| --- |
| library_name: sklearn |
| tags: |
| - sklearn |
| - rdkit |
| --- |
| # extra-trees-morgan1024-physchem |
|
|
| Features: morgan1024, physchem |
|
|
| Model: the original LazyPredict ExtraTrees model, a sklearn Pipeline |
| (ColumnTransformer + ExtraTreesRegressor, 100 trees). Unpickling |
| `model.pkl` requires `lazypredict` (listed in `requirements.txt`). |
| Inference uses `infer.py`, which builds a pandas DataFrame with |
| columns `feature_0`..`feature_1029` (Morgan 1024 first, then the 6 |
| physchem descriptors in the order MolWt, MolLogP, TPSA, NumHDonors, |
| NumHAcceptors, NumRotatableBonds). |
|
|
| ## Metrics (original LazyPredict run) |
|
|
| Test set (80/10/10 split, seed 42): RMSE 0.4449, MAE 0.1437, R2 0.8668. |
| FDA-12: RMSE 0.9637, MAE 0.6794 (11 of 12 compounds with a wetlab pIC50). |
|
|
| See `metrics.csv` and `fda.csv` in this repo for the full tables. |
|
|
| ## Inference |
|
|
| ```python |
| from infer import load_model, predict_smiles |
| model = load_model("model.pkl") |
| print(predict_smiles(model, ["CCO"])) |
| ``` |
|
|
| See `infer.py`, `config.json` and `requirements.txt` in this repo. |
|
|
