Publikation:

From machine learning to natural product derivatives that selectively activate transcription factor PPARgamma

Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2010

Autor:innen

Schroeter, Timon
Steri, Ramona
Zettl, Heiko
Proschak, Ewgenij
Hansen, Katja
Rau, Oliver
Schwarz, Oliver
Müller-Kuhrt, Lutz
Schneider, Gisbert
et al.

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
DOI (zitierfähiger Link)
https://doi.org/10.1002/cmdc.200900469
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung

Sammlungen

Informatik und Informationswissenschaft: Publikationen
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

ChemMedChem. Wiley. 2010, 5(2), pp. 191-194. ISSN 0960-894X. eISSN 1860-7187. Available under: doi: 10.1002/cmdc.200900469

Zusammenfassung

Advanced kernel‐based machine learning methods enable the identification of innovative bioactive compounds with minimal experimental effort. Comparative virtual screening revealed that nonlinear models of the underlying structure–activity relationship are necessary for successful compound picking. In a proof‐of‐concept study a novel truxillic acid derivative was found to selectively activate transcription factor PPARγ.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

drug design, machine learning, natural products, NMR, virtual screening

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690RUPP, Matthias, Timon SCHROETER, Ramona STERI, Heiko ZETTL, Ewgenij PROSCHAK, Katja HANSEN, Oliver RAU, Oliver SCHWARZ, Lutz MÜLLER-KUHRT, Gisbert SCHNEIDER, 2010. From machine learning to natural product derivatives that selectively activate transcription factor PPARgamma. In: ChemMedChem. Wiley. 2010, 5(2), pp. 191-194. ISSN 0960-894X. eISSN 1860-7187. Available under: doi: 10.1002/cmdc.200900469
BibTex
@article{Rupp2010-02-01machi-53197,
  year={2010},
  doi={10.1002/cmdc.200900469},
  title={From machine learning to natural product derivatives that selectively activate transcription factor PPARgamma},
  number={2},
  volume={5},
  issn={0960-894X},
  journal={ChemMedChem},
  pages={191--194},
  author={Rupp, Matthias and Schroeter, Timon and Steri, Ramona and Zettl, Heiko and Proschak, Ewgenij and Hansen, Katja and Rau, Oliver and Schwarz, Oliver and Müller-Kuhrt, Lutz and Schneider, Gisbert}
}
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/123456789/53197">
    <dc:contributor>Schneider, Gisbert</dc:contributor>
    <dc:contributor>Zettl, Heiko</dc:contributor>
    <dc:contributor>Schwarz, Oliver</dc:contributor>
    <dc:creator>Schroeter, Timon</dc:creator>
    <dcterms:issued>2010-02-01</dcterms:issued>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Rau, Oliver</dc:creator>
    <dc:contributor>Schroeter, Timon</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:language>eng</dc:language>
    <dc:creator>Zettl, Heiko</dc:creator>
    <dc:creator>Steri, Ramona</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-03-19T08:27:25Z</dcterms:available>
    <dc:contributor>Hansen, Katja</dc:contributor>
    <dcterms:title>From machine learning to natural product derivatives that selectively activate transcription factor PPARgamma</dcterms:title>
    <dc:contributor>Proschak, Ewgenij</dc:contributor>
    <dc:creator>Schwarz, Oliver</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Rupp, Matthias</dc:creator>
    <dc:contributor>Rupp, Matthias</dc:contributor>
    <dc:contributor>Rau, Oliver</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-03-19T08:27:25Z</dc:date>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:abstract xml:lang="eng">Advanced kernel‐based machine learning methods enable the identification of innovative bioactive compounds with minimal experimental effort. Comparative virtual screening revealed that nonlinear models of the underlying structure–activity relationship are necessary for successful compound picking. In a proof‐of‐concept study a novel truxillic acid derivative was found to selectively activate transcription factor PPARγ.</dcterms:abstract>
    <dc:creator>Schneider, Gisbert</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/53197"/>
    <dc:creator>Müller-Kuhrt, Lutz</dc:creator>
    <dc:creator>Hansen, Katja</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Müller-Kuhrt, Lutz</dc:contributor>
    <dc:creator>Proschak, Ewgenij</dc:creator>
    <dc:contributor>Steri, Ramona</dc:contributor>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Ja
Diese Publikation teilen