| --- |
| license: mit |
| tags: |
| - PROTAC |
| - drug-discovery |
| - cheminformatics |
| - regression |
| - xgboost |
| library_name: xgboost |
| pipeline_tag: tabular-regression |
| --- |
| |
| # PROTAC Synthesizability — XGBoost |
|
|
| An XGBoost regressor that predicts the **heavy-atom-count weighted synthesizability |
| score** (`hac_weighted_score`) of PROTAC molecules from SMILES. |
| Nested 5×5 scaffold cross-validation, Optuna tuning. **Mean CV R² = 0.565.** |
|
|
| ## Files |
|
|
| - `xgb_v3_final.ubj` — XGBoost booster |
| - `xgb_v3_final.skops` — fitted preprocessor + target transformer |
| - `xgb_v3_hparams.yaml` — hyperparameters |
|
|
| (`.ubj` and `.skops` are both required to load the model.) |
|
|
| ## Usage |
|
|
| Requires the project code: https://github.com/ribesstefano/PROTAC-Synthesizability |
|
|
| Install the package (editable) so `protac_synth` is importable, then: |
|
|
| ```python |
| from protac_synth.models.xgb.model import XGBoostRegressor |
| from protac_synth.chem_utils import ( |
| standardize_all, compute_fingerprints, compute_descriptors, |
| ) |
| |
| model = XGBoostRegressor.load("xgb_v3_final") # base path, no extension |
| smiles = ["O=C(O)c1ccccc1"] |
| |
| # featurization consumes standardized RDKit Mols, not raw SMILES |
| mols = standardize_all(smiles) |
| preds = model.predict( |
| smiles, |
| X_fp=compute_fingerprints(mols, model.fp_size, model.fp_radius), |
| X_desc=compute_descriptors(mols), |
| ) |
| ``` |
|
|
| Notes: |
| - `compute_fingerprints` / `compute_descriptors` take pre-standardized Mols |
| (from `standardize_all`), so each molecule is parsed and standardized once. |
| - Using `model.fp_size` / `model.fp_radius` guarantees the fingerprint settings |
| match those the model was trained with. |
|
|
| ## Dependencies |
|
|
| The `.skops` preprocessor and the RDKit descriptor set are **version-sensitive** — |
| loading under a different version can raise `InconsistentVersionWarning` or produce |
| a feature-count mismatch. Pin the versions the model was exported with: |
|
|
| - `scikit-learn==1.6.1` (the version the `.skops` pipeline was saved with) |
| - `rdkit` — pin the exact version used at training; the RDKit descriptor list |
| changes across releases, and a mismatch changes the descriptor column count. |
| - `skops`, `xgboost`, `numpy` — pin to the training environment. |
|
|
| ## License |
|
|
| MIT |
