Pharmacophore and molecular docking based identification of novel structurally diverse PDE-5 inhibitors

Paliwal, Sarvesh; Mittal, Anupama; Sharma, Manu; Pandey, Anubhuti; Singh, Aarti; Paliwal, Sunil

doi:10.1007/s00044-014-1144-4

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…To understand the nature of molecular interactions of the lead compounds, we carried out molecular docking studies using DS [ 37 , 38 ]. LibDocker, a molecular dynamics annealing-based algorithm, which is accessible as an extension of DS V.2.0, was used for the docking studies [ 39 , 40 ]. The crystal structures to be used for docking studies were obtained from PDB.…”

Section: Methodsmentioning

confidence: 99%

Pharmacophore-driven identification of N-methyl-D-receptor antagonists as potent neuroprotective agents validated using in vivo studies

Sharma

Mittal

Singh

et al. 2020

Biology Methods and Protocols

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Alzheimer’s disease, apparently the most widespread reason behind dementia, is delineated by a continuous cognitive weakening in the aged. During its progression, N-Methyl-D-Aspartate receptor antagonists are known to play a pivotal part in the mechanisms of learning and memory. Since there is an unmet medical need for the treatment of Alzheimer’s disease, we aim to identify possible chemical compounds targeted towards N-Methyl-D-Aspartate receptors. Three-dimensional models are developed to unveil some of the essential characteristics of the N-Methyl-D-Aspartate receptors by using a collection of already discovered N-Methyl-D-Aspartate receptor inhibitors. This is followed by virtual screening, which results in novel chemical compounds having the potential to inhibit N-Methyl-D-Aspartate receptors. Molecular docking studies and analysis promulgated two lead compounds with a high Libdock score. The compounds are shortlisted based on high estimated activity, fit values, LibDock score, no violation of Lipinski’s and availability for procuring. Finally, the shortlisted compounds are tested by employing in-vivo studies, which we further propose as potential NMDA inhibitors for treating Alzheimer’s Disease.

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Section: Methodsmentioning

confidence: 99%

Pharmacophore-driven identification of N-methyl-D-receptor antagonists as potent neuroprotective agents validated using in vivo studies

Sharma

Mittal

Singh

et al. 2020

Biology Methods and Protocols

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“…Pharmacophore models can be used as initial filters to reduce the number of molecules to be docked, during the docking process as pharmacophore guides and after docking as filters to select ligands and rank the poses (45). Pharmacophore models are used as filters to determine molecules that meet the basic structural and chemical functionality requirements of the query before molecular docking (46). Before the evaluation using docking, pharmacophore is utilized as a search query to filter the ligand database.…”

Section: Applications In Dockingmentioning

confidence: 99%

Pharmacophore Modeling in Drug Discovery: Methodology and Current Status

Muhammed

Akı-Yalçın

2021

Journal of the Turkish Chemical Society Section A: Chemistry

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A pharmacophore describes the framework of molecular features that are vital for the biological activity of a compound. Pharmacophore models are built by using the structural information about the active ligands or targets. The pharmacophore models developed are used to identify novel compounds that satisfy the pharmacophore requirements and thus expected to be biologically active. Drug discovery process is a challenging task that requires the contribution of multidisciplinary approaches. Pharmacophore modeling has been used in various stages of the drug discovery process. The major application areas are virtual screening, docking, drug target fishing, ligand profiling, and ADMET prediction. There are several pharmacophore modeling programs in use. The user must select the right program for the right purpose carefully. There are new developments in pharmacophore modeling with the involvement of the other computational methods. It has been integrated with molecular dynamics simulations. The latest computational approaches like machine learning have also played an important role in the advances achieved. Moreover, with the rapid advance in computing capacity, data storage, software and algorithms, more advances are anticipated. Pharmacophore modeling has contributed to a faster, cheaper, and more effective drug discovery process. With the integration of pharmacophore modeling with the other computational methods and advances in the latest algorithms, programs that have better perfomance are emerging. Thus, improvements in the quality of the phamacophore models generated have been achieved with this new developments.

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“…LibDock is a rapid and semi-flexible docking algorithm for molecular docking of a large number of compounds [20]. Diverse conformations of all compounds were generated by the BEST mode, and the maximum conformations were set to 255 with the energy threshold of 20.0 kcal/mol.…”

Section: Molecular Dockingmentioning

confidence: 99%

Combination Computing of Support Vector Machine, Support Vector Regression and Molecular Docking for Potential Cytochrome P450 1A2 Inhibitors

Chen

Qiao

Cai

et al. 2016

Chinese Journal of Chemical Physics

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The computational approaches of support vector machine (SVM), support vector regression (SVR) and molecular docking were widely utilized for the computation of active compounds. In this work, to improve the accuracy and reliability of prediction, the strategy of combining the above three computational approaches was applied to predict potential cytochrome P450 1A2 (CYP1A2) inhibitors. The accuracy of the optimal SVM qualitative model was 99.432%, 97.727%, and 91.667% for training set, internal test set and external test set, respectively, showing this model had high discrimination ability. The R 2 and mean square error for the optimal SVR quantitative model were 0.763, 0.013 for training set, and 0.753, 0.056 for test set respectively, indicating that this SVR model has high predictive ability for the biological activities of compounds. According to the results of the SVM and SVR models, some types of descriptors were identified to be essential to bioactivity prediction of compounds, including the connectivity indices, constitutional descriptors and functional group counts. Moreover, molecular docking studies were used to reveal the binding poses and binding affinity of potential inhibitors interacting with CYP1A2. Wherein, the amino acids of THR124 and ASP320 could form key hydrogen bond interactions with active compounds. And the amino acids of ALA317 and GLY316 could form strong hydrophobic bond interactions with active compounds. The models obtained above were applied to discover potential CYP1A2 inhibitors from natural products, which could predict the CYPs-mediated drug-drug interactions and provide useful guidance and reference for rational drug combination therapy. A set of 20 potential CYP1A2 inhibitors were obtained. Part of the results was consistent with references, which further indicates the accuracy of these models and the reliability of this combinatorial computation strategy.

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Pharmacophore and molecular docking based identification of novel structurally diverse PDE-5 inhibitors

Cited by 6 publications

References 30 publications

Pharmacophore-driven identification of N-methyl-D-receptor antagonists as potent neuroprotective agents validated using in vivo studies

Pharmacophore-driven identification of N-methyl-D-receptor antagonists as potent neuroprotective agents validated using in vivo studies

Pharmacophore Modeling in Drug Discovery: Methodology and Current Status

Combination Computing of Support Vector Machine, Support Vector Regression and Molecular Docking for Potential Cytochrome P450 1A2 Inhibitors

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