Linked Life Data

18570371

Source:http://linkedlifedata.com/resource/pubmed/id/18570371

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2008-7-29
pubmed:abstractText
The design of biologically active compounds from ligand-free protein structures using a structure-based approach is still a major challenge. In this paper, we present a fast knowledge-based approach (HS-Pharm) that allows the prioritization of cavity atoms that should be targeted for ligand binding, by training machine learning algorithms with atom-based fingerprints of known ligand-binding pockets. The knowledge of hot spots for ligand binding is here used for focusing structure-based pharmacophore models. Three targets of pharmacological interest (neuraminidase, beta2 adrenergic receptor, and cyclooxygenase-2) were used to test the evaluated methodology, and the derived structure-based pharmacophores were used in retrospective virtual screening studies. The current study shows that structure-based pharmacophore screening is a powerful technique for the fast identification of potential hits in a chemical library, and that it is a valid alternative to virtual screening by molecular docking.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1549-9596
pubmed:author
pubmed:issnType
Print
pubmed:volume
48
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1396-410
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores.
pubmed:affiliation
Bioinformatics of the Drug, UMR 7175 CNRS-ULP (Universite Louis Pasteur-Strasbourg I), 74 route du Rhin, B.P. 24, F-67400 Illkirch, France.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Validation Studies