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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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<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 (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&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>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "@id": "https://orcid.org/0000-0002-4410-2805", 
      "@type": "Person", 
      "name": "Cadow, Joris"
    }, 
    {
      "@id": "https://orcid.org/0000-0001-8307-5670", 
      "@type": "Person", 
      "name": "Born, Jannis"
    }, 
    {
      "@id": "https://orcid.org/0000-0002-8872-0269", 
      "@type": "Person", 
      "name": "Manica, Matteo"
    }, 
    {
      "@id": "https://orcid.org/0000-0002-8318-687X", 
      "@type": "Person", 
      "name": "Oskooei, Ali"
    }, 
    {
      "@id": "https://orcid.org/0000-0003-3766-4233", 
      "@type": "Person", 
      "name": "Rodr\u00edguez Mart\u00ednez, Mar\u00eda"
    }
  ], 
  "headline": "PaccMann: a web service for interpretable anticancer compound sensitivity prediction", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2020-05-13", 
  "url": "https://zenodo.org/record/3935564", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1093/nar/gkaa327", 
  "@id": "https://doi.org/10.1093/nar/gkaa327", 
  "@type": "ScholarlyArticle", 
  "name": "PaccMann: a web service for interpretable anticancer compound sensitivity prediction"
}
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