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--- |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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- bleu |
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pipeline_tag: text2text-generation |
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tags: |
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- chemistry |
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- biology |
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- medical |
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- smiles |
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- iupac |
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- text-generation-inference |
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widget: |
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- text: CCO |
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example_title: ethanol |
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--- |
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# SMILES2IUPAC-canonical-small |
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SMILES2IUPAC-canonical-small was designed to accurately translate SMILES chemical names to IUPAC standards. |
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## Model Details |
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### Model Description |
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SMILES2IUPAC-canonical-small is based on the MT5 model with optimizations in implementing different tokenizers for the encoder and decoder. |
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- **Developed by:** Knowladgator Engineering |
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- **Model type:** Encoder-Decoder with attention mechanism |
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- **Language(s) (NLP):** SMILES, IUPAC (English) |
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- **License:** Apache License 2.0 |
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### Model Sources |
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- **Paper:** coming soon |
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- **Demo:** [ChemicalConverters](https://huggingface.co/spaces/knowledgator/ChemicalConverters) |
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## Quickstart |
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Firstly, install the library: |
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```commandline |
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pip install chemical-converters |
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``` |
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### SMILES to IUPAC |
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#### ! Preferred IUPAC style |
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To choose the preferred IUPAC style, place style tokens before |
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your SMILES sequence. |
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| Style Token | Description | |
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|-------------|----------------------------------------------------------------------------------------------------| |
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| `<BASE>` | The most known name of the substance, sometimes is the mixture of traditional and systematic style | |
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| `<SYST>` | The totally systematic style without trivial names | |
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| `<TRAD>` | The style is based on trivial names of the parts of substances | |
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#### To perform simple translation, follow the example: |
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```python |
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from chemicalconverters import NamesConverter |
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converter = NamesConverter(model_name="knowledgator/SMILES2IUPAC-canonical-small") |
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print(converter.smiles_to_iupac('CCO')) |
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print(converter.smiles_to_iupac(['<SYST>CCO', '<TRAD>CCO', '<BASE>CCO'])) |
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``` |
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```text |
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['ethanol'] |
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['ethanol', 'ethanol', 'ethanol'] |
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``` |
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#### Processing in batches: |
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```python |
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from chemicalconverters import NamesConverter |
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converter = NamesConverter(model_name="knowledgator/SMILES2IUPAC-canonical-small") |
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print(converter.smiles_to_iupac(["<BASE>C=CC=C" for _ in range(10)], num_beams=1, |
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process_in_batch=True, batch_size=1000)) |
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``` |
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```text |
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['buta-1,3-diene', 'buta-1,3-diene'...] |
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``` |
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#### Validation SMILES to IUPAC translations |
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It's possible to validate the translations by reverse translation into IUPAC |
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and calculating Tanimoto similarity of two molecules fingerprints. |
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````python |
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from chemicalconverters import NamesConverter |
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converter = NamesConverter(model_name="knowledgator/SMILES2IUPAC-canonical-small") |
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print(converter.smiles_to_iupac('CCO', validate=True)) |
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```` |
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````text |
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['ethanol'] 1.0 |
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```` |
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The larger is Tanimoto similarity, the larger is probability, that the prediction was correct. |
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You can also process validation manually: |
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```python |
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from chemicalconverters import NamesConverter |
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validation_model = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base") |
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print(NamesConverter.validate_iupac(input_sequence='CCO', predicted_sequence='CCO', validation_model=validation_model)) |
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``` |
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```text |
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1.0 |
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``` |
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## Bias, Risks, and Limitations |
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This model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES. |
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### Training Procedure |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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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. |
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## Evaluation |
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| Model | Accuracy | BLEU-4 score | Size(MB) | |
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|-------------------------------------|---------|------------------|----------| |
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| SMILES2IUPAC-canonical-small |75%| 0.93 | 23 | |
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| SMILES2IUPAC-canonical-base |86.9%|0.964|180| |
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| STOUT V2.0\* | 66.65% | 0.92 | 128 | |
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| STOUT V2.0 (according to our tests) | | 0.89 | 128 | |
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*According to the original paper https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00512-4 |
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## Citation |
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Coming soon. |
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## Model Card Authors |
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[Mykhailo Shtopko](https://huggingface.co/BioMike) |
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## Model Card Contact |
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info@knowledgator.com |
