[HTML][HTML] Computational studies on imidazo [1, 2-a] pyridine-3-carboxamide analogues as antimycobacterial agents: Common pharmacophore generation, atom-based …

SN Mali, HK Chaudhari - Open …, 2018 - openpharmaceuticalsciencesjournal …
Open Pharmaceutical Sciences Journal, 2018openpharmaceuticalsciencesjournal …
Background: IMB-1402, Q203 and ND09759 analogs were found to have strong efficiency
against Multi-drug-resistant tuberculosis (MDR-TB)/Extensively drug-resistant tuberculosis
(XDR-TB) strains. Objectives: To know the structural necessities for imidazo [1, 2-a] pyridine-
3-carboxamide analogues, we intended to develop the ligand-based pharmacophore,
Quantitative structure–activity relationship models (3D-QSAR model). We also performed
Molecular docking, molecular simulation and Prime/Molecular Mechanics Generalized Born …
Abstract
Background:
IMB-1402, Q203 and ND09759 analogs were found to have strong efficiency against Multi-drug-resistant tuberculosis (MDR-TB)/Extensively drug-resistant tuberculosis (XDR-TB) strains.
Objectives:
To know the structural necessities for imidazo [1, 2-a] pyridine-3-carboxamide analogues, we intended to develop the ligand-based pharmacophore, Quantitative structure–activity relationship models (3D-QSAR model). We also performed Molecular docking, molecular simulation and Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM-GBSA) studies.
Methods:
All the studies like Common pharmacophore hypothesis generation, Atom based 3D-QSAR study, Prime MMGBSA, Docking, Qikprop, and Molecular dynamics simulation were processed using various modules incorporated within the maestro software interface from Schrodinger, LLC, New York USA (release 2017).
Results:
The common pharmacophore hypothesis (CPH) generation resulted in a five-featured hypothesis HHPRR, containing 1 positive, 2 hydrophobic and 2 aromatic rings. An Atom-based 3D-QSAR model was predicted for twenty seven training sets (a correlation coefficient ie R 2= 0.9181, Standard deviation ie SD= 0.3305, variance ratio ie F= 85.9) and eleven test sets (cross-validation correlation coefficient ie Q 2= 0.6745, Root Mean Square Error ie RMSE= 0.65, Pearson R= 0.8427, P= 1.21 E-12) compounds employing alignment based on CPH. The dataset of thirty-eight molecules was allowed for docking into the active site of pantothenate synthetase (PDBID-3IVX) that shows H-bonding (Hydrogen bonding) interactions with residues Gly158, Met195, Pro38 and additionally shows further Pi-cation interactions with a residue like Hie47. We also obtained good simulation results for1. 2ns study.
Conclusion:
From the results, the generated 3D-QSAR model may be applicable for additional designing of various novel potent derivatives in the future.
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