for training each tree. The parameters that were optimized were the minimum number of samples per leaf and the number of trees. The minimum number of samples per leaf tested were 4, 6, 8, 10, 15, and 20. The tested number of trees were 50, 100, 150, 200, 250, 300, 350, 400, 500, 600, 700, 800, 900, and 1000. In addition, the random state for splitting the cross-validation set and selection of the features in the RF models for each tree was also varied with values of 1, 2, and 3. The accuracy of the cross-validation set for each possible combination of the minimum number of samples per leaf, number of trees, and random state was used for the optimization of the RF models. After obtaining the optimized models for the amide, UoA, and cocamide series, the applicability domains were assessed according to Section 'Applicability Domain Calculations'. Finally, for each r i series, the optimized model and applicability domain assessment were applied on the test set to evaluate the performance of the model on unseen data.