
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/">
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:source>2nd International Conference on Chemo and Bioinformatics, Book of Proceedings, ICCBIKG 2023, September 28-29, 2023 Kragujevac, Serbia</dc:source>
  <dc:identifier>https://phaidrabg.bg.ac.rs/o:31598</dc:identifier>
  <dc:identifier>doi:10.46793/ICCBI23.593R</dc:identifier>
  <dc:date>2023</dc:date>
  <dc:subject xml:lang="srp">Keywords: molecular similarity, molecular structure, binary vectors, molecular fingerprints, similarity measure.</dc:subject>
  <dc:creator id="https://orcid.org/0000-0003-4956-0407 https://plus.cobiss.net/cobiss/sr/sr/conor/26137703">Redžepović, Izudin</dc:creator>
  <dc:description xml:lang="eng">Abstract:
This paper unveils the findings derived from an in-depth exploration of a novel similarity measure designed to assess pairwise resemblances. Called the Substructure Similarity Index, this measure centers around the comparison of substructures identified within compounds. Through a rigorous evaluation conducted on an extensive dataset of drugs and by juxtaposing it against other commonly employed indices, the study reveals that the Substructure Similarity Index can be adeptly employed for molecular similarity calculations since it provides information that cannot be obtained by available measures.</dc:description>
  <dc:rights>All rights reserved</dc:rights>
  <dc:title xml:lang="eng">A metric for pairwise similarity analysis of binary cheminformatics data</dc:title>
  <dc:format>application/pdf</dc:format>
  <dc:format>1377735 bytes</dc:format>
  <dc:language>eng</dc:language>
</oai_dc:dc>
