ArticleJournal of chemical information and modeling2011
CrystalDock: a novel approach to fragment-based drug design.
Article in Journal of chemical information and modeling, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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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.
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Who cites it
7 citing papers in PubMed.
- CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities.Journal of computer-aided molecular design · 2021Article
- Multiple Target Drug Design Using LigBuilder 3.Methods in molecular biology (Clifton, N.J.) · 2021Article
- LigBuilder V3: A Multi-TargetFrontiers in chemistry · 2020Article
- Scoria: a Python module for manipulating 3D molecular data.Journal of cheminformatics · 2017Article
- Considerations of Protein Subpockets in Fragment-Based Drug Design.Chemical biology & drug design · 2016Article
- POVME 2.0: An Enhanced Tool for Determining Pocket Shape and Volume Characteristics.Journal of chemical theory and computation · 2014Article
- A reverse combination of structure-based and ligand-based strategies for virtual screening.Journal of computer-aided molecular design · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We present a novel algorithm called CrystalDock that analyzes a molecular pocket of interest and identifies potential binding fragments. The program first identifies groups of pocket-lining receptor residues (i.e., microenvironments) and then searches for geometrically similar microenvironments present in publically available databases of ligand-bound experimental structures. Germane fragments from the crystallographic or NMR ligands are subsequently placed within the novel binding pocket. These positioned fragments can be linked together to produce ligands that are likely to be potent; alternatively, they can be joined to an inhibitor with a known or suspected binding pose to potentially improve binding affinity. To demonstrate the utility of the algorithm, CrystalDock is used to analyze the principal binding pockets of influenza neuraminidase and Trypanosoma brucei RNA editing ligase 1, validated drug targets in the fight against pandemic influenza and African sleeping sickness, respectively. In both cases, CrystalDock suggests modifications to known inhibitors that may improve binding affinity.
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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.