To achieve integration of drug PK in de novo drug generation, we utilized public datasets to train QSPR models for the prediction of drug PK and potency. After data collection and pre-processing, a grid search was performed on different features, feature filters, model algorithms, and hyperparameters (Supplementary Table ). For all models, the difference in performance on the 20% holdout set and cross-validation test sets is small (<0.15 on the R 2 ) (Figure ). This shows that there is no large overfitting effect present due to feature and hyperparameter selection. The importance of individual features was analyzed using permutation importance (Supplementary Figure ).