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Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes

Miljković, Filip; Rodríguez-Pérez, Raquel; Bajorath, Jürgen


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  <identifier identifierType="DOI">10.5281/zenodo.3370478</identifier>
  <creators>
    <creator>
      <creatorName>Miljković, Filip</creatorName>
      <givenName>Filip</givenName>
      <familyName>Miljković</familyName>
      <affiliation>Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany.</affiliation>
    </creator>
    <creator>
      <creatorName>Rodríguez-Pérez, Raquel</creatorName>
      <givenName>Raquel</givenName>
      <familyName>Rodríguez-Pérez</familyName>
      <affiliation>Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany.</affiliation>
    </creator>
    <creator>
      <creatorName>Bajorath, Jürgen</creatorName>
      <givenName>Jürgen</givenName>
      <familyName>Bajorath</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0557-5714</nameIdentifier>
      <affiliation>Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany.</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>protein kinases</subject>
    <subject>kinase inhibitors</subject>
    <subject>machine learning</subject>
    <subject>inhibitor binding modes</subject>
    <subject>classification models</subject>
    <subject>X-ray data</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-08-18</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3370478</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3370477</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Random forest (RF), support-vector machine (SVM), and deep neural network (DNN) models for predicting kinase inhibitors with different binding modes in X-ray structures are made available together with the data sets used for training and testing.&lt;/p&gt;

&lt;p&gt;Please refer to READ_ME.txt for more information.&lt;/p&gt;</description>
  </descriptions>
</resource>
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