Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.

Thomas, Morgan; Smith, Robert T; O'Boyle, Noel M; de Graaf, Chris; Bender, Andreas

Thomas, Morgan; Smith, Robert T; O'Boyle, Noel M; de Graaf, Chris; Bender, Andreas.

Afiliación

Thomas M; Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
Smith RT; Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK.
O'Boyle NM; Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK.
de Graaf C; Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK. chris.degraaf@soseiheptares.com.
Bender A; Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK. ab454@cam.ac.uk.

J Cheminform ; 13(1): 39, 2021 May 13.

Article en En | MEDLINE | ID: mdl-33985583

Palabras clave

AI; Artificial Intelligence; De novo design; Deep learning; Generative models; LBDD; Ligand-based drug design; Molecular docking; QSAR; Quantitative structureactivity relationship; Recurrent neural network; Reinforcement learning; SBDD; Structure-based drug design

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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: J Cheminform Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido

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