Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery
For a series of 35 piperazino-phthalimide and piperazino-isoindolinone based urotensin-II receptor (UT) antagonists, a thoroughly validated 3D pharmacophore model has been developed, consisting of four chemical features: one hydrogen bond acceptor lipid (HBA_L), one hydrophobe (HY), and two ring aro...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2014/921863 |
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| author | Anubhuti Pandey Sarvesh Kumar Paliwal Shailendra Kumar Paliwal |
| author_facet | Anubhuti Pandey Sarvesh Kumar Paliwal Shailendra Kumar Paliwal |
| author_sort | Anubhuti Pandey |
| collection | DOAJ |
| description | For a series of 35 piperazino-phthalimide and piperazino-isoindolinone based urotensin-II receptor (UT) antagonists, a thoroughly validated 3D pharmacophore model has been developed, consisting of four chemical features: one hydrogen bond acceptor lipid (HBA_L), one hydrophobe (HY), and two ring aromatic (RA). Multiple validation techniques like CatScramble, test set prediction, and mapping analysis of advanced known antagonists have been employed to check the predictive power and robustness of the developed model. The results demonstrate that the best model, Hypo 1, shows a correlation (r) of 0.902, a root mean square deviation (RMSD) of 0.886, and the cost difference of 39.69 bits. The model obtained is highly predictive with good correlation values for both internal (r2=0.707) as well as external (r2=0.614) test set compounds. Moreover, the pharmacophore model has been used as a 3D query for virtual screening which served to detect prospective new lead compounds which can be further optimized as UT antagonists with potential for treatment of cardiovascular diseases. |
| format | Article |
| id | doaj-art-33c14cb26b2c4730a6f3054d71652719 |
| institution | Kabale University |
| issn | 2090-9063 2090-9071 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Chemistry |
| spelling | doaj-art-33c14cb26b2c4730a6f3054d716527192025-02-03T05:46:10ZengWileyJournal of Chemistry2090-90632090-90712014-01-01201410.1155/2014/921863921863Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug DiscoveryAnubhuti Pandey0Sarvesh Kumar Paliwal1Shailendra Kumar Paliwal2Department of Pharmacy, Banasthali University, Rajasthan 304022, IndiaDepartment of Pharmacy, Banasthali University, Rajasthan 304022, IndiaDepartment of Pharmacy, LLRM Medical College, Meerut, Uttar Pradesh 250004, IndiaFor a series of 35 piperazino-phthalimide and piperazino-isoindolinone based urotensin-II receptor (UT) antagonists, a thoroughly validated 3D pharmacophore model has been developed, consisting of four chemical features: one hydrogen bond acceptor lipid (HBA_L), one hydrophobe (HY), and two ring aromatic (RA). Multiple validation techniques like CatScramble, test set prediction, and mapping analysis of advanced known antagonists have been employed to check the predictive power and robustness of the developed model. The results demonstrate that the best model, Hypo 1, shows a correlation (r) of 0.902, a root mean square deviation (RMSD) of 0.886, and the cost difference of 39.69 bits. The model obtained is highly predictive with good correlation values for both internal (r2=0.707) as well as external (r2=0.614) test set compounds. Moreover, the pharmacophore model has been used as a 3D query for virtual screening which served to detect prospective new lead compounds which can be further optimized as UT antagonists with potential for treatment of cardiovascular diseases.http://dx.doi.org/10.1155/2014/921863 |
| spellingShingle | Anubhuti Pandey Sarvesh Kumar Paliwal Shailendra Kumar Paliwal Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery Journal of Chemistry |
| title | Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery |
| title_full | Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery |
| title_fullStr | Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery |
| title_full_unstemmed | Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery |
| title_short | Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery |
| title_sort | chemical feature based molecular modeling of urotensin ii receptor antagonists generation of predictive pharmacophore model for early drug discovery |
| url | http://dx.doi.org/10.1155/2014/921863 |
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