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
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.
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from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Delower/dpp4-pic50-extra-trees", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html