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: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 (, 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:source>Nucleic Acids Research 48 502-508</dc:source>
  <dc:title>PaccMann: a web service for interpretable anticancer compound sensitivity prediction</dc:title>
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