Exploring the GDB-13 chemical space using deep generative models

Recent applications of recurrent neural networks (RNN) enable training models that sample the chemical space. In this study we train RNN with molecular string representations (SMILES) with a subset of the enumerated database GDB-13 (975 million molecules). We show that a model trained with 1 million...

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Detalles Bibliográficos
Autores principales: Arús-Pous, Josep, Blaschke, Thomas, Ulander, Silas, Reymond, Jean-Louis, Chen, Hongming, Engkvist, Ola
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419837/
https://www.ncbi.nlm.nih.gov/pubmed/30868314
http://dx.doi.org/10.1186/s13321-019-0341-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419837/
https://www.ncbi.nlm.nih.gov/pubmed/30868314
http://dx.doi.org/10.1186/s13321-019-0341-z

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