Pharmacophore-similarity-based QSAR (PS-QSAR) for group-specific biological activity predictions
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Recent technological breakthroughs in medicinal chemistry arena had ameliorated the perspectives of quantitative structure–activity relationship (QSAR) methods. In this direction, we developed a group-based QSAR method based on pharmacophore-similarity concept which takes into account the 2D topological pharmacophoric descriptors and predicts the group-specific biological activities. This activity prediction may assist the contribution of certain pharmacophore features encoded by respective fragments toward activity improvement and/or detrimental effects. We termed this method as pharmacophore-similarity-based QSAR (PS-QSAR) and studied the activity contribution of fragments from 3-hydroxypyridinones derivatives possessing antimalarial activities.Keywords:
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Recent technological breakthroughs in medicinal chemistry arena had ameliorated the perspectives of quantitative structure–activity relationship (QSAR) methods. In this direction, we developed a group-based QSAR method based on pharmacophore-similarity concept which takes into account the 2D topological pharmacophoric descriptors and predicts the group-specific biological activities. This activity prediction may assist the contribution of certain pharmacophore features encoded by respective fragments toward activity improvement and/or detrimental effects. We termed this method as pharmacophore-similarity-based QSAR (PS-QSAR) and studied the activity contribution of fragments from 3-hydroxypyridinones derivatives possessing antimalarial activities.
Similarity (geometry)
Molecular descriptor
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Endothelial Nitric Oxide synthase (eNOS) has an emerging role in chronic inflammation and cancer thus prompting continuous attempts to discover new inhibitors of this enzyme. Towards this end, efforts to discover and optimize new eNOS inhibitors are essential. Therefore, we explored the pharmacophoric space of 151 eNOS inhibitors using ten diverse sets of inhibitors to identify high quality pharmacophores. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing a self-consistent quantitative structure-activity relationship (QSAR) of optimal predictive potential (r2121 = 0.77, F = 63.5, r2LOO = 0.62, and r2PRESS against 30 external test inhibitors = 0.63). Interestingly, only one pharmacophore emerged in the optimal QSAR equation. Comparisons with the binding site of eNOS and receiver-operating characteristic (ROC) curves analysis established the validity of this QSAR-selected pharmacophore model. We employed the pharmacophoric model and associated QSAR equation to screen the national cancer institute list of compounds (NCI).
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In the present study, 3D-QSAR analysis was performed on a set of 37 TGF-β inhibitors utilizing pharmacophore based alignment to uncover the essential structural and steric features of these newly discovered b-annulated 1,4- dihydropyridine (1,4-DHP) molecules to get better antagonism of the TGF-β receptor. The best 3D-QSAR model identified with PLS factor 4 that had the highest values of external predictability parameters exhibited Q2 (0.8972), and R2 (0.9826) and displayed high values of F (281.9) and low SD (0.0785). This selected model was validated statistically by determining Pearson-r (0.9718) for test set molecules. Contours thus obtained from different properties generated using our QSAR model explained the variation in the activity of dataset with respect to different attachments in the core structure. This would help to make suitable structural modifications in 1, 4-DHP molecules so as to make a better complementary fit to the active site of TGF-β receptor, which in turn would improve the potency of newly designed molecules. Keywords: Atom based QSAR, Pharmacophore, TGFβ signalling, Cardiogenesis.
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A definition of a pharmacophore for the 5-HT7 antagonists was carried out by searching the common chemical features of selective antagonists from the literature. A molecular design is described by analyzing the differences between this new pharmacophore and three other 3D serotonin pharmacophores previously described. This comparison led to the synthesis of a new series of potent 5-HT7 antagonists.
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Quantitative structure–activity relationship studies have been performed on twenty one β-carboline derivatives to investigate the structural requirements for antitumor activity. The best 2D-QSAR model (r2 = 0.802, F = 24.321, r2se = 0.325) indicated statistical significance and internal predictivity of the developed model shown by the value of cross validated squared correlation coefficient which was 0.724. A five-point pharmacophore hypothesis yielded ligand based pharmacophore 3D-QSAR models with good partial least-square (PLS) statistics results. The training set and test set correlation was characterized by PLS factors (r 2 = 0.842, SD = 0.306, F = 21.3, P = 4.27e -05, Q2 ext = 0.748, RMSE = 0.531, Pearson-R = 0.975). A docking study revealed the binding orientations of DNA intercalates at active site of amino acid residues. The results of 2D-QSAR and 3D-QSAR give detailed structural insights and at the same time highlight the important binding features of novel β-carboline derivatives as antitumor agents. Keywords: β-carboline, Antitumor, Pharmacophore, 2D-QSAR, 3D-QSAR, Docking.
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