Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface.

Journal: PloS one
Published Date: Jan 1, 2021

Abstract

The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein of SARS-CoV-2 virus with ACE2 human receptor interface. Based on over 1800 structures in the training set we have predicted binding properties of the complete set of nearly 600000 structures from the same class using the Machine Learning Random Forest Regressor approach.

Authors

  • Monika Rola
    Faculty of Chemistry, Lodz University of Technology, Lodz, Poland.
  • Jakub Krassowski
    Faculty of Chemistry, Lodz University of Technology, Lodz, Poland.
  • Julita Górska
    Faculty of Chemistry, Lodz University of Technology, Lodz, Poland.
  • Anna Grobelna
    Faculty of Chemistry, Lodz University of Technology, Lodz, Poland.
  • Wojciech Płonka
    Center for Bioinformatics (ZBH), Universität Hamburg, Hamburg, Germany.
  • Agata Paneth
    Department of Organic Chemistry, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland.
  • Piotr Paneth
    International Center for Research on Innovative Biobased Materials (ICRI-BioM) - International Research Agenda, Lodz University of Technology, Lodz, Poland.

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