ArticleJournal of computational chemistry2010
Ligand and structure-based models for the prediction of ligand-receptor affinities and virtual screenings: Development and application to the beta(2)-adrenergic receptor.
Article in Journal of computational chemistry, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Decoys Selection in Benchmarking Datasets: Overview and Perspectives.Frontiers in pharmacology · 2018Review
- How Open Data Shapes In Silico Transporter Modeling.Molecules (Basel, Switzerland) · 2017Review
- CombinatorialFrontiers in molecular neuroscience · 2017Article
- Structural insights and functional implications of inter-individual variability in β2-adrenergic receptor.Scientific reports · 2016Article
- Discovery of GPCR ligands for probing signal transduction pathways.Frontiers in pharmacology · 2014Review
- Development of 7TM receptor-ligand complex models using ligand-biased, semi-empirical helix-bundle repacking in torsion space: application to the agonist interaction of the human dopamine D2 receptor.Journal of computer-aided molecular design · 2013Article
- Graph analysis of β2 adrenergic receptor structures: a "social network" of GPCR residues.In silico pharmacology · 2013Article
- New insights for drug design from the X-ray crystallographic structures of G-protein-coupled receptors.Molecular pharmacology · 2012Review
- In silico screening for agonists and blockers of the β(2) adrenergic receptor: implications of inactive and activated state structures.Journal of computational chemistry · 2012Article
- Homology modeling of class a G protein-coupled receptors.Methods in molecular biology (Clifton, N.J.) · 2012Article
- Predicting the biological activities through QSAR analysis and docking-based scoring.Methods in molecular biology (Clifton, N.J.) · 2012Article
- In silico identification of new ligands for GPR17: a promising therapeutic target for neurodegenerative diseases.Journal of computer-aided molecular design · 2011Article
- In silico analysis of the binding of agonists and blockers to the β2-adrenergic receptor.Journal of molecular graphics & modelling · 2011Article
- Docking-based virtual screening for ligands of G protein-coupled receptors: not only crystal structures but also in silico models.Journal of molecular graphics & modelling · 2011Article
- Predicting novel binding modes of agonists to β adrenergic receptors using all-atom molecular dynamics simulations.PLoS computational biology · 2011Article
- Conserved binding mode of human beta2 adrenergic receptor inverse agonists and antagonist revealed by X-ray crystallography.Journal of the American Chemical Society · 2010Article
- Structural basis of the selectivity of the beta(2)-adrenergic receptor for fluorinated catecholamines.Bioorganic & medicinal chemistry · 2009Article
- Comparative sequence and structural analyses of G-protein-coupled receptor crystal structures and implications for molecular models.PloS one · 2009Article
- Drug-drug interaction through molecular structure similarity analysis.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
3 authors.
Funding
Abstract
In this study, we evaluated the applicability of ligand-based and structure-based models to quantitative affinity predictions and virtual screenings for ligands of the beta(2)-adrenergic receptor, a G protein-coupled receptor (GPCR). We also devised and evaluated a number of consensus models obtained through partial least square regressions, to combine the strengths of the individual components. In all cases, the bioactive conformation of each ligand was derived from molecular docking at the crystal structure of the receptor. We identified the most effective models applicable to the different scenarios, in the presence or in the absence of a training set. For ranking the affinity of closely related analogs when a training set is available, a ligand-based consensus model (LI-CM) seems to be the best choice, while the structure-based MM-GBSA score seems the best alternative in the absence of a training set. For virtual screening purposes, the structure-based MM-GBSA score was found to be the method of choice. Consensus models consistently had performances superior or close to those of the best individual components, and were endowed with a significantly increased robustness. Given multiple models with no a priori knowledge of their predictive capabilities, constructing a consensus model ensures results very close to those that the best model alone would have yielded.
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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.