Applicability domain of models are defined quantitatively by the average-similarity between the given test compound and its five nearest neighbors in Dataset-CV. If the given test compound is close to its neighbors with a larger average-similarity, it means that the chemical space of this given compound is covered by Dataset-CV, thus the prediction is probably interpolated from Dataset-CV, which gives more reliable estimation. According to the average-similarity histograms (Figure S11) of Dataset-CV and Dataset-Test, the compounds in the test set (Dataset-Test) are fully covered by the dataset for the cross-validation (Dataset-CV), thus the given compound with average-similarity higher than 0.1 is assumed to be within the applicability domain of our models according to Figure S11. If the compound is not located within the applicability domain of our models, the prediction for this compound is probably extrapolated from Dataset-CV and consequently is not confident.