The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters
Abstract
From the historically grown archive of protein-ligand complexes in the Protein Data Bank small organic ligands are extracted and interpreted in terms of their chemical characteristics and features. Subsequently, pharmacophores representing ligand-receptor interaction are derived from each of these small molecules and its surrounding amino acids. Based on a defined set of only six types of chemical features and volume constraints, three-dimensional pharmacophore models are constructed, which are sufficiently selective to identify the described binding mode and are thus a useful tool for in-silico screening of large compound databases. The algorithms for ligand extraction and interpretation as well as the pharmacophore creation technique from the automatically interpreted data are presented and applied to a rhinovirus capsid complex as application example.
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Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763
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Metadata
- Authors
- Wolber, G., & Langer, T.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2004-11-24
- Volume
- 45
- Issue
- 1
- Pages
- 160-169
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2208203352
- Cited by
- 1,954
- References
- 2
- FWCI
- 14.97
- Type
- journal article
- Language
- en
- Open access
- closed
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