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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  "DOI": "10.1093/nar/gkaa327", 
  "container_title": "Nucleic Acids Research", 
  "language": "eng", 
  "title": "PaccMann: a web service for interpretable anticancer compound sensitivity prediction", 
  "issued": {
    "date-parts": [
  "abstract": "<p>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&rsquo;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.</p>", 
  "author": [
      "family": "Cadow, Joris"
      "family": "Born, Jannis"
      "family": "Manica, Matteo"
      "family": "Oskooei, Ali"
      "family": "Rodr\u00edguez Mart\u00ednez, Mar\u00eda"
  "page": "502-508", 
  "volume": "48", 
  "type": "article-journal", 
  "id": "3935564"
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