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DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space.
Sícho, Martin; Luukkonen, Sohvi; van den Maagdenberg, Helle W; Schoenmaker, Linde; Béquignon, Olivier J M; van Westen, Gerard J P.
Afiliación
  • Sícho M; Leiden Academic Centre for Drug Research, Leiden University, 55 Einsteinweg, 2333 CC, Leiden, The Netherlands.
  • Luukkonen S; 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.
  • van den Maagdenberg HW; Leiden Academic Centre for Drug Research, Leiden University, 55 Einsteinweg, 2333 CC, Leiden, The Netherlands.
  • Schoenmaker L; Leiden Academic Centre for Drug Research, Leiden University, 55 Einsteinweg, 2333 CC, Leiden, The Netherlands.
  • Béquignon OJM; Leiden Academic Centre for Drug Research, Leiden University, 55 Einsteinweg, 2333 CC, Leiden, The Netherlands.
  • van Westen GJP; Leiden Academic Centre for Drug Research, Leiden University, 55 Einsteinweg, 2333 CC, Leiden, The Netherlands.
J Chem Inf Model ; 63(12): 3629-3636, 2023 06 26.
Article en En | MEDLINE | ID: mdl-37272707
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.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Revista: J Chem Inf Model Asunto de la revista: INFORMATICA MEDICA / QUIMICA Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Revista: J Chem Inf Model Asunto de la revista: INFORMATICA MEDICA / QUIMICA Año: 2023 Tipo del documento: Article