Distance phenomena in high-dimensional chemical descriptor spaces : Consequences for similarity-based approaches

Rupp, Matthias; Schneider, Petra; Schneider, Gisbert

Distance phenomena in high-dimensional chemical descriptor spaces : Consequences for similarity-based approaches

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2009

Autor:innen

Rupp, Matthias

Schneider, Petra

Schneider, Gisbert

DOI (zitierfähiger Link)

https://doi.org/10.1002/jcc.21218

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Journal of Computational Chemistry. Wiley-Blackwell. 2009, 30(14), pp. 2285-2296. ISSN 0192-8651. eISSN 1096-987X. Available under: doi: 10.1002/jcc.21218

Zusammenfassung

Measuring the (dis)similarity of molecules is important for many cheminformatics applications like compound ranking, clustering, and property prediction. In this work, we focus on real-valued vector representations of molecules (as opposed to the binary spaces of fingerprints). We demonstrate the influence which the choice of (dis)similarity measure can have on results, and provide recommendations for such choices. We review the mathematical concepts used to measure (dis)similarity in vector spaces, namely norms, metrics, inner products, and, similarity coefficients, as well as the relationships between them, employing (dis)similarity measures commonly used in cheminformatics as examples. We present several phenomena (empty space phenomenon, sphere volume related phenomena, distance concentration) in high-dimensional descriptor spaces which are not encountered in two and three dimensions. These phenomena are theoretically characterized and illustrated on both artificial and real (bioactivity) data.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

distances, high‐dimensional data, chemical descriptors, distance concentration

Zitieren

ISO 690

RUPP, Matthias, Petra SCHNEIDER, Gisbert SCHNEIDER, 2009. Distance phenomena in high-dimensional chemical descriptor spaces : Consequences for similarity-based approaches. In: Journal of Computational Chemistry. Wiley-Blackwell. 2009, 30(14), pp. 2285-2296. ISSN 0192-8651. eISSN 1096-987X. Available under: doi: 10.1002/jcc.21218

BibTex

@article{Rupp2009-11-15Dista-52121,
  year={2009},
  doi={10.1002/jcc.21218},
  title={Distance phenomena in high-dimensional chemical descriptor spaces : Consequences for similarity-based approaches},
  number={14},
  volume={30},
  issn={0192-8651},
  journal={Journal of Computational Chemistry},
  pages={2285--2296},
  author={Rupp, Matthias and Schneider, Petra 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/123456789/52121">
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-12-15T10:07:54Z</dcterms:available>
    <dc:contributor>Schneider, Gisbert</dc:contributor>
    <dc:creator>Schneider, Petra</dc:creator>
    <dcterms:title>Distance phenomena in high-dimensional chemical descriptor spaces : Consequences for similarity-based approaches</dcterms:title>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Rupp, Matthias</dc:creator>
    <dc:language>eng</dc:language>
    <dc:contributor>Schneider, Petra</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:abstract xml:lang="eng">Measuring the (dis)similarity of molecules is important for many cheminformatics applications like compound ranking, clustering, and property prediction. In this work, we focus on real-valued vector representations of molecules (as opposed to the binary spaces of fingerprints). We demonstrate the influence which the choice of (dis)similarity measure can have on results, and provide recommendations for such choices. We review the mathematical concepts used to measure (dis)similarity in vector spaces, namely norms, metrics, inner products, and, similarity coefficients, as well as the relationships between them, employing (dis)similarity measures commonly used in cheminformatics as examples. We present several phenomena (empty space phenomenon, sphere volume related phenomena, distance concentration) in high-dimensional descriptor spaces which are not encountered in two and three dimensions. These phenomena are theoretically characterized and illustrated on both artificial and real (bioactivity) data.</dcterms:abstract>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-12-15T10:07:54Z</dc:date>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/52121"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Schneider, Gisbert</dc:creator>
    <dc:contributor>Rupp, Matthias</dc:contributor>
    <dcterms:issued>2009-11-15</dcterms:issued>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:rights>terms-of-use</dc:rights>
  </rdf:Description>
</rdf:RDF>

Universitätsbibliographie

Nein

Begutachtet

Ja