In this study considering both the importance of the exact values from IC50 or Ki data and the difficulty of the constructing of regression model, we hence built in silico models for the classification with the practical threshold of the inhibitory activity (10 μmol/L) against MATE1 using three different types of information to explore their information content, namely a MM-GB/SA model by itself, a machine learning model based on ligand chemical descriptors, and finally a machine learning model utilizing both ligand chemical descriptors and MM-GB/SA scores.