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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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Hongming, Chen</dc:creator>
  <dc:creator>Engkvist, Ola</dc:creator>
  <dc:creator>Wang, Yinhai</dc:creator>
  <dc:creator>Olivecrona, Marcus</dc:creator>
  <dc:creator>Blaschke, Thomas</dc:creator>
  <dc:description>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, de novo molecular design, synthesis prediction and biological image analysis.</dc:description>
  <dc:subject>"Marie Sklodowska-Curie Actions"</dc:subject>
  <dc:title>The rise of deep learning in drug discovery</dc:title>
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