Publikation:

Learned Feature Generation for Molecules

Lade...
Vorschaubild

Dateien

Winter_2-m7ofa8jpw2sa6.pdf
Winter_2-m7ofa8jpw2sa6.pdfGröße: 365.15 KBDownloads: 364

Datum

2018

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

DUIVESTEIJN, Wouter, ed., Arno SIEBES, ed., Antti UKKONEN, ed.. Advances in Intelligent Data Analysis XVII : 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24-26, 2018, proceedings. Cham: Springer, 2018, pp. 380-391. Lecture Notes in Computer Science. 11191. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-030-01767-5. Available under: doi: 10.1007/978-3-030-01768-2_31

Zusammenfassung

When classifying molecules for virtual screening, the molecular structure first needs to be converted into meaningful features, before a classifier can be trained. The most common methods use a static algorithm that has been created based on domain knowledge to perform this generation of features. We propose an approach where this conversion is learned by convolutional neural network finding features that are useful for teh task at hand based on the available data. Preliminary results indicate that our current approach can already come up with fetaures that perform similarly well as common methods. Since this approach does not jet use any chemiocal properties, results could be improved in future versions

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Convolutional neural networks; Feature generation; Molecular fetaures; Virtual screening

Konferenz

17th International Symposium, IDA 2018, 24. Okt. 2018 - 26. Okt. 2018, ’s-Hertogenbosch, The Netherlands
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690WINTER, Patrick, Christian BORGELT, Michael R. BERTHOLD, 2018. Learned Feature Generation for Molecules. 17th International Symposium, IDA 2018. ’s-Hertogenbosch, The Netherlands, 24. Okt. 2018 - 26. Okt. 2018. In: DUIVESTEIJN, Wouter, ed., Arno SIEBES, ed., Antti UKKONEN, ed.. Advances in Intelligent Data Analysis XVII : 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24-26, 2018, proceedings. Cham: Springer, 2018, pp. 380-391. Lecture Notes in Computer Science. 11191. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-030-01767-5. Available under: doi: 10.1007/978-3-030-01768-2_31
BibTex
@inproceedings{Winter2018-10-05Learn-44691,
  year={2018},
  doi={10.1007/978-3-030-01768-2_31},
  title={Learned Feature Generation for Molecules},
  number={11191},
  isbn={978-3-030-01767-5},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Computer Science},
  booktitle={Advances in Intelligent Data Analysis XVII : 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24-26, 2018, proceedings},
  pages={380--391},
  editor={Duivesteijn, Wouter and Siebes, Arno and Ukkonen, Antti},
  author={Winter, Patrick 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/123456789/44691">
    <dc:creator>Berthold, Michael R.</dc:creator>
    <dcterms:abstract xml:lang="eng">When classifying molecules for virtual screening, the molecular structure first needs to be converted into meaningful features, before a classifier can be trained. The most common methods use a static algorithm that has been created based on domain knowledge to perform this generation of features. We propose an approach where this conversion is learned by convolutional neural network finding features that are useful for teh task at hand based on the available data. Preliminary results indicate that our current approach can already come up with fetaures that perform similarly well as common methods. Since this approach does not jet use any chemiocal properties, results could be improved in future versions</dcterms:abstract>
    <dc:language>eng</dc:language>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Borgelt, Christian</dc:contributor>
    <dc:contributor>Winter, Patrick</dc:contributor>
    <dc:rights>terms-of-use</dc:rights>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-01-23T13:29:47Z</dcterms:available>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/44691"/>
    <dc:contributor>Berthold, Michael R.</dc:contributor>
    <dc:creator>Borgelt, Christian</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/44691/1/Winter_2-m7ofa8jpw2sa6.pdf"/>
    <dcterms:issued>2018-10-05</dcterms:issued>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-01-23T13:29:47Z</dc:date>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/44691/1/Winter_2-m7ofa8jpw2sa6.pdf"/>
    <dcterms:title>Learned Feature Generation for Molecules</dcterms:title>
    <dc:creator>Winter, Patrick</dc:creator>
  </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
Ja
Begutachtet
Diese Publikation teilen