Applicability domain calculations were used to assess whether the training data, used in the random forest models, sufficiently covered the variable space for new chemicals on which the models need to be applied. This was done through leverage calculations of a chemical with the entire training set, yielding a distance of that chemical to the training set. Fingerprints are used to calculate this distance, meaning that lower distance values are obtained for compound that are structurally more similar to the training set than compounds with high leverage values. Equation shows how the leverage is calculated, where X is the training data matrix and x i is the sample vector, both containing the 2DAPC and reduced PubChem fingerprints for our models. To set a threshold for this, the leverage was calculated for all training samples with the entire training set of a model, yielding values between 0 and 1.