from chemicalconverters import NamesConverter

converter = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base")
print(converter.iupac_to_smiles('ethanol'))
print(converter.iupac_to_smiles(['ethanol', 'ethanol', 'ethanol']))

text

['CCO']
['CCO', 'CCO', 'CCO']

Processing in batches:

python

from chemicalconverters import NamesConverter

converter = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base")
print(converter.iupac_to_smiles(["buta-1,3-diene" for _ in range(10)], num_beams=1, 
                                process_in_batch=True, batch_size=1000))

['<SYST>C=CC=C', '<SYST>C=CC=C'...]

Our models also predict IUPAC styles from the table:

Style Token	Description
`<BASE>`	The most known name of the substance, sometimes is the mixture of traditional and systematic style
`<SYST>`	The totally systematic style without trivial names
`<TRAD>`	The style is based on trivial names of the parts of substances

Bias, Risks, and Limitations

This model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES.

Training Procedure

The model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.00001, batch_size=512 for 2 epochs.

Evaluation

Model	Accuracy	BLEU-4 score	Size(MB)
IUPAC2SMILES-canonical-small	88.9%	0.966	23
IUPAC2SMILES-canonical-base	93.7%	0.974	180
STOUT V2.0*	68.47%	0.92	128
*According to the original paper https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00512-4

Citation

Coming soon.

Model Card Authors

Mykhailo Shtopko

Model Card Contact

info@knowledgator.com