This HTML5 document contains 45 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n14http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n20http://localhost/temp/predkladatel/
n19http://purl.org/net/nknouf/ns/bibtex#
n10http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n7http://linked.opendata.cz/resource/domain/vavai/projekt/
n15http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F00216208%3A11320%2F14%3A10276035%21RIV15-MSM-11320___/
n9http://linked.opendata.cz/ontology/domain/vavai/
n4https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n17http://bibframe.org/vocab/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n6http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n5http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n21http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n16http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n22http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n13http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F14%3A10276035%21RIV15-MSM-11320___
rdf:type
n9:Vysledek skos:Concept
dcterms:description
Using pharmacophores in virtual screening of large chemical compound libraries proved to be a valuable concept in computer-aided drug design. Traditionally, pharmacophore-based screening is performed in 3D space where crystallized or predicted structures of ligands are superposed and where pharmacophore features are identified and compiled into a 3D pharmacophore model. However, in many cases the structures of the ligands are not known which results in using a 2D pharmacophore model. We introduce a method capable of automatic generation of 2D pharmacophore models given previous knowledge about the biological target of interest. The knowledge comprises of a set of known active and inactive molecules with respect to the target. From the set of active and inactive molecules 2D pharmacophore features are extracted using pharmacophore fingerprints. Then a statistical procedure is applied to identify features separating the active from the inactive molecules and these features are then used to build a pharmacophore model. Finally, a similarity measure utilizing the model is applied for virtual screening. The method was tested on multiple state of the art datasets and compared to several virtual screening methods. Our approach seems to exceed the existing methods in most cases. We believe that the presented methodology forms a valuable addition to the set of tools available for the early stage drug discovery process. Using pharmacophores in virtual screening of large chemical compound libraries proved to be a valuable concept in computer-aided drug design. Traditionally, pharmacophore-based screening is performed in 3D space where crystallized or predicted structures of ligands are superposed and where pharmacophore features are identified and compiled into a 3D pharmacophore model. However, in many cases the structures of the ligands are not known which results in using a 2D pharmacophore model. We introduce a method capable of automatic generation of 2D pharmacophore models given previous knowledge about the biological target of interest. The knowledge comprises of a set of known active and inactive molecules with respect to the target. From the set of active and inactive molecules 2D pharmacophore features are extracted using pharmacophore fingerprints. Then a statistical procedure is applied to identify features separating the active from the inactive molecules and these features are then used to build a pharmacophore model. Finally, a similarity measure utilizing the model is applied for virtual screening. The method was tested on multiple state of the art datasets and compared to several virtual screening methods. Our approach seems to exceed the existing methods in most cases. We believe that the presented methodology forms a valuable addition to the set of tools available for the early stage drug discovery process.
dcterms:title
2D Pharmacophore Query Generation 2D Pharmacophore Query Generation
skos:prefLabel
2D Pharmacophore Query Generation 2D Pharmacophore Query Generation
skos:notation
RIV/00216208:11320/14:10276035!RIV15-MSM-11320___
n3:aktivita
n21:S n21:P
n3:aktivity
P(GAP202/11/0968), P(GP14-29032P), S
n3:dodaniDat
n13:2015
n3:domaciTvurceVysledku
n10:9318585 n10:9268421
n3:druhVysledku
n18:D
n3:duvernostUdaju
n5:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
58156
n3:idVysledku
RIV/00216208:11320/14:10276035
n3:jazykVysledku
n16:eng
n3:klicovaSlova
virtual screening; pharmacophore modeling; 2D pharmacophores
n3:klicoveSlovo
n6:pharmacophore%20modeling n6:virtual%20screening n6:2D%20pharmacophores
n3:kontrolniKodProRIV
[452EEB619B2A]
n3:mistoKonaniAkce
Zhangjiajie, China
n3:mistoVydani
Switzerland
n3:nazevZdroje
Lecture Notes in Computer Science
n3:obor
n22:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n7:GAP202%2F11%2F0968 n7:GP14-29032P
n3:rokUplatneniVysledku
n13:2014
n3:tvurceVysledku
Hoksza, David Škoda, Petr
n3:typAkce
n14:WRD
n3:zahajeniAkce
2014-06-28+02:00
s:issn
0302-9743
s:numberOfPages
11
n17:doi
10.1007/978-3-319-08171-7_26
n19:hasPublisher
Springer International Publishing
n4:isbn
978-3-319-08170-0
n20:organizacniJednotka
11320