Using XGBoost, Random Forest regression, ANNs, and MPNNs, four models using a particular set of descriptors were trained (Figure , Table ). Our analysis focused on MAE and RMSE as important metrics to compare the performance of the models and descriptors (see SI). We evaluated the models' cross-validation results to determine how well they generalize to unseen data. We further investigated the subtleties of each model's configuration, taking into account how the quantity and variety of descriptors affected the complexity of the model. We started our investigation with XGBoost, due to its ability to capture complex, non-linear relationships and mitigate overfitting through built-in regularization. a Model includes the given number of descriptors; b Type of Fingerprint: c FGs = number of functional groups included; d Additional selected descriptors included. Details of the type functional groups as well as the uncertainties for MAE are described in the SI.