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PrePeP: A Light-Weight, Extensible Tool for Predicting Frequent Hitters

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track (ECML PKDD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12461))

Abstract

We present PrePeP, a light-weight tool for predicting whether molecules are frequent hitters, and visually inspecting the subgraphs supporting this decision. PrePeP is contains three modules: a mining component, an encoding/predicting component, and a graphical interface, all of which are easily extensible.

The tool can be downloaded at http://scientific-data-mining.org, “Software”.

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Notes

  1. 1.

    https://lejournal.cnrs.fr/articles/covid-19-15-milliard-de-molecules-passees-au-criblage-virtuel.

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Author information

Authors and Affiliations

  1. Normandie Univ, UNICAEN, ENSICAEN, CNRS – UMR GREYC, Caen, France

    Christophe Couronne, Maksim Koptelov & Albrecht Zimmermann

Authors
  1. Christophe Couronne
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  2. Maksim Koptelov
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  3. Albrecht Zimmermann
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Corresponding author

Correspondence to Albrecht Zimmermann .

Editor information

Editors and Affiliations

  1. Microsoft Research, Redmond, WA, USA

    Yuxiao Dong

  2. University College Dublin, Dublin, Ireland

    Georgiana Ifrim

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Dunja Mladenić

  4. Amazon Alexa Knowledge, Cambridge, UK

    Craig Saunders

  5. Ghent University, Kotrijk, Belgium

    Sofie Van Hoecke

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Couronne, C., Koptelov, M., Zimmermann, A. (2021). PrePeP: A Light-Weight, Extensible Tool for Predicting Frequent Hitters. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_41

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  • DOI: https://doi.org/10.1007/978-3-030-67670-4_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67669-8

  • Online ISBN: 978-3-030-67670-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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