ArticleMolecules (Basel, Switzerland)2022
Combination of Docking-Based and Pharmacophore-Based Virtual Screening Identifies Novel Agonists That Target the Urotensin Receptor.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- High-throughput virtual screening, identification andJournal of enzyme inhibition and medicinal chemistry · 2025Article
- G Protein-Coupled Receptor-Ligand Pose and Functional Class Prediction.International journal of molecular sciences · 2024Article
- The identification of potent dual-target monopolar spindle 1 (MPS1) and histone deacetylase 8 (HDAC8) inhibitors through pharmacophore modeling, molecular docking, molecular dynamics simulations, and biological evaluation.Frontiers in pharmacology · 2024Article
- Arabinofuranosyl Thymine Derivatives-Potential Candidates against Cowpox Virus: A Computational Screening Study.International journal of molecular sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
The urotensin receptor (UT receptor), a G-protein-coupled receptor mediating urotensin-II and urotensin-II-related peptide signaling in the urotensinergic system, has multiple pharmacological activities. However, there is no drug targeting the UT receptor currently in clinical use, and the discovery of new leads is still important. The complete crystal structure of the UT receptor has not yet been resolved and a screening strategy combining multiple methods can improve the accuracy and efficiency of drug screening. This study aimed to identify novel UT receptor agonists using a combination of docking-based, pharmacophore-based, and cell-based drug screening. First, the three-dimensional structures of the UT receptor were constructed through single-template, multi-template homologous modeling and threading strategies. After structure evaluation and ligand enrichment analysis, a model from the threading modeling was selected for docking-based virtual screening based on stepwise filtering, and 1368 positive compounds were obtained from our compound library. Second, the pharmacophore models were constructed using known ligands targeting the UT receptor for pharmacophore-based virtual screening. A model was selected after model validation, and 300 positive compounds were retrieved. Then, after intersecting the results of two different virtual screening methods with 570 compound entities from our primary screening, 14 compounds were obtained. Finally, three hits were obtained after in vitro confirmation. Furthermore, preliminary evaluation of the hits showed that they influenced glucose consumption. In summary, by integrating docking-based, pharmacophore-based, and in vitro drug screening, three new agonists targeting the UT receptor were identified which may serve as promising therapeutic agents for urotensinergic system disorders.
Indexed as
Identifiers
What OpenQuestion holds
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.