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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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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Miljković, Filip</dc:creator>
  <dc:creator>Rodríguez-Pérez, Raquel</dc:creator>
  <dc:creator>Bajorath, Jürgen</dc:creator>
  <dc:date>2019-08-18</dc:date>
  <dc:description>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.

Please refer to READ_ME.txt for more information.</dc:description>
  <dc:identifier>https://zenodo.org/record/3370478</dc:identifier>
  <dc:identifier>10.5281/zenodo.3370478</dc:identifier>
  <dc:identifier>oai:zenodo.org:3370478</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.3370477</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>protein kinases</dc:subject>
  <dc:subject>kinase inhibitors</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>inhibitor binding modes</dc:subject>
  <dc:subject>classification models</dc:subject>
  <dc:subject>X-ray data</dc:subject>
  <dc:title>Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
</oai_dc:dc>
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