The best data preprocessing/model combination was used on different bootstraps of the whole dataset to assess the applicability domain of the trained models and further investigate the model performance. For the bootstrapping, the dataset was split using a random split or cluster split (20%) for fifty replicas each. The applicability domain of each replicate was estimated using the TOPKAT Optimum Predictor Space (OPS) as implemented in MLChemAD. Briefly, here the OPS was defined as the orthogonal projections via eigenvalue decomposition of the min-max normalized dataset. A new observation is considered outside of the applicability domain if the Mahalanobis distance of the observation in the OPS is larger than 1.5 times the number of dimensions divided by the number of observations.