Binding site detection and druggability prediction of protein targets for structure-based drug design.
By Yaxia Yuan, Jianfeng Pei, Luhua Lai. 2013
172 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Molecular Biology
In Pharmacology
Citations in the corpus
- PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms (2019, unknown): “Till now, various approaches have been developed for the identi-fication of protein LBS, which are comprehensively reviewed and summarized in [33, 48, 40, 32, 31, 15, 29, 9, 39, 4, 34, 46].”
- PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database (2017, methodology): “CAVITY is a newer method, which adopts a structural geometry-based ligand-binding sites detection and analysis strategy with the capability of predicting both the druggabilities and ligandabilities of the detected binding sites (14,15).”
- CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome (2022, methodology): “We applied the CAVITY tool developed by our lab to detect all the potential cavities on protein surfaces [30].”
- CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction (2018, methodology): “Four original toolkits––CAVITY (9), CavPharmer (10,11), CorrSite (12) and CovCys (13)––are integrated in CavityPlus to provide online identification of cavities on protein surfaces and, for a given cavity, pharmacophore modelling, allosteric site identification, and covalent ligand-binding ability p”