In this work we aimed to integrate ligand-based and structure-based information to arrive at a MATE1 inhibition model which can be used for IC50 prediction. We found that overall best predictivity was observed in the RF model (including ECFP4 and MM-GB/SA input variables) with an ROC-AUC of 0.833. On the one hand RF models have good precision and specificity; on the other hand MPNN models have good sensitivity. Additionally, fingerprint-based models performed better with increasing proximity to the training set, which was shown not to be the case for a MM-GB/SA regression model due to its physics-based nature. Via Structural Interaction Fingerprint analysis, we identified residues relevant for inhibitor are mostly the same with non-inhibitors including substrates, such as Gln49, Trp274, Tyr277, Tyr299, Ile303, Tyr306. This is consistent with the fact that the IC50 values of each inhibitor when using different substrates are similar, and practically this can release the experimental scientists from bothering of selecting substrates for MATE1. Hence we were in the current study able to build a fit-for-purpose classification model for MATE1 inhibitory activity based on dose-response data based on both ECFP4 fingerprints and MM-GB/SA score for the first time, which will be useful in practice to identify and eliminate more compounds related to MATE1-based DDI in the early drug discovery stages.