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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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| Publicado no: | J Cheminform |
|---|---|
| Main Authors: | , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Springer International Publishing
2019
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| Assuntos: | |
| Acesso em linha: | 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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