Publikation: Large scale mining of molecular fragments with wildcards
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
The main task of drug discovery is to find novel bioactive molecules, i.e., chemical compounds that, for example, protect human cells against a virus. One way to support solving this task is to analyze a database of known and tested molecules with the aim to build a classifier that predicts whether a novel molecule will be active or inactive, so that future chemical tests can be focused on the most promising candidates. In [1] an algorithm for constructing such a classifier was proposed that uses molecular fragments to discriminate between active and inactive molecules. In this paper we present two extensions of this approach: A special treatment of rings and a method that finds fragments with wildcards based on chemical expert knowledge.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
HOFER, Heiko, Christian BORGELT, Michael R. BERTHOLD, 2004. Large scale mining of molecular fragments with wildcards. In: Intelligent Data Analysis. 2004, 8(5), pp. 495-504. ISSN 1088-467X. eISSN 1571-4128BibTex
@article{Hofer2004Large-5517, year={2004}, title={Large scale mining of molecular fragments with wildcards}, number={5}, volume={8}, issn={1088-467X}, journal={Intelligent Data Analysis}, pages={495--504}, author={Hofer, Heiko and Borgelt, Christian and Berthold, Michael R.} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/5517"> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/2.0/"/> <dc:creator>Berthold, Michael R.</dc:creator> <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights> <dcterms:issued>2004</dcterms:issued> <dc:creator>Borgelt, Christian</dc:creator> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5517/1/Large_scale_mining_of_molecular_fragments_with_wildcards.pdf"/> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5517/1/Large_scale_mining_of_molecular_fragments_with_wildcards.pdf"/> <dc:language>deu</dc:language> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:contributor>Berthold, Michael R.</dc:contributor> <dc:format>application/pdf</dc:format> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:56:10Z</dc:date> <dc:contributor>Borgelt, Christian</dc:contributor> <dcterms:abstract xml:lang="eng">The main task of drug discovery is to find novel bioactive molecules, i.e., chemical compounds that, for example, protect human cells against a virus. One way to support solving this task is to analyze a database of known and tested molecules with the aim to build a classifier that predicts whether a novel molecule will be active or inactive, so that future chemical tests can be focused on the most promising candidates. In [1] an algorithm for constructing such a classifier was proposed that uses molecular fragments to discriminate between active and inactive molecules. In this paper we present two extensions of this approach: A special treatment of rings and a method that finds fragments with wildcards based on chemical expert knowledge.</dcterms:abstract> <dc:creator>Hofer, Heiko</dc:creator> <dcterms:title>Large scale mining of molecular fragments with wildcards</dcterms:title> <dc:contributor>Hofer, Heiko</dc:contributor> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5517"/> <dcterms:bibliographicCitation>First publ. in: Intelligent Data Analysis 8 (2004), 5, pp. 495-504</dcterms:bibliographicCitation> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:56:10Z</dcterms:available> </rdf:Description> </rdf:RDF>