## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline
**Descriptor** & **Structural Feature Described** & **Descriptor Block** \\ \hline O-058 & Hydrogen bonding capacity & (Atom-centered fragments) = O \\ CATS2D\_03\_AL & Lipophilicity & CATS2D Acceptor-Lipophilic at lag 03 \\ CATS2D\_08\_LL & Lipophilicity & CATS2D Lipophilic-Lipophilic at lag 08 \\ B08[C-N] & Presence/absence of C-N at topological distance 8 & 2D Atom pairs \\ \hline \end{tabular}
\end{table} 
Table 6: Molecular descriptors selected in the obtained model. MAE\({}_{\text{cv}}\) = 0.316); these results are shown in Figure 6A. Second, an internal validation by LMO was performed, which was developed by leaving out 30% of the dataset to study the behavior of our model. The value obtained for this experiment (Q\({}^{2}\)LMO = 0.648) was like the value previously achieved in LOO, although it is important to notice that this technique gives better results with larger databases. The Q\({}^{2}\)LMO values (_red circles_) were similar to each other and distributed around the Q\({}^{2}\) of the model (blue circle), corroborating its good fit and stability (see Figure 6B). In the _Y-scrambling_ experiment, the absence of change in correlation for the model was checked. The expected behavior is that both values of R\({}^{2}\)Y-scr and Q\({}^{2}\)Y-scr (for each iteration) should differ appreciably from the R\({}^{2}\) and Q\({}^{2}\) values of the model. Both values of R\({}^{2}\) and Q\({}^{2}\) for every iteration are shown in Figure 6 (_yellow_ and _red circles_, respectively), and their averages were R\({}^{2}\)Y-scr = 0.098 and Q\({}^{2}\)Y-scr = -0.174, quite far from the respective values of these parameters for the model (_light_ and _dark blue_ for R\({}^{2}\) and Q\({}^{2}\), respectively), confirming its validity. To evaluate the model's ability to predict new compounds, an external validation was also performed. The procedure was carried out by applying the model equation developed with the training set to the set of external predictions. Based on the results with the external set, we can say that the model has a good predictive power (R\({}^{2}\)ext = 0.709), with lower value of 0.339 for RMSE\({}_{\text{ext}}\). As can be seen in Figure 5A, all compounds in the prediction set are within the applicability domain of the model. Table 3 shows the predicted values for the compounds in the prediction set and the residuals with respect to experimental values and the leverage values. The predicted values are quite similar to the experimental values, demonstrating the predictive power of the developed model. The validated model was then used to predict the affinity for DAT of new molecules of interest. For this, we selected a set of eleven compounds with different actions over the CNS; among the activities of these compounds, we can find psychostimulant, centrally acting sympathomimetic agent, anorectic psychostimulant, centrally acting anticholinergic, a radiopharmaceutical used to diagnose Parkinsonism, nasal decongestant for systemic use, and psychedelic. This feature ensures that they can reach the DAT and makes the evaluation of these compounds in virtual screening more interesting. This group of compounds was evaluated with the computational model and assessed by docking to estimate the binding affinities for the DAT. Among the compounds selected for virtual screening, \({}^{123}\)I-Ioflupane (one of the most widely used DAT radiotracers) was chosen, which achieved a good prediction for the LogK\({}_{\text{d}}\) value of 3.088; taking this as a point of reference, we highlighted the compounds with affinity for DAT potentially better than \({}^{123}\)I-Ioflupane. We found that Modafinil and Armodafinil seem to be the better compounds showing the lowest LogK\({}_{\text{d}}\) value of 2.386, followed by Benztropine with 2.449 and Procaine with a value of 2.713, slightly lower than the 3.088 shown by \({}^{123}\)I-Ioflupane. The results obtained with the QSAR model fully agree with those obtained in the docking experiments, which are described below.