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---
license: apache-2.0
metrics:
- accuracy
- bleu
pipeline_tag: text2text-generation
tags:
- chemistry
- biology
- medical
- smiles
- iupac
- text-generation-inference
widget:
- text: ethanol
example_title: CCO
---
# IUPAC2SMILES-canonical-base
IUPAC2SMILES-canonical-base was designed to accurately translate IUPAC chemical names to SMILES.
## Model Details
### Model Description
IUPAC2SMILES-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](https://huggingface.co/spaces/knowledgator/ChemicalConverters)
## Quickstart
Firstly, install the library:
```commandline
pip install chemical-converters
```
### IUPAC to SMILES
#### To perform simple translation, follow the example:
```python
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']))
```
```text
['CCO']
['CCO', 'CCO', 'CCO']
```
#### Processing in batches:
```python
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))
```
```text
['<SYST>C=CC=C', '<SYST>C=CC=C'...]
```
Our models also predict IUPAC styles from the table:
| 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 |
## Bias, Risks, and Limitations
This model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES.
### Training Procedure
<!-- 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. -->
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) |
|-------------------------------------|---------|------------------|----------|
| IUPAC2SMILES-canonical-small |88.9% |0.966 |23 |
| IUPAC2SMILES-canonical-base |93.7% |0.974 |180 |
| STOUT V2.0\* |68.47% |0.92 |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](https://huggingface.co/BioMike)
## Model Card Contact
info@knowledgator.com |