README.md

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.

Installing the Package

To install the package, run the following command:

pip install .

Running the Package

To run the package after installation, here is an example snippet:

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, I just wanted to create a separate and "less generic" repo for fast prototyping new ideas. Stefano.

Why haven't you trained on more (i.e., the whole) data? We did, and we might just need way more data to get better results...