Besides the random forest (RF) model shown in the previous publication a number of other architectures were evaluated for the task of 19 F NMR chemical shift prediction. Lasso regression was chosen as a reasonable linear baseline. K-nearest neighbor (KNN) regression has been applied to this problem in the past and it was included here as a reference. Random forest and gradient-boosted trees (GBT) serve as similar model architectures that have already been used with some success. The XGBoost GBT implementation was used. Lastly, we also evaluated a model based on Chemprop as a deep-learning representative.