The Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)(26) algorithms were selected to train the PCM models. After feature selection, as described in the molecular descriptors section, models were trained and tuned using a grid search of hyperparameters to optimize performance during cross-validation (Table ). Model Evaluation. After model training, predictions were evaluated using the Q 2 (27) metric for the cross-validation and R 2 for the external test set. Additionally, the following performance metrics were calculated and reported for both the cross-validation and test sets: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).