1 Ocak 2017 Dergi makalesi Açık Erişim

Mason, Daniel J.; Stott, Ian; Ashenden, Stephanie; Weinstein, Zohar B.; Karakoc, Idil; Meral, Selin; Kuru, Nurdan; Bender, Andreas; Cokol, Murat

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/51339</identifier>
  <creators>
    <creator>
      <creatorName>Mason, Daniel J.</creatorName>
      <givenName>Daniel J.</givenName>
      <familyName>Mason</familyName>
      <affiliation>Univ Cambridge, Dept Chem, Ctr Mol Informat, Cambridge CB2 1EW, England</affiliation>
    </creator>
    <creator>
      <creatorName>Stott, Ian</creatorName>
      <givenName>Ian</givenName>
      <familyName>Stott</familyName>
      <affiliation>Unilever Res Labs, Wirral CH63 3JW, Merseyside, England</affiliation>
    </creator>
    <creator>
      <creatorName>Ashenden, Stephanie</creatorName>
      <givenName>Stephanie</givenName>
      <familyName>Ashenden</familyName>
      <affiliation>Univ Cambridge, Dept Chem, Ctr Mol Informat, Cambridge CB2 1EW, England</affiliation>
    </creator>
    <creator>
      <creatorName>Weinstein, Zohar B.</creatorName>
      <givenName>Zohar B.</givenName>
      <familyName>Weinstein</familyName>
      <affiliation>Boston Univ, Sch Med, Boston, MA 02118 USA</affiliation>
    </creator>
    <creator>
      <creatorName>Karakoc, Idil</creatorName>
      <givenName>Idil</givenName>
      <familyName>Karakoc</familyName>
      <affiliation>Sabanci Univ, Fac Engn &amp; Nat Sci, TR-34956 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Meral, Selin</creatorName>
      <givenName>Selin</givenName>
      <familyName>Meral</familyName>
      <affiliation>Sabanci Univ, Fac Engn &amp; Nat Sci, TR-34956 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Kuru, Nurdan</creatorName>
      <givenName>Nurdan</givenName>
      <familyName>Kuru</familyName>
      <affiliation>Sabanci Univ, Fac Engn &amp; Nat Sci, TR-34956 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Bender, Andreas</creatorName>
      <givenName>Andreas</givenName>
      <familyName>Bender</familyName>
      <affiliation>Univ Cambridge, Dept Chem, Ctr Mol Informat, Cambridge CB2 1EW, England</affiliation>
    </creator>
    <creator>
      <creatorName>Cokol, Murat</creatorName>
      <givenName>Murat</givenName>
      <familyName>Cokol</familyName>
    </creator>
  </creators>
  <titles>
    <title>Prediction Of Antibiotic Interactions Using Descriptors Derived From Molecular Structure</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2017</publicationYear>
  <dates>
    <date dateType="Issued">2017-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/51339</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1021/acs.jmedchem.7b00204</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs. Of the 12 pairs predicted to be synergistic, 10 were experimentally validated, corresponding to a 2.8-fold enrichment. Having thus validated our methodology, we produced a compendium of interaction predictions for all pairwise combinations among 100 antibiotics. Our methodology can make reliable antibiotic interaction predictions for any antibiotic pair within the applicability domain of the model since it solely requires chemical structures as an input.</description>
  </descriptions>
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