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Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds.
Korshunova, Maria; Huang, Niles; Capuzzi, Stephen; Radchenko, Dmytro S; Savych, Olena; Moroz, Yuriy S; Wells, Carrow I; Willson, Timothy M; Tropsha, Alexander; Isayev, Olexandr.
Afiliação
  • Korshunova M; Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA, USA. mariewelt@cmu.edu.
  • Huang N; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. mariewelt@cmu.edu.
  • Capuzzi S; Department of Biochemistry, University of Oxford, Oxford, UK.
  • Radchenko DS; Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Savych O; Enamine Ltd, 78 Chervonotkatska Street, Kyiv, 02094, Ukraine.
  • Moroz YS; Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyiv, 01601, Ukraine.
  • Wells CI; Enamine Ltd, 78 Chervonotkatska Street, Kyiv, 02094, Ukraine.
  • Willson TM; Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyiv, 01601, Ukraine.
  • Tropsha A; Chemspace LLC, Chervonotkatska Street 85, Suite 1, Kyiv, 02094, Ukraine.
  • Isayev O; Structual Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Commun Chem ; 5(1): 129, 2022 Oct 18.
Article em En | MEDLINE | ID: mdl-36697952
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article