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

[Image: see text] 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 cus...

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Detalles Bibliográficos
Autores principales: Šícho, Martin, Luukkonen, Sohvi, van den Maagdenberg, Helle W., Schoenmaker, Linde, Béquignon, Olivier J. M., van Westen, Gerard J. P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10306259/
https://www.ncbi.nlm.nih.gov/pubmed/37272707
http://dx.doi.org/10.1021/acs.jcim.3c00434
Descripción
Sumario:[Image: see text] 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.