Journal article Open Access

The rise of deep learning in drug discovery

Hongming, Chen; Engkvist, Ola; Wang, Yinhai; Olivecrona, Marcus; Blaschke, Thomas


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  <identifier identifierType="URL">https://zenodo.org/record/1175821</identifier>
  <creators>
    <creator>
      <creatorName>Hongming, Chen</creatorName>
      <givenName>Chen</givenName>
      <familyName>Hongming</familyName>
      <affiliation>Hit Discovery, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&amp;D Gothenburg, Mölndal 43183, Sweden</affiliation>
    </creator>
    <creator>
      <creatorName>Engkvist, Ola</creatorName>
      <givenName>Ola</givenName>
      <familyName>Engkvist</familyName>
      <affiliation>Hit Discovery, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&amp;D Gothenburg, Mölndal 43183, Sweden</affiliation>
    </creator>
    <creator>
      <creatorName>Wang, Yinhai</creatorName>
      <givenName>Yinhai</givenName>
      <familyName>Wang</familyName>
      <affiliation>Quantitative Biology, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Unit 310, Cambridge Science Park, Milton Road, Cambridge CB4 0WG, UK</affiliation>
    </creator>
    <creator>
      <creatorName>Olivecrona, Marcus</creatorName>
      <givenName>Marcus</givenName>
      <familyName>Olivecrona</familyName>
      <affiliation>Hit Discovery, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&amp;D Gothenburg, Mölndal 43183, Sweden</affiliation>
    </creator>
    <creator>
      <creatorName>Blaschke, Thomas</creatorName>
      <givenName>Thomas</givenName>
      <familyName>Blaschke</familyName>
      <affiliation>Hit Discovery, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&amp;D Gothenburg, Mölndal 43183, Sweden</affiliation>
    </creator>
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  <titles>
    <title>The rise of deep learning in drug discovery</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <dates>
    <date dateType="Issued">2018-01-31</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1175821</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/j.drudis.2018.01.039</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/bigchem</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://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;Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, &lt;em&gt;de novo&lt;/em&gt; molecular design, synthesis prediction and biological image analysis.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/676434/">676434</awardNumber>
      <awardTitle>Big Data in Chemistry</awardTitle>
    </fundingReference>
  </fundingReferences>
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