SMILES2IUPAC-canonical-base

SMILES2IUPAC-canonical-base was designed to accurately translate SMILES chemical names to IUPAC standards.

Model Details

Model Description

SMILES2IUPAC-canonical-base 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

SMILES to IUPAC

! Preferred IUPAC style

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

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

To perform simple translation, follow the example:

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']

Processing in batches:

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'...]

Validation SMILES to IUPAC translations

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 larger 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='CCO', validation_model=validation_model))

1.0

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)
SMILES2IUPAC-canonical-small	75%	0.93	23
SMILES2IUPAC-canonical-base	86.9%	0.964	180
STOUT V2.0*	66.65%	0.92	128
STOUT V2.0 (according to our tests)		0.89	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

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