ArticleJournal of molecular modeling2012
3D-QSAR based pharmacophore modeling and virtual screening for identification of novel pteridine reductase inhibitors.
Article in Journal of molecular modeling, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 33 citations in OpenAlex.
- Synthesis and Biological Evaluation of Imidazopyridine-Isatin Hybrids as Inhibitors ofDrug design, development and therapy · 2026Article
- Identification of Potential Pteridin Reductase-1 Inhibitors for the Treatment of Leishmaniasis: A Bioinformatics Approach.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Lead generation of UPPS inhibitors targeting MRSA: Using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamic simulations.BMC chemistry · 2024Article
- Screening and Activity Evaluation of Novel BCR-ABL/T315I Tyrosine Kinase Inhibitors.Current medicinal chemistry · 2024Article
- Spiro heterocycles bearing piperidine moiety as potential scaffold for antileishmanial activity: synthesis, biological evaluation, andJournal of enzyme inhibition and medicinal chemistry · 2023Article
- New pyrazolylpyrazoline derivatives as dual acting antimalarial-antileishamanial agents: synthesis, biological evaluation and molecular modelling simulations.Journal of enzyme inhibition and medicinal chemistry · 2022Article
- Identification of Influenza PAMolecules (Basel, Switzerland) · 2021Article
- The Discovery of Novel BCR-ABL Tyrosine Kinase Inhibitors Using a Pharmacophore Modeling and Virtual Screening Approach.Frontiers in cell and developmental biology · 2021Article
- Pharmacophore-Based Virtual Screening Toward the Discovery of Novel Anti-echinococcal Compounds.Frontiers in cellular and infection microbiology · 2020Article
- Discovery of Novel DPP-IV Inhibitors as Potential Candidates for the Treatment of Type 2Molecules (Basel, Switzerland) · 2019Article
- Ligand-based Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Discovery of Potential Topoisomerase I Inhibitors.Computational and structural biotechnology journal · 2019Article
- Probing the opportunities for designing anthelmintic leads by sub-structural topology-based QSAR modelling.Molecular diversity · 2018Article
- In silico design of anti-atherogenic biomaterials.Biomaterials · 2013Article
- Pharmacophore-based virtual screening and biological evaluation of small molecule inhibitors for protein arginine methylation.Journal of medicinal chemistry · 2012Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pteridine reductase is a promising target for development of novel therapeutic agents against Trypanosomatid parasites. A 3D-QSAR pharmacophore hypothesis has been generated for a series of L. major pteridine reductase inhibitors using Catalyst/HypoGen algorithm for identification of the chemical features that are responsible for the inhibitory activity. Four pharmacophore features, namely: two H-bond donors (D), one Hydrophobic aromatic (H) and one Ring aromatic (R) have been identified as key features involved in inhibitor-PTR1 interaction. These features are able to predict the activity of external test set of pteridine reductase inhibitors with a correlation coefficient (r) of 0.80. Based on the analysis of the best hypotheses, some potent Pteridine reductase inhibitors were screened out and predicted with anti-PTR1 activity. It turned out that the newly identified inhibitory molecules are at least 300 fold more potent than the current crop of existing inhibitors. Overall the current SAR study is an effort for elucidating quantitative structure-activity relationship for the PTR1 inhibitors. The results from the combined 3D-QSAR modeling and molecular docking approach have led to the prediction of new potent inhibitory scaffolds.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.