ArticleJournal of computer-aided molecular design2012
Ligand-, structure- and pharmacophore-based molecular fingerprints: a case study on adenosine A(1), A (2A), A (2B), and A (3) receptor antagonists.
Article in Journal of computer-aided molecular design, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deep Learning Pipeline for Accelerating Virtual Screening in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Pharmacophore-Based Discovery of Substrates of a Novel Drug/Proton-Antiporter in the Human Brain Endothelial hCMEC/D3 Cell Line.Pharmaceutics · 2022Article
- Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief Networks.Molecules (Basel, Switzerland) · 2020Article
- Comparative Study of Carborane- and Phenyl-Modified Adenosine Derivatives as Ligands for the A2A and A3 Adenosine Receptors Based on a Rigid in Silico Docking and Radioligand Replacement Assay.Molecules (Basel, Switzerland) · 2018Article
- Mapping ligand binding pockets in chloride ClC-1 channels through an integrated in silico and experimental approach using anthracene-9-carboxylic acid and niflumic acid.British journal of pharmacology · 2018Article
- In Silico Studies Targeting G-protein Coupled Receptors for Drug Research Against Parkinson's Disease.Current neuropharmacology · 2018Article
- Structure-Based Design of Potent and Selective Ligands at the Four Adenosine Receptors.Molecules (Basel, Switzerland) · 2017Article
- Human Aquaporin-4 and Molecular Modeling: Historical Perspective and View to the Future.International journal of molecular sciences · 2016Review
- A Rational Approach for the Identification of Non-Hydroxamate HDAC6-Selective Inhibitors.Scientific reports · 2016Article
- Pharmacophore-based discovery of inhibitors of a novel drug/proton antiporter in human brain endothelial hCMEC/D3 cell line.British journal of pharmacology · 2015Article
- Structure-based approaches to ligands for G-protein-coupled adenosine and P2Y receptors, from small molecules to nanoconjugates.Journal of medicinal chemistry · 2013Review
- Isozyme-specific ligands for O-acetylserine sulfhydrylase, a novel antibiotic target.PloS one · 2013Article
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6 authors.
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Abstract
FLAP fingerprints are applied in the ligand-, structure- and pharmacophore-based mode in a case study on antagonists of all four adenosine receptor (AR) subtypes. Structurally diverse antagonist collections with respect to the different ARs were constructed by including binding data to human species only. FLAP models well discriminate "active" (=highly potent) from "inactive" (=weakly potent) AR antagonists, as indicated by enrichment curves, numbers of false positives, and AUC values. For all FLAP modes, model predictivity slightly decreases as follows: A(2B)R > A(2A)R > A(3)R > A(1)R antagonists. General performance of FLAP modes in this study is: ligand- > structure- > pharmacophore- based mode. We also compared the FLAP performance with other common ligand- and structure-based fingerprints. Concerning the ligand-based mode, FLAP model performance is superior to ECFP4 and ROCS for all AR subtypes. Although focusing on the early first part of the A(2A), A(2B) and A(3) enrichment curves, ECFP4 and ROCS still retain a satisfactory retrieval of actives. FLAP is also superior when comparing the structure-based mode with PLANTS and GOLD. In this study we applied for the first time the novel FLAPPharm tool for pharmacophore generation. Pharmacophore hypotheses, generated with this tool, convincingly match with formerly published data. Finally, we could demonstrate the capability of FLAP models to uncover selectivity aspects although single AR subtype models were not trained for this purpose.
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