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--- |
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library_name: transformers |
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tags: |
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- chemistry |
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- biology |
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- cheminformatics |
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- materials science |
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license: mit |
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language: |
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- en |
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metrics: |
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- mse |
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- r_squared |
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base_model: |
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- seyonec/ChemBERTa-zinc-base-v1 |
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--- |
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# ChemSolubilityBERTa |
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## Model Description |
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ChemSolubilityBERTa is a prototype designed to predict the aqueous solubility of chemical compounds from their SMILES representations. Based on ChemBERTa, a BERT-like transformer-based architecture, ChemBERTa pre-trained on 77M SMILES strings for molecular property prediction. We adapted ChemBERTa to predict solubility values by fine-tuning ChemBERTa with the ESOL (Estimated SOLubility) dataset, a water solubility prediction dataset of 1,128 samples. A user inputs a SMILES string, and the model outputs a log solubility value (log mol/L). |
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You can read the full paper [here](./01_ChemSolubilityBERTa.pdf). |
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## Fine-Tuning Details |
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- Pretrained model: `seyonec/ChemBERTa-zinc-base-v1` |
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- Dataset: ESOL (delaney-processed) |
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- Task: Aqueous solubility prediction (log mol/L) |
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- Number of training epochs: 3 |
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- Batch size: 16 |
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## How to Use |
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You can use the model to predict solubility for any molecule represented by a SMILES string: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("username/ChemSolubilityBERTa") |
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model = AutoModelForSequenceClassification.from_pretrained("username/ChemSolubilityBERTa") |
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smiles_string = "CCO" # Example for ethanol |
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inputs = tokenizer(smiles_string, return_tensors='pt') |
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outputs = model(**inputs) |
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solubility = outputs.logits.item() |
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print(f"Predicted solubility: {solubility}") |
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``` |
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## Citation and Usage |
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If you use ChemSolubilityBERTa in your research or projects, please cite the following: |
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@misc{ChemSolubilityBERTa, |
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author = {Farooq Khan}, |
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title = {ChemSolubilityBERTa: A Transformer-Based Model for Predicting Aqueous Solubility from SMILES}, |
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year = {2024}, |
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url = {https://huggingface.co/khanfs/ChemSolubilityBERTa} |
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} |
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## License |
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This model is licensed under the [MIT License](https://opensource.org/licenses/MIT). |
