Molecular modeling studies of pyridopurinone derivatives—Potential phosphodiesterase 5 inhibitors

Srivani, P.; Srinivas, Eedubilli; Raghu, R.; Sastry, G. Narahari

doi:10.1016/j.jmgm.2007.01.007

Cited by 43 publications

(22 citation statements)

References 45 publications

(45 reference statements)

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“…The major benefit of CADD in drug discovery is that it costs much less than any biomedical test of inhibition in cells just like in the references from APS [22][23][24][25][26][27][28] . The ligand based method is built on regression analysis for molecular structure and properties against activities, while the protein based method focuses on the docking procedure in which the structure of the protein would be docked with many kinds of inhibitors and the binding energies would be calculated [29,30] . When the patterns of results in the two methods agree with each other, it indicates a reliable outcome.…”

Section: Introductionmentioning

confidence: 99%

Discovery of potent inhibitors for phosphodiesterase 5 by virtual screening and pharmacophore analysis

et al. 2009

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Aim: To explore the potent inhibitor from one of the Traditional Chinese medicine (TCM), Epimedium sagittatum. Methods: We predicted the potent compound, eS03b, de novo evolution from the four Epimedium sagittatum components were verified by molecular docking, pharmacophore analysis, and analysis of quantitative structure-activity relationship (QSAR) model, which was constructed by multiple linear regression. Results: ES03b was chosen to undergo drug modification via de novo evolution. By analyzing the pharmacophore features, we found that the hydrophobic core in the binding site and the hydrogen bond generated at Asn663 played key roles in designing PDE5 inhibitors. eS03b generated 49 diversities (evo01-49). evo48 had high activity in prediction. Although the value of prediction was overestimated, evo48 was suggested as the potent lead. Conclusion: In this study, we showed that the hydrophobic core in the binding site and hydrogen bond production on Asn663 played key roles to design PDE5 inhibitors. From several require validation analysis, Evo48 was suggested to be a potent inhibitor.

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Section: Introductionmentioning

confidence: 99%

Discovery of potent inhibitors for phosphodiesterase 5 by virtual screening and pharmacophore analysis

et al. 2009

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“…Noticeably, subtype selectivity of PDEs were reported to be an important parameter in drug design approaches [66,67]. PDEs have altogether 11 subfamilies where PDE5 has only one subtype, PDE5A.…”

Section: Comparison With Othersmentioning

confidence: 99%

Exploring the structure determinants of pyrazinone derivatives as PDE5 3HC8 inhibitors: An in silico analysis

Ren

et al. 2012

Journal of Molecular Graphics and Modelling

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“…A distance dependent dielectric constant was used (energy falls as 1/r 2 ). To avoid too high and unrealistic energy values inside the molecule, a 30 kcal/mol energy cutoff was specified and the electrostatic fields were rejected at the lattice points with maximal steric interactions [20,39]. The regression analysis was carried out using the partial least-squares (PLS) to derive a linear correlation between the CoMFA fields and the activity values of the reverse transcriptase inhibitors; the R 2 and q 2 values were measured.…”

Section: Comparative Molecular Field (Comfa) Modelmentioning

confidence: 99%

2D and 3D QSAR studies of diarylpyrimidine HIV-1 reverse transcriptase inhibitors

Rebehmed

Barbault

Teixeira

et al. 2008

J Comput Aided Mol Des

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2D and 3D QSAR studies were applied on a set of 28 diarylpyrimidine derivatives to model and understand their HIV-1 reverse transcriptase (RT) inhibitory activities. Special cares were taken to build our set of molecules according to their bioactive conformations which is crucial to elaborate good QSAR models. 2D QSAR was performed using the heuristic method in CODESSA which had led to a linear model (R (2) = 0.928 and s (2) = 0.015) between the inhibitory activity and five descriptors. CoMFA and CoMSIA models were established using SYBYL package of programs. The better predictive ability of the CoMSIA model (q (2) = 0.730) over the CoMFA model (q (2) = 0.597) was assigned to the large contribution of hydrogen-bonding interactions to the inhibitory activity. CoMSIA physicochemical properties are in agreement with the 2D QSAR descriptors. The CoMSIA PLS contour surfaces were mapped to the binding pocket of the RT and showed that the results obtained by the 2D and 3D models are in respect with the protein environment. This link permitted us to validate our model and give important insights for the structure activity interpretations. These results will guide further structural modification and prediction of new HIV-1 RT inhibitors.

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Molecular modeling studies of pyridopurinone derivatives—Potential phosphodiesterase 5 inhibitors

Cited by 43 publications

References 45 publications

Discovery of potent inhibitors for phosphodiesterase 5 by virtual screening and pharmacophore analysis

Discovery of potent inhibitors for phosphodiesterase 5 by virtual screening and pharmacophore analysis

Exploring the structure determinants of pyrazinone derivatives as PDE5 3HC8 inhibitors: An in silico analysis

2D and 3D QSAR studies of diarylpyrimidine HIV-1 reverse transcriptase inhibitors

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