Randomized SMILES strings improve the quality of molecular generative models

Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces of valid and meaningful structures. Herein we perform an extensive benchmark on models trained with subsets of GDB-13 of differen...

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Detalles Bibliográficos
Autores principales: Arús-Pous, Josep, Johansson, Simon Viet, Prykhodko, Oleksii, Bjerrum, Esben Jannik, Tyrchan, Christian, 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/PMC6873550/
https://www.ncbi.nlm.nih.gov/pubmed/33430971
http://dx.doi.org/10.1186/s13321-019-0393-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873550/
https://www.ncbi.nlm.nih.gov/pubmed/33430971
http://dx.doi.org/10.1186/s13321-019-0393-0

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