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PaccMannRL: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning

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Research in Computational Molecular Biology (RECOMB 2020)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12074))

Abstract

The pharmaceutical industry has experienced a significant productivity decline: Less than 0.01% of drug candidates obtain market approval, with an estimated 10–15 years until market release and costs that range between one [2] to three billion dollars per drug [3].

J. Born, M. Manica, A. Oskooei—Equal Contributions.

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References

  1. Manica, M., et al.: Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders. Molecular Pharmaceutics (2019). ACS Publications

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  2. Scannell, J.W., Blanckley, A., Boldon, H., Warrington, B.: Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discovery 11(3), 191 (2012)

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  3. Schneider, G.: Mind and machine in drug design. Nat. Mach. Intell. 1, 128–130 (2019)

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Authors and Affiliations

  1. IBM Research Zurich, Zurich, Switzerland

    Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow & María Rodríguez Martínez

  2. Machine Learning for Computational Biology Lab, ETH Zurich, Zurich, Switzerland

    Jannis Born

Authors
  1. Jannis Born
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  2. Matteo Manica
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  3. Ali Oskooei
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  4. Joris Cadow
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  5. María Rodríguez Martínez
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Corresponding author

Correspondence to Jannis Born .

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Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Russell Schwartz

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Born, J., Manica, M., Oskooei, A., Cadow, J., Rodríguez Martínez, M. (2020). PaccMannRL: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning. In: Schwartz, R. (eds) Research in Computational Molecular Biology. RECOMB 2020. Lecture Notes in Computer Science(), vol 12074. Springer, Cham. https://doi.org/10.1007/978-3-030-45257-5_18

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  • DOI: https://doi.org/10.1007/978-3-030-45257-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45256-8

  • Online ISBN: 978-3-030-45257-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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