library_name: transformers
tags:
- chemistry
- biology
- cheminformatics
- materials science
license: mit
language:
- en
metrics:
- mse
- r_squared
base_model:
- seyonec/ChemBERTa-zinc-base-v1
ChemSolubilityBERTa
Model Description
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). You can read the full paper here.
Fine-Tuning Details
- Pretrained model:
seyonec/ChemBERTa-zinc-base-v1 - Dataset: ESOL (delaney-processed)
- Task: Aqueous solubility prediction (log mol/L)
- Number of training epochs: 3
- Batch size: 16
How to Use
You can use the model to predict solubility for any molecule represented by a SMILES string:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("username/ChemSolubilityBERTa")
model = AutoModelForSequenceClassification.from_pretrained("username/ChemSolubilityBERTa")
smiles_string = "CCO" # Example for ethanol
inputs = tokenizer(smiles_string, return_tensors='pt')
outputs = model(**inputs)
solubility = outputs.logits.item()
print(f"Predicted solubility: {solubility}")
Citation and Usage
If you use ChemSolubilityBERTa in your research or projects, please cite the following:
@misc{ChemSolubilityBERTa, author = {Farooq Khan}, title = {ChemSolubilityBERTa: A Transformer-Based Model for Predicting Aqueous Solubility from SMILES}, year = {2024}, url = {https://huggingface.co/khanfs/ChemSolubilityBERTa} }
License
This model is licensed under the MIT License.