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  <created>1673986175</created>
  <changed>1673986175</changed>
  <title><![CDATA[Summit Supercomputer, Deep Learning Power Protein Interaction Prediction]]></title>
  <body><![CDATA[<p>Understanding protein interactions is key to innumerable fields &mdash;&nbsp;including, notably, drug design. Now, researchers from the Georgia Institute of Technology have developed a machine learning tool to predict interactions between multiple proteins, paving the way for easier identification of drug targets for antibiotics and therapeutics.&nbsp;The open-source, publicly available tool is called&nbsp;<a href="https://github.com/FreshAirTonight/af2complex">AF2Complex</a>&nbsp;&mdash;&nbsp;short for AlphaFold 2 Complex, since the tool is built on top of London-based artificial intelligence lab DeepMind&rsquo;s AlphaFold 2 protein structure prediction program.&nbsp;<a href="https://biosciences.gatech.edu/people/jeffrey-skolnick">Jeffrey Skolnick</a>, Regents&#39; Professor and Mary and Maisie Gibson Chair in the <a href="https://biosciences.gatech.edu">School of Biological Sciences</a>, and <a href="https://biosciences.gatech.edu/people/mu_gao">Mu Gao</a>, senior research scientist, are co-authors of the study.&nbsp;</p>
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      <url><![CDATA[https://www.hpcwire.com/2023/01/09/summit-supercomputer-deep-learning-power-protein-interaction-prediction/]]></url>
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      <value>2023-01-09</value>
      <timezone></timezone>
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          <item>1278</item>
          <item>1275</item>
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          <item><![CDATA[College of Sciences]]></item>
          <item><![CDATA[School of Biological Sciences]]></item>
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