Linked Life Data

18691055

Source:http://linkedlifedata.com/resource/pubmed/id/18691055

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
20
pubmed:dateCreated
2008-8-11
pubmed:abstractText
Computational chemistry software for lead discovery has become well established in pharmaceutical industry and has found its way to the desktop computers of medicinal chemists for different purposes, providing insight on the mode of action and binding properties, and creating new ideas for lead structure refinement. In this review we investigate the performance and reliability of recent state-of-the-art data modeling techniques, as well as ligand-based and structure-based modeling approaches for 3D virtual screening. We discuss and summarize recently published success stories and lately developed techniques. Parallel screening is one of these emerging approaches allowing for efficient activity in silico profiling of several compounds against different targets or anti-targets simultaneously. This is of special interest to medicinal chemists, as the approach allows revealing unknown binding modes ('target-fishing') as well as integrated ADME profiling or--more generally--the prediction of off-target effects.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0929-8673
pubmed:author
pubmed:issnType
Print
pubmed:volume
15
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2040-53
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Enhancing drug discovery through in silico screening: strategies to increase true positives retrieval rates.
pubmed:affiliation
Department of Pharmaceutical Chemistry, Faculty of Chemistry and Pharmacy, and Center for Molecular Biosciences, University of Innsbruck, Innrain 52, Innsbruck, Austria.
pubmed:publicationType
Journal Article, Review