A machine learning-based tool for predicting PROTAC protein degradation activity.
## 📚 Table of Contents - [Data Curation](#-data-curation) - [Installation](#-installation) - [Usage](#-usage) ## 📝 Data Curation The code for data curation can be found in the Jupyter notebook [`data_curation.ipynb`](notebooks/data_curation.ipynb). ## 🚀 Installation To install the package, open your terminal and run the following command: ```bash pip install . ``` ## 🎯 Usage After installing the package, you can use it as follows: ```python 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`](src/run_experiments.py). ## 📜 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.