PROTAC-Degradation-Predictor
A machine learning-based tool for predicting PROTAC protein degradation activity.
📚 Table of Contents
📝 Data Curation
The code for data curation can be found in the Jupyter notebook data_curation.ipynb.
🚀 Installation
To install the package, open your terminal and run the following command:
pip install .
🎯 Usage
After installing the package, you can use it as follows:
import protac_degradation_predictor as pdp
protac_smiles = 'CC(C)(C)OC(=O)N1CCN(CC1)C2=CC(=C(C=C2)C(=O)NC3=CC(=C(C=C3)F)Cl)C(=O)NC4=CC=C(C=C4)F'
e3_ligase = 'VHL'
target_uniprot = 'P04637'
cell_line = 'HeLa'
active_protac = pdp.is_protac_active(
protac_smiles,
e3_ligase,
target_uniprot,
cell_line,
device='cuda', # Default to 'cpu'
proba_threshold=0.5, # Default value
)
print(f'The given PROTAC is: {"active" if active_protac else "inactive"}')
This example demonstrates how to predict the activity of a PROTAC molecule. The is_protac_active function takes the SMILES string of the PROTAC, the E3 ligase, the UniProt ID of the target protein, and the cell line as inputs. It returns whether the PROTAC is active or not.
📈 Training
The code for training the model can be found in the file run_experiments.py.
📜 License
This project is licensed under the MIT License - see the LICENSE file for details.