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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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| Publicat a: | J Cheminform |
|---|---|
| Autors principals: | , , , , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
Springer International Publishing
2019
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6873550/ https://ncbi.nlm.nih.gov/pubmed/33430971 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s13321-019-0393-0 |
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