## 3 Results  
### Prediction of the BBB Permeation  
The first example concerns the prediction of the BBB permeation based on [PERSON]'s dataset, which includes 415 compounds, 276 permeants and 139 non-permeants [11]. Among the 129 computed descriptors, the variable selection collected only nine attributes: they include size/shape descriptors (such as mass, or volume), the log P as computed by the Molecular Lipophilicity Potential (MLP) approach [27], three Kier-Hall E-state descriptors [28] and the total charge, which encodes for the quaternary ammonia molecules included in the dataset. The selected descriptors are in agreement with the above-mentioned BBB score [15] and confirms that molecular size and polarity play a crucial role. The final model obtained by the random forest algorithm shows satisfactory results as confirmed by the 83.4% of correctly predicted molecules with a Matthews correlation coefficient (MCC) equal to 0.620 and an area under the curve (AUC) equal to 0.865. Table 1 summarizes the performances for each class, where 0 and 1 indicate non-permeant and permeant molecules, respectively.  
The obtained results are in line with those reported by [PERSON] in the reference paper. In detail, [PERSON] developed the predictive model by support vector machine (SVM) with recursive feature elimination (RFE) [29] reaching an 83.7% of correctly predicted molecules with an MCC value of 0.645. The major difference between the two models is that the here-proposed model includes only descriptors, while that of [PERSON] required 35 attributes. In detail, the here-proposed model does not include quantum mechanical parameters, thus markedly reducing the computational costs without affecting the overall predictive power. Finally, both models comprise Kier-Hall E-state descriptors, but the here-proposed model includes only three descriptors, while the SVM + RFE model requires 17 Kier-Hall E-state descriptors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **TP Rate** & **FP Rate** & **Precision** & **F-measure** & **PRC Area** \\ \hline
**0** & 0.705 & 0.101 & 0.778 & 0.740 & 0.787 \\
**1** & 0.899 & 0.295 & 0.858 & 0.878 & 0.912 \\
**mean** & 0.834 & 0.230 & 0.831 & 0.832 & 0.870 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Predictive performances reached by the model for the blood–brain barrier (BBB) permeation (0 and 1 indicate non-permeant and permeant molecules, respectively).  
Figure 1: The VEGA ZZ graphical user interface for Tree2C.  
Along with the source codes/script routinely generated by Tree2C, the predictive model was also used to develop a web service based on a PHP script generated by Tree2C ([[https://www.ddl.unimi.it/vegaol/bbbp.htm](https://www.ddl.unimi.it/vegaol/bbbp.htm)]([https://www.ddl.unimi.it/vegaol/bbbp.htm](https://www.ddl.unimi.it/vegaol/bbbp.htm))) in order to predict the BBB permeation. As depicted by Figure 2, this requires as input the molecular structure in one of the file formats supported by VEGA, or only the molecule name. In this last case, the structure is directly downloaded from PubChem by its PUG REST interface [30]. The VEGA engine then calculates the molecular descriptors required to perform the prediction by the decision tree encoded by the PHP code.