Interestingly, only activities tested on GAT2 and GAT3 were predicted with acceptable performance, namely, R 2 of the test set is greater than 0.6, while LOTO validation on the rest of the SLC6 members was unsuccessful, as illustrated in Figure . GAT2 and GAT3 share a sequence identity above 93% in the binding pocket, which is all residues except for V122 in GAT2, corresponding to E140 in GAT3, as shown in Figure . Moreover, GAT2 and GAT3 have 242 and 290 activity values in the dataset, respectively, 210 of which are identical molecules tested on both targets. This could explain why the model has performed so well on these two targets, as the chemical space of molecules and targets has been covered by the training set when testing both targets. Therefore, in light of the current results, the PCM model is rendered incapable of providing reliable predictions of drug candidates for orphan SLC6 members in virtual screening scenarios. By analyzing the built-in measure of feature importance of the algorithms used to train the PCM models, it was apparent that one residue position ranked highest in all models across all splits, datasets, and descriptors, namely A173 in hSERT, corresponding to G149 and G153 in hNET and hDAT, respectively. While XGBoost has several feature importance metrics, we decided to focus on Gain and Total Gain as they relate to the features that most improved prediction accuracy. All indicators highlight the importance of the Z4 and Z2 descriptors of this position. While Z2 is associated with the volume and steric properties of amino acids, the interpretation of the Z4 descriptor is less interpretable.( ) Nevertheless, it corresponds to properties such as electronegativity and heat of formation.( ) This particular residue position in the monoamine transporters has been a topic of interest in various studies. Andersen et al. introduced point mutations into the serotonin transporter to mimic the norepinephrine transporter and found that an A173G point mutation resulted in the largest change in the Ki of citalopram against its transporter hSERT, with a 14-fold increase. Lynagh et. al suggest that V148 and G149 in hNET corresponding to I172 and A173 in hSERT are the two amino acid differences in the substrate binding sites of NET and SERT that cause a low protein-ligand complementarity. Goral et al. proposed that residues V148 and G149 in hNET, corresponding to I172 and A173 in hSERT, are initial structural determinants of compound binding to hNET. In a further study by Tu et al. , it was reported that I172, A173, and L443 in hSERT and their corresponding residues in hNET and hDAT are the non-conserved "warm spot" binding site residues that play an important role in inhibitor selectivity in the monoamine transporters due to their different conformations. Considering the GABA transporters subfamily, this position corresponds to a conserved asparagine residue that, to the best of our knowledge, does not seem to play a role in the binding of ligands to these transporters. However, this residue position could be important for the selectivity in the subfamily of monoamine transporters, considering the dataset imbalance. The dataset has a massive imbalance where more than 74% of the bioactivities belong to the monoamine transporters.