The RF model was implemented and calibrated using Scikit-learn. The hyperparameters were obtained through a 5-fold cross validation on the training set consisting of 80% of the compounds, using Optuna. Finally, a RF model was trained on the full training set and evaluated on the hold-out test set consisting of the remaining 20%. The trained random forest model had an ROC-AUC score of 0.966 and a Brier score of 0.003 on the test set. The activity for DRD2-target were predicted for all predicted products and USPTO products.