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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: ethanol |
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example_title: CCO |
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--- |
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# IUPAC2SMILES-canonical-base |
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IUPAC2SMILES-canonical-base was designed to accurately translate IUPAC chemical names to SMILES. |
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## Model Details |
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### Model Description |
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IUPAC2SMILES-canonical-base 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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### IUPAC to SMILES |
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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/IUPAC2SMILES-canonical-base") |
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print(converter.iupac_to_smiles('ethanol')) |
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print(converter.iupac_to_smiles(['ethanol', 'ethanol', 'ethanol'])) |
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``` |
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```text |
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['CCO'] |
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['CCO', 'CCO', 'CCO'] |
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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/IUPAC2SMILES-canonical-base") |
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print(converter.iupac_to_smiles(["buta-1,3-diene" 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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['<SYST>C=CC=C', '<SYST>C=CC=C'...] |
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``` |
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Our models also predict IUPAC styles from the table: |
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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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## 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=0.00001, batch_size=512 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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| IUPAC2SMILES-canonical-small |88.9% |0.966 |23 | |
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| IUPAC2SMILES-canonical-base |93.7% |0.974 |180 | |
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| STOUT V2.0\* |68.47% |0.92 |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 |
