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PaccMann: a web service for interpretable anticancer compound sensitivity prediction.
Journal article

PaccMann: a web service for interpretable anticancer compound sensitivity prediction.

  • Cadow J Computational Systems Biology Group, IBM Research Europe, Säumerstrasse 4, Rüschlikon, 8803, Switzerland.
  • Born J Computational Systems Biology Group, IBM Research Europe, Säumerstrasse 4, Rüschlikon, 8803, Switzerland.
  • Manica M Computational Systems Biology Group, IBM Research Europe, Säumerstrasse 4, Rüschlikon, 8803, Switzerland.
  • Oskooei A Computational Systems Biology Group, IBM Research Europe, Säumerstrasse 4, Rüschlikon, 8803, Switzerland.
  • Rodríguez Martínez M Computational Systems Biology Group, IBM Research Europe, Säumerstrasse 4, Rüschlikon, 8803, Switzerland.
  • 2020-05-14
Published in:
  • Nucleic acids research. - 2020
Antineoplastic Agents
Computer Simulation
Drug Repositioning
Gene Expression Profiling
Internet
Neural Networks, Computer
Sirolimus
Software
English 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.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://sonar.ch/global/documents/262312
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