecules diversified by single atom or functional group compared to internal molecules as external set and helps in the understanding of corresponding effects toward receptor interactions and biological endpoints. Applied on anthranilamide-series of factor Xa analogs, the inhibitory activities were correlated (r(2) = 0.99) and validated (q(2) = 0.68) with distance-based pharmacophore descriptors using support vector machine. The selected external set molecules exhibited better prediction accuracy by securing activities less than one residual threshold. The effect on inhibitory activity was assessed by the examination of pharmacophore-similarity and its interactions with key residues of Human factor Xa enzyme using molecular docking approach. Furthermore, qualitative pharmacophore models were developed on the subset of molecular dataset divided as most actives, moderately actives and least actives, to recognize crucial activity governing pharmacophore features. The outcome of this study will bring new insights about the requirements of pharmacophore features and prioritizes its selection in the design and optimization of potent Xa inhibitors.

Application quantum and physico chemical molecular descriptors utilizing principal components to study mode of anticoagulant activity of pyridyl chromen-2-one derivatives

Factors II, V, VII and Xa have materialized as a key enzymes for the intervention of blood coagulation cascade and for the development of new anti thrombotic agents. The combined density functional quantum mechanical/molecular mechanical (QM/MM) approach has been used to access inhibition of prothrombin and thrombin production. The biological activities of coumarin derivatives as clotting factor inhibitors was quantitatively analyzed in terms of physicochemical parameters utilizing the principal component analysis. Structural requirements for maximal potency were derived from the results of a quantitative structure activity relationship analysis.

Synthesis and structure-activity relationships of novel pyrimido[1,2-b]indazoles as potential anticancer agents against A-549 cell lines

A series of novel pyrimido[1,2-b]indazoles 5, 7 have been prepared from 3-trifluoromethyl-5-phenyl-2,6-dicyano anilines 1 via novel indazole regioisomers 3 and 4 through a facile strategy. Specific examples were evaluated for anticancer activity in vitro and found to exhibit promising activity against A-549 cell lines and are more effective than Etoposide. QSAR models were developed and validated by cross-validation method. The results of the best QSAR model were further compared with the crystal structure of tubulin protein. The binding energies estimated were found to have a good correlation with the experimental inhibitory potencies.

Quantitative structure activity relationship studies of aryl heterocycle-based thrombin inhibitors

A quantitative structure activity relationship (QSAR) analysis has been performed on a data set of 42 aryl heterocycle-based thrombin inhibitors. Several types of descriptors including topological, spatial, thermodynamic, information content and E-state indices were used to derive a quantitative relationship between the anti thrombin activity and structural properties. Genetic algorithm based genetic function approximation method of variable selection was used to generate the model. Best model was developed when number of descriptors in the equation was set to five. Highly statistically significant model was obtained with atom type logP descriptors, logP and Shadow_YZ. The model is not only able to predict the activity of new compounds but also explained the important regions in the molecules in a quantitative manner.