As a test of the importance for proper evaluation of the "understanding" of the models, training and testing sets were generated by random splitting of the full dataset, as commonly done, and the model retrained (entry 24, median MAE = 0.52). The apparent increase in accuracy on the testing set while no change was made to neither the algorithm nor parameters emphasizes the need to test models using sets dissimilar to the training set to truly evaluate the accuracy of these models rather than evaluating what the models have memorized (not learned). Full data is provided as supporting information. Models in column 1 (light blue background) are our reference models using the same conditions as in Figure . Column 20 highlights the revised reference models after the conjugation feature has been modified (see text). Cat. Var. stands for categorical variables. Bottom panel: features included (green)/excluded (red).