Since the ROC-AUC in 2-fold cross validation (CV) was 0.82, this model was considered to have high predictivity . However, the number of compounds was rather limited for the model to be utilized in practical drug discovery projects from the viewpoint of chemical space coverage. In 2013, a larger dataset of 910 compounds was used to predict percentage inhibition at 20 μM concentration, with 50% inhibition used as the threshold for a binary classification model 14 based on Random Forest (RF) with 21 Dragon descriptors . The ROC-AUC by multiple external datasets was 0.78, and this model hence also had high numerical predictivity. In 2015, a pharmacophore model was constructed using docked poses obtained from GLIDE . The dataset was the same as in the preceding study , and 42 inhibitors were used to construct the pharmacophore model and tested by another 42 inhibitors and 398 noninhibitors. Since the best balanced accuracy of this model was 0.59, the predictivity was numerically moderate; however, this research for the first time utilized structural information in MATE1 activity modelling . The most recent models also used structural information: In one study performed in early 2021, although there was no construction of predictive models, the authors built a homology model of hMATE1 from PDB entry 3MKT (the multidrug transport protein NorM from Vibrio cholerae) .