Randomized SMILES strings improve the quality of molecular generative models
Abstract 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...
I tiakina i:
| Ngā kaituhi matua: | , , , , , , , |
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| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
BMC
2019-11-01
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| Rangatū: | Journal of Cheminformatics |
| Ngā marau: | |
| Urunga tuihono: | http://link.springer.com/article/10.1186/s13321-019-0393-0 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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