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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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    "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>", 
    "language": "eng", 
    "title": "PaccMann: a web service for interpretable anticancer compound sensitivity prediction", 
    "license": {
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      "volume": "48", 
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    "publication_date": "2020-05-13", 
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        "name": "Oskooei, Ali"
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