Following the definition above, each compound in Dataset-Test finds its own five nearest neighbors in Dataset-CV and compute its corresponding average-similarity. Similarly, each compound in Dataset-CV also retrieves its own five nearest neighbors in Dataset-CV and calculates its corresponding average-similarity. Finally the histograms of the average-similarity for Dataset-Test and Dataset-CV are given in Figure S11 to address the applicability domain of our models. This average-similarity is used to reflect the closeness between the given compound and its neighboring compounds in the cross-validation dataset (Dataset-CV). If the average-similarity is close to 1, it means that the given compound can find very similar compounds in the training set, and the prediction for the given compound based on our models is not extrapolated and can be considered as a reliable inference. Nevertheless, in reality it is often very difficult for us to expect that the compound of user's interest can always find very similar neighboring compounds in our dataset. Thus, an appropriate threshold for the average-similarity should be defined based on Figure S11.