---
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.