State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
Development of algorithms to predict reactant and reagents given a target molecule is key to accelerate retrosynthesis approaches. Here the authors demonstrate that applying augmentation techniques to the SMILE representation of target data significantly improves the quality of the reaction predicti...
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2020
|
| Sujets: | |
| Accès en ligne: | https://doaj.org/article/f3db04ada827413392a0875994a0169d |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|