## 4 Discussion  
The development of new computational models demands a suitable goodness-of-fit, robustness, applicability domain, and predictability. The model developed in this study showed an R\({}^{2}\) value of 0.755, indicating a suitable fit for modeling DAT binding affinity. This means that the model explains more than 75% of the experimental variance. There was no overfitting in the model, thus representing a good fit with a minimum number of descriptors, because of the low value of the LOF parameter (0.195) and an R\({}^{2}\)\({}_{\text{adj}}\) = 0.729; the LOF is used to penalize the addition of descriptors in the model equation and should be as low as possible, while the R\({}^{2}\)\({}_{\text{adj}}\) value should be as similar as possible to the R\({}^{2}\) value, which the developed model compiled with. The correlation between the descriptors of the model was low since the K\({}_{\text{XX}}\) value (0.236) was small, indicating that there was no redundant information in the selected descriptors. In addition, the correlation between the descriptors and the response variable was appropriate, according to the \(\Delta\)K parameter (0.076), with a small error in the calculations of both training and prediction estimates (RMSE\({}_{\text{tr}}\) = 0.358; MAE\({}_{\text{tr}}\) = 0.276; \(s\) = 0.381). Figure 4 shows the values predicted by the model equation against the experimental LogK\({}_{\text{d}}\) values for both training and prediction series. As can be seen, most of the points are close to the line, and three compounds with atypical behavior in comparison to the rest of the molecules in the database were detected as outliers and not used to develop the model. Table 2 shows the predicted values for the compounds in the training set and the predictions in the LOO experiments as well as the residuals with respect to experimental values and leverage values (which were used to establish the applicability domain). The predictions are quite similar to the experimental values for most compounds. The molecular descriptors included in the model can be seen in Table 6 with the general interpretation of the molecular feature that describes each descriptor and the descriptor block to which they belong in the software. A critical aspect in QSAR studies is the definition of the AD of the models. Next, we used two approaches to establish the chemical space described by our model. First, we used a [PERSON] graph (Figure 5A): the AD is defined as the area at the left of a leverage threshold \(h^{\star}\) = 0.3571 and within \(\pm\)3 standard deviations. As shown in the figure, most compounds are within the applicability domain of the model. There is only one compound (49, belonging to the training set) with a leverage value (\(h\) = 0.2327) greater than the critical leverage (\(h^{\star}\)), although it shows the SD value within the limits; therefore, the prediction should be considered with caution. In addition, we used another approach available in QSARINS software, the Insubria graph (Figure 5B), which plots the leverage values against the predicted responses for each compound. The results agree with the previous approach, identifying the same compound outside the AD with a leverage value greater than the cutoff value, but in this approach two compounds in the prediction set (36 and 56) present LogK\({}_{\text{d}}\) values that were slightly overestimated; see Table 3 for details. To evaluate the robustness and stability of the model, several internal validation strategies were performed. First, an LOO cross-validation technique, which iteratively excludes a compound of the dataset: The variance explained in the prediction by LOO was Q\({}^{2}\)\({}_{\text{LOO}}\) = 0.680, with a small error in the predictions (RMSE\({}_{\text{cv}}\) = 0.409 and