As only summary statistics were available in the literature reports of the baseline models, we trained a feature-based model to serve as a surrogate for the direct comparison of predictions. In each case, we reported two sets of results for feature-based models. First, to compare with NeuralFP, we paired ECFP features with various machine learning algorithms (random forests, gradient boosting, support vector machines, neural networks) and reported the lowest RM SE test . Further, we obtained an optimized feature-based model with a combinatorial search of molecular features (ECFP, Mordred, MACCS) and machine learning algorithms, where the best performing model was measured by RM SE test to represent the feature-based methods. A consistent 5-fold cross-validation split was defined for each task.