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Table of Contents

• Introduction
• Models
• Quickstart
• Citation

Chemical-Converters serves as a foundational showcase of our technological capabilities within the chemical domain. The available models, which could be used in this library, represent our entry-level offerings, designed to provide a glimpse into the potential applications of our advanced solutions. For access to our comprehensive suite of larger and more precise models, we invite interested parties to e ngage directly with us.

Developed by the brilliant minds at Knowledgator, the library showcases the abilities of our chemical transformer models. Whether you're working on a research project, studying for an exam, or just exploring the chemical universe, Chemical-Converters is your go-to tool 🛠.

The models` architecture is based on Google MT5 with certain modification to support different inputs and outputs. All available models are presented in the table:

also, you can check the most resent models within the library:

from chemicalconverters import NamesConverter

print(NamesConverter.available_models())

{'knowledgator/SMILES2IUPAC-canonical-small': 'Small model for converting canonical SMILES to IUPAC with accuracy 75%, does not support isomeric or isotopic SMILES', 'knowledgator/SMILES2IUPAC-canonical-base': 'Medium model for converting canonical SMILES to IUPAC with accuracy 87%, does not support isomeric or isotopic SMILES', 'knowledgator/IUPAC2SMILES-canonical-small': 'Small model for converting IUPAC to canonical SMILES with accuracy 89%, does not support isomeric or isotopic SMILES', 'knowledgator/IUPAC2SMILES-canonical-base': 'Medium model for converting IUPAC to canonical SMILES with accuracy 94%, does not support isomeric or isotopic SMILES'}

Firstly, install the library:

pip install chemical-converters

You can choose pretrained model from table in the section "Models", but we recommend to use model "knowledgator/SMILES2IUPAC-canonical-base".

To choose the preferred IUPAC style, place style tokens before your SMILES sequence.

from chemicalconverters import NamesConverter

converter = NamesConverter(model_name="knowledgator/SMILES2IUPAC-canonical-base")
print(converter.smiles_to_iupac('CCO'))
print(converter.smiles_to_iupac(['<SYST>CCO', '<TRAD>CCO', '<BASE>CCO']))

['ethanol']
['ethanol', 'ethanol', 'ethanol']

from chemicalconverters import NamesConverter

converter = NamesConverter(model_name="knowledgator/SMILES2IUPAC-canonical-base")
print(converter.smiles_to_iupac(["<BASE>C=CC=C" for _ in range(10)], num_beams=1, 
                                process_in_batch=True, batch_size=1000))

['buta-1,3-diene', 'buta-1,3-diene'...]

It's possible to validate the translations by reverse translation into IUPAC and calculating Tanimoto similarity of two molecules fingerprints.

from chemicalconverters import NamesConverter

converter = NamesConverter(model_name="knowledgator/SMILES2IUPAC-canonical-base")
print(converter.smiles_to_iupac('CCO', validate=True))

['ethanol'] 1.0

The larger is Tanimoto similarity, the more is probability, that the prediction was correct.

You can also process validation manually:

from chemicalconverters import NamesConverter

validation_model = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base")
print(NamesConverter.validate_iupac(input_sequence='CCO', predicted_sequence='ethanol', validation_model=validation_model))

1.0

!Note validation was not implemented in processing in batches.

You can choose pretrained model from table in the section "Models", but we recommend to use model "knowledgator/IUPAC2SMILES-canonical-base".

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']))

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

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:

Coming soon.