Journal article Open Access

PaccMann: a web service for interpretable anticancer compound sensitivity prediction

Cadow, Joris; Born, Jannis; Manica, Matteo; Oskooei, Ali; Rodríguez Martínez, María


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  <dc:creator>Cadow, Joris</dc:creator>
  <dc:creator>Born, Jannis</dc:creator>
  <dc:creator>Manica, Matteo</dc:creator>
  <dc:creator>Oskooei, Ali</dc:creator>
  <dc:creator>Rodríguez Martínez, María</dc:creator>
  <dc:date>2020-05-13</dc:date>
  <dc:description>The identification of new targeted and personalized therapies for cancer requires the fast and accurate assessment of the drug efficacy of potential compounds against a particular biomolecular sample. It has been suggested that the integration of complementary sources of information might strengthen the accuracy of a drug efficacy prediction model. Here, we present a web-based platform for the Prediction of AntiCancer Compound sensitivity with Multimodal Attention-based Neural Networks (PaccMann). PaccMann is trained on public transcriptomic cell line profiles, compound structure information and drug sensitivity screenings, and outperforms state-of-the-art methods on anticancer drug sensitivity prediction. On the open-access web service (https://ibm.biz/paccmann-aas), users can select a known drug compound or design their own compound structure in an interactive editor, perform in-silico drug testing and investigate compound efficacy on publicly available or user-provided transcriptomic profiles. PaccMann leverages methods for model interpretability and outputs confidence scores as well as attention heatmaps that highlight the genes and chemical sub-structures that were more important to make a prediction, hence facilitating the understanding of the model’s decision making and the involved biochemical processes. We hope to serve the community with a toolbox for fast and efficient validation in drug repositioning or lead compound identification regimes.</dc:description>
  <dc:identifier>https://zenodo.org/record/3935564</dc:identifier>
  <dc:identifier>10.1093/nar/gkaa327</dc:identifier>
  <dc:identifier>oai:zenodo.org:3935564</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/826121/</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/ipc</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:source>Nucleic Acids Research 48 502-508</dc:source>
  <dc:title>PaccMann: a web service for interpretable anticancer compound sensitivity prediction</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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