We first train a penalized LogP predictor using a subet of 250 SMILES extracted from ChEMBL, then cleaned and filtered following Baltruschat and Czodrowski approach . Extended Connectivity Morgan Fingerprints of radius 3 (ECFP6) and length 2048 were generated using RDKit , alongside their associated LogP values which were calculated using the penalized LogP oracle described in Appendix D. We employed Scikit-learn to fit a Random Forest Regressor (RFR) to all 250 ECFP6 vectors, optimizing hyperparameters (n estimators=300 and min samples split=2) to minimize the Mean Squared Error (MSE) across 5 crossvalidation folds. The resulting model achieved a Pearson linear correlation coefficient of 0.72 between true and predicted LogP values on a holdout test set of 600 SMILES from ChEMBL (Additional file 1: Figure ).