We next investigated the applicability domain of two RF models, based on only ECFP4 fingerprints, and based on both ECFP4 and MM-GB/SA scores,by 5-NN analysis using Tanimoto similarity of ECFP4 fingerprints. According to one practical implementation, if more compounds are predicted correctly in a higher Tanimoto similarity bin in a 5-NN analysis, we can say this model has an interpretable applicability domain, which we can use to estimate prediction confidence for a new compound in a given 5NN similarity bin . The results of this analysis are shown in Figure where it can be seen that in RF model using only ECFP4, the percentage of correctly predicted compounds increases according to the Tanimoto similarity, from 71% in the 0.1 to 0.2 of Tanimoto similarity bin (TSB), over 73% (0.2 to 0.3 of TSB) and 84% (0.3 to 0.4 of TSB), to 85% (more than 0.4 of TSB). The second results of this analysis are shown in Figure where it can be seen that in RF model using both ECFP4 and MM-GB/SA score, the percentage of correctly predicted compounds also increases according to the Tanimoto similarity, from 71% (0.1 to 0.2 of TSB), over 75% (0.2 to 0.3 of TSB) and 79% (0.3 to 0.4 of TSB), to 85% (more than 0.4 of TSB). Therefore based on these results, we can conclude that for the RF model, regardless of the explanatory variables, we see a correlation of model performance with proximity to the training set. Finally the result of MM-GB/SA regression model is shown in Figure where it can been that the percentage of correctly predicted compounds was largely independent of the Tanimoto similarity, ranging from 71% (0.1 to 0.2 of TSB), over 65% (0.2 to 0.3 of TSB), and 71% (0.3 to 0.4 of TSB) to 72% (more than 0.4 of TSB). Hence the performance of the MM-GB/SA regression model is independent of similarity to the training set, which is due to MM-GB/SA based on physics-based (instead of data-driven) model . From the practical viewpoint of drug discovery process, even if we do not have any experimental results, MM-GB/SA model secure us about 70% predictivity; we can say that according to the position of the chemical space of the new compounds we should select the kinds of models (data-driven, physics-based, or parallel use of them).