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Central Nervous System Agents in Medicinal Chemistry


ISSN (Print): 1871-5249
ISSN (Online): 1875-6166

Research Article

In-Silico Screening of Ligand Based Pharmacophore, Database Mining and Molecular Docking on 2, 5-Diaminopyrimidines Azapurines as Potential Inhibitors of Glycogen Synthase Kinase-3β

Author(s): Pooja Mishra, Seema Kesar*, Sarvesh K. Paliwal, Monika Chauhan and Kirtika Madan

Volume 18 , Issue 2 , 2018

Page: [150 - 158] Pages: 9

DOI: 10.2174/1871524918666180530074116

Price: $65


Background: Glycogen synthase kinase-3β plays a significant role in the regulation of various pathological pathways relating to the Central Nervous System (CNS). Dysregulation of Glycogen synthase kinase 3 (GSK-3) activity gives rise to numerous neuroinflammation and neurodegenerative related disorders that affect the whole central nervous system.

Objective: By the sequential application of in-silico tools, efforts have been attempted to design the novel GSK-3β inhibitors.

Method: Owing to the potential role of GSK-3β in nervous disorders, we have attempted to develop the quantitative four featured pharmacophore model comprising two Hydrogen Bond Acceptors (HBA), one Ring Aromatic (RA), and one Hydrophobe (HY), which were further affirmed by costfunction analysis, rm2 matrices, internal and external test set validation and Guner-Henry (GH) scoring analysis. Validated pharmacophoric model was used for virtual screening and out of 345 compounds, two potential virtual hits were finalized that were on the basis of fit value, estimated activity and Lipinski’s violation. The chosen compounds were subjected to dock within the active site of GSK-3β.

Result: Four essential features, i.e., two Hydrogen Bond Acceptors (HBA), one Ring Aromatic (RA), and one Hydrophobe (HY), were subjected to build the pharmacophoric model and showed good correlation coefficient, RMSD and cost difference values of 0.91, 0.94 and 42.9 respectively and further model was validated employing cost-function analysis, rm2-matrices, internal and external test set prediction with r2 value of 0.77 and 0.84. Docked conformations showed potential interactions in between the features of the identified hits (NCI 4296, NCI 3034) and the amino acids present in the active site.

Conclusion: In line with the overhead discussion, and through our stepwise computational approaches, we have identified novel, structurally diverse glycogen synthase kinase inhibitors.

Keywords: In silico based pharmacophore modelling, molecular docking, tanimoto similarity indices, GSK-3, CNS, HBA.

Graphical Abstract

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