IUPAC2SMILES-canonical-small

IUPAC2SMILES-canonical-small

IUPAC2SMILES-canonical-small was designed to accurately translate IUPAC chemical names to SMILES.

Model Details

Model Description

IUPAC2SMILES-canonical-small is based on the MT5 model with optimizations in implementing different tokenizers for the encoder and decoder.

• Developed by: Knowladgator Engineering
• Model type: Encoder-Decoder with attention mechanism
• Language(s) (NLP): SMILES, IUPAC (English)
• License: Apache License 2.0

Model Sources

• Paper: coming soon
• Demo: ChemicalConverters

Quickstart

Firstly, install the library:

pip install chemical-converters

IUPAC to SMILES

To perform simple translation, follow the example:

from chemicalconverters import NamesConverter

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

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

Processing in batches:

from chemicalconverters import NamesConverter

converter = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-small")
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:

Bias, Risks, and Limitations

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

Training Procedure

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The model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.0003, batch_size=1024 for 2 epochs.

Evaluation

Citation

Coming soon.

Model Card Authors

Mykhailo Shtopko

Model Card Contact

[email protected]