ReviewCurrent topics in medicinal chemistry2012
Compound activity prediction using models of binding pockets or ligand properties in 3D.
Review in Current topics in medicinal chemistry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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
16 citing papers in PubMed, 40 citations in OpenAlex.
- A Quantum-Classical Analysis of Variability in Peptide Energies and Conformations.The protein journal · 2026Article
- Structure-based virtual screening aids the identification of glycosyltransferases in the biosynthesis of salidroside.Plant biotechnology journal · 2025Article
- Structure-based discovery of conformationally selective inhibitors of the serotonin transporter.Cell · 2023Article
- Allosteric modulators of solute carrier function: a theoretical framework.Frontiers in physiology · 2023Article
- Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.Briefings in bioinformatics · 2021Review
- A two-stage computational approach to predict novel ligands for a chemosensory receptor.Current research in structural biology · 2020Article
- Nilotinib, an approved leukemia drug, inhibits smoothened signaling in Hedgehog-dependent medulloblastoma.PloS one · 2019Article
- Structure-based predictions of activity cliffs.Journal of chemical information and modeling · 2015Article
- Structural biology. Crystal structure of the chemokine receptor CXCR4 in complex with a viral chemokine.Science (New York, N.Y.) · 2015Article
- Experiment-Guided Molecular Modeling of Protein-Protein Complexes Involving GPCRs.Methods in molecular biology (Clifton, N.J.) · 2015Article
- Stoichiometry and geometry of the CXC chemokine receptor 4 complex with CXC ligand 12: molecular modeling and experimental validation.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- In silico identification and pharmacological evaluation of novel endocrine disrupting chemicals that act via the ligand-binding domain of the estrogen receptor α.Toxicological sciences : an official journal of the Society of Toxicology · 2014Article
- Leveraging structure for enzyme function prediction: methods, opportunities, and challenges.Trends in biochemical sciences · 2014Review
- Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology.Molecular informatics · 2014Review
- Docking to multiple pockets or ligand fields for screening, activity prediction and scaffold hopping.Future medicinal chemistry · 2014Article
- Quantum Mechanics Approaches to Drug Research in the Era of Structural Chemogenomics.International journal of quantum chemistry · 2013Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Transient interactions of endogenous and exogenous small molecules with flexible binding sites in proteins or macromolecular assemblies play a critical role in all biological processes. Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions with increasing accuracy and specificity. Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocketbased and ligand property-based, for two important classes of proteins, nuclear and G-protein coupled receptors. For half the targets, the pocket models discriminate actives from property matched decoys with acceptable accuracy (the area under ROC curve, AUC, exceeding 84%) and for about one fifth of the targets with high accuracy (AUC > 95%). The 3D ligand property field models performed better than 95% in half of the cases. The high performance models can already become a basis of activity predictions for new chemicals. Family-wide benchmarking of the models highlights strengths of both approaches and helps identify their inherent bottlenecks and challenges.
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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.