Anand Gaurav

3D-QSAR studies of 4-quinolone derivatives as high-affinity ligands at the benzodiazepine site of brain GABAA receptors

Three dimensional quantitative structure activity relationship approach using CoMFA and CoMSIA was applied to a series of 4-quinolone derivatives as high-affinity ligands at the benzodiazepine site of brain GABAA receptors. For the purpose, 27 compounds were used to develop models. 3D-QSAR models with high-squared correlation coefficient of up to 0.979 for CoMFA and 0.931 for CoMSIA were established. The robustness of the model was confirmed with the help of leave one out cross-validation method with rcv2 values of up to 0.526 and 0.546 for CoMFA and CoMSIA, respectively. Developed models highlighted the importance of shape of the molecules, i.e., steric descriptors for GABAA receptor binding.

Pharmacophore modeling, 3DQSAR, and docking-based design of polysubstituted quinolines derivatives as inhibitors of phosphodiesterase 4, and preliminary evaluation of their anti-asthmatic potential

Abstract3D QSAR models using 3D pharmacophore, CoMFA, and CoMSIA approaches were developed for a series of quinoline derivatives as PDE4 receptor antagonists. Hypogen method was used to engender the 3D pharmacophore model. The top scoring four feature pharmacophore models, Hypo1 contains one hydrogen-bond acceptor, two hydrogen-bond donors, and a hydrophobic feature. Hypo1 was validated using test set, Fischer’s randomization test, and screening of decoy set. CoMFA and CoMSIA models were developed using the alignment obtained by pharmacophore (Hypo1), substructure alignment, and by application of region focusing. Substructure alignment followed by region focusing provided the best CoMFA and CoMSIA models. Based on the results of 3D QSAR studies, some new molecules were designed and evaluated by Docking and Lipinski filters. The designed molecules were synthesized and two top scoring molecules were subjected to preliminary evaluation of their efficacy in treatment of asthma and COPD. The molecules demonstrated efficacy comparable to standard drugs in treatment of asthma and COPD.

Structure-based three-dimensional pharmacophores as an alternative to traditional methodologies

Structure-based pharmacophore approaches have become widely used in drug discovery and design. This can be attributed to the development of new tools and methods over the past decade. Various tools based on different premises have been developed, including active site information in traditional pharmacophores. These tools have been widely used in virtual screening, de novo design, and lead optimization and been proven to be highly successful. Studies based on simultaneous use of structure-based pharmacophores, ligand-based phar- macophores, and docking have also come into the picture recently. Here, the development of structure-based pharmacophores as an alternative to traditional drug discovery approaches is discussed, with emphasis on the advances and latest developments in tools and success stories

3D QSAR Pharmacophore, CoMFA and CoMSIA Based Design and Docking Studies on Phenyl Alkyl Ketones as Inhibitors of Phosphodiesterase 4

In the present study, three dimensional quantitative structure activity relationship (3D QSAR) models using 3D Pharmacophore, CoMFA and CoMSIA approaches were developed for a series of phenyl alkyl ketone derivatives as PDE4 receptor antagonists. An ideal 3D QSAR pharmacophore model was developed and validated using external test set, Fischers randomization method and decoy set screening. The top scoring four feature pharmacophore model, Hypo1, includes two hydrogen bond acceptors, two hydrophobic features. Amongst the developed models, Hypo1 has the maximum correlation coefficient (0.9658), cost difference (349.593), low RMS (1.41), and high goodness of fit. CoMFA and CoMSIA models were developed based on the alignment obtained using the pharmacophore (Hypo1), substructure alignment and by application of region focusing. The robustness of CoMFA and CoMSIA model was confirmed with the help of leave one out cross-validation method, while the predictive ability of models was tested using a test set. 3D-QSAR models with high squared correlation coefficient of up to 0.9720 for CoMFA and 0.9610 for CoMSIA were established. Robustness of the models is demonstrated by R(2)(cv) values of up to 0.7582 and 0.8539 for CoMFA and CoMSIA, respectively. Predictive ability of the models is reflected by R(2)(pred) values of 0.9630 and 0.9470 for CoMFA and CoMSIA respectively. Novel molecules were designed on the basis of results of 3D QSAR studies. Designed molecules were evaluated by Docking and Lipinski filters. Predicted activity of the designed molecules correlated well with the docking scores and the molecules also passed the Lipinski filters.

QSAR Studies on 4-Quinolone Derivatives as High-Affinity Ligands at the Benzodiazepine Site of Brain GABAA Receptors

Quantitative structure activity relationship approach using stepwise regression analysis was applied to a series of 4-quinolone derivatives as high-affinity ligands at the benzodiazepine site of brain GABA(A) receptors. For the purpose 25 compounds were used to develop models. Statistically significant equations were obtained with high squared correlation coefficient (r(2)=0.8761, 0.9295 and 0.8705) and low root mean square error (RMSE=0.4844, 0.3894 and 0.4952). The robustness of the model was confirmed with the help of leave one out cross validation method which exhibited high r(2)(cv) values (r(2)(cv)=0.7875, 0.8263 and 0.7927). A good correlation of various molecular shape parameters, like ovality, Szeged index, and energy of the molecule with the GABA(A) affinity was achieved.