ArticleScientific reports2024
Identification of DprE1 inhibitors for tuberculosis through integrated in-silico approaches.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Development and validation of a LASSO-derived nomogram for predicting unfavorable treatment outcomes in drug-resistant pulmonary tuberculosis.BMC infectious diseases · 2026Article
- Molecular docking analysis of DprE1 fromBioinformation · 2026Article
- Discovery of potent DprE1-targeted antitubercular agents: synthesis and evaluation of PBTZ169/TBA7371-based derivatives.Molecular diversity · 2025Article
- Scaffold Hopping in Tuberculosis Drug Discovery: Principles, Applications, and Case Studies.Journal of medicinal chemistry · 2025Review
- Design, Synthesis,ACS omega · 2025Article
- Synthesis, molecular docking, molecular dynamic simulation and biological evaluation of novel 3,4-dihydropyridine derivatives as potent antituberculosis agents.Molecular diversity · 2025Article
- Identifying RAGE inhibitors as potential therapeutics for Alzheimer's disease via integrated in-silico approaches.Scientific reports · 2025Article
- Pharmaceutical Salts: Comprehensive Insights From Fundamental Chemistry to FDA Approvals (2019-2023).AAPS PharmSciTech · 2025Review
- Mechanistic Insights into the Anticancer Potential of Methoxyflavones Analogs: A Review.Molecules (Basel, Switzerland) · 2025Review
- Activity of combinations of bactericidal and bacteriostatic compounds inFrontiers in microbiology · 2025Review
- Bibliometric and Visualization Analysis of DprE1 Inhibitors to Combat Tuberculosis.Drug design, development and therapy · 2025Article
- Article
- Exploring the Chemical Space of Mycobacterial Oxidative Phosphorylation Inhibitors Using Molecular Modeling.ChemMedChem · 2024Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Decaprenylphosphoryl-β-D-ribose-2'-epimerase (DprE1), a crucial enzyme in the process of arabinogalactan and lipoarabinomannan biosynthesis, has become the target of choice for anti-TB drug discovery in the recent past. The current study aims to find the potential DprE1 inhibitors through in-silico approaches. Here, we built the pharmacophore and 3D-QSAR model using the reported 40 azaindole derivatives of DprE1 inhibitors. The best pharmacophore hypothesis (ADRRR_1) was employed for the virtual screening of the chEMBL database. To identify prospective hits, molecules with good phase scores (> 2.000) were further evaluated by molecular docking studies for their ability to bind to the DprE1 enzyme (PDB: 4KW5). Based on their binding affinities (< - 9.0 kcal/mole), the best hits were subjected to the calculation of free-binding energies (Prime/MM-GBSA), pharmacokinetic, and druglikeness evaluations. The top 10 hits retrieved from these results were selected to predict their inhibitory activities via the developed 3D-QSAR model with a regression coefficient (R
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