In subsequent work performed in late 2021, Alphafold2 was used to build a MATE1 protein structure, which resulted in a pharmacophore classification model with an accuracy 0.51 . In this research the authors also built a machine learning classification model using Neural Networks (with an accuracy of 0.83) and Support Vector Machines (with an accuracy 0.75). Although the accuracies were relatively high numerically, the thresholds of active/inactive were derived from percentage inhibition values (here of less than 10% and more than 50% inhibition), which is on the one hand not a quantitative inhibition value, and performance values were likely inflated due to the range of activities in between the thresholds not considered in the model. Also, the compounds used for modeling were limited to 58 tyrosine kinase inhibitors, which would not allow for model application across wider chemical space. Based on these related works, all of them are classification models; however, in practice often two-class models are not sufficient for decision making, which also agrees with regulatory guidelines . To sum up these related works, on the one hand we can find the difficulty of the construction of regression models, assuming due to the shortage of reliable experimental data. On the other hand, the progress of utilizing structural information can be seen as a promising method for the prediction of inhibitory activity of MATE1.