DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space.

Journal: Journal of chemical information and modeling
Published Date:

Abstract

The discovery of novel molecules with desirable properties is a classic challenge in medicinal chemistry. With the recent advancements of machine learning, there has been a surge of drug design tools. However, few resources exist that are user-friendly as well as easily customizable. In this application note, we present the new versatile open-source software package DrugEx for multiobjective reinforcement learning. This package contains the consolidated and redesigned scripts from the prior DrugEx papers including multiple generator architectures, a variety of scoring tools, and multiobjective optimization methods. It has a flexible application programming interface and can readily be used via the command line interface or the graphical user interface GenUI. The DrugEx package is publicly available at https://github.com/CDDLeiden/DrugEx.

Authors

  • Martin Šícho
    Faculty of Mathematics, Informatics and Natural Sciences, Department of Computer Science, Center for Bioinformatics, Universität Hamburg , Hamburg, 20146, Germany.
  • Sohvi Luukkonen
    ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, A-4040 Linz, Austria.
  • Helle W van den Maagdenberg
    Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden, 2333 CC, the Netherlands.
  • Linde Schoenmaker
    Leiden Academic Centre for Drug Research, Leiden University, 55 Einsteinweg, 2333 CC, Leiden, The Netherlands.
  • Olivier J M Béquignon
    Leiden Academic Centre for Drug Research, Leiden University, 55 Einsteinweg, 2333 CC, Leiden, The Netherlands.
  • Gerard J P van Westen
    Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands. Electronic address: gerard@lacdr.leidenuniv.nl.