State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis

We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using text-like representation of chemical reactions (SMILES) and Natural Language Processing (NLP) neural network Transformer architecture. We showed that data augmentation, which is...

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Detalles Bibliográficos
Autores principales: Tetko, Igor V., Karpov, Pavel, Van Deursen, Ruud, Godin, Guillaume
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643129/
https://www.ncbi.nlm.nih.gov/pubmed/33149154
http://dx.doi.org/10.1038/s41467-020-19266-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643129/
https://www.ncbi.nlm.nih.gov/pubmed/33149154
http://dx.doi.org/10.1038/s41467-020-19266-y

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