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

Martin Šícho; Sohvi Luukkonen; Helle W van den Maagdenberg; Linde Schoenmaker; Olivier J M Béquignon; Gerard J P van Westen

doi:10.1021/acs.jcim.3c00434

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

J Chem Inf Model. 2023 Jun 26;63(12):3629-3636. doi: 10.1021/acs.jcim.3c00434. Epub 2023 Jun 5.

Authors

Martin Šícho^{1

2}, Sohvi Luukkonen¹, Helle W van den Maagdenberg¹, Linde Schoenmaker¹, Olivier J M Béquignon¹, Gerard J P van Westen¹

Affiliations

¹ Leiden Academic Centre for Drug Research, Leiden University, 55 Einsteinweg, 2333 CC, Leiden, The Netherlands.
² CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic.

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 de novo 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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Deep Learning*
Drug Design
Machine Learning
Software