| # PROTAC-Degradation-Predictor | |
| Predicting PROTAC protein degradation activity via machine learning. | |
| ## Data Curation | |
| For data curation code, please refer to the code in the Jupyter notebooks [`data_curation.ipynb`](notebooks/data_curation.ipynb). | |
| ## Installing the Package | |
| To install the package, run the following command: | |
| ```bash | |
| pip install . | |
| ``` | |
| ## Running the Package | |
| To run the package after installation, here is an example snippet: | |
| ```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='gpu', # Default to 'cpu' | |
| proba_threshold=0.5, # Default value | |
| ) | |
| print(f'The given PROTAC is: {"active" if active_protac else "inactive"}') | |
| ``` | |
| > If you're coming from my [thesis repo](https://github.com/ribesstefano/Machine-Learning-for-Predicting-Targeted-Protein-Degradation), I just wanted to create a separate and "less generic" repo for fast prototyping new ideas. | |
| > Stefano. | |