## 4 Conclusions  
The aim of the present work was to develop a QSAR model suitable for the classification of 228 pharmaceutical drugs according to their GIT permeability at different pH levels. The starting point of this study was a previous paper published by [PERSON] et al., where the same aim was achieved by logistic regression followed by decision trees. The present research represents an equally efficient solution for the same purpose, requiring a less substantial computational effort. In general, the predictive capability obtained is equal to or greater than that achieved by [PERSON] et al. Furthermore, the application of a variable selection method has allowed highlighting which molecular descriptors are the most relevant for the classification of compounds. In particular, this further step has indicated that the predictors associated with the 2D and 3D structures of the investigated compounds are the most significative features for discriminating drugs into low- and high-permeable.  
Conceptualization, A.B. and F.M.; methodology, A.B. and F.M.; software, A.B. and F.M.; validation, A.B. and F.M.; formal analysis, L.M. and F.M.; investigation, A.B. and L.M.; resources, F.M.; data curation, L.M.; writing--original draft preparation, A.B. and M.F.; writing--review and editing, A.B., M.F. and F.M.; visualization, A.B. and M.F.; supervision, F.M.; project administration, F.M.; funding acquisition, F.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**pH**} & \multirow{2}{*}{**n Var**} & \multirow{2}{*}{**LVs**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**CCR (\%)**} \\ \cline{5-8}  & & & & & **Class High** & **Class Low** \\ \hline \multirow{2}{*}{3} & CV & \multirow{2}{*}{41} & \multirow{2}{*}{12} & 96.73 & 91.43 & 98.30 \\  & Prediction & & & 80.00 & 65.22 & 86.54 \\ \hline \multirow{2}{*}{5} & CV & \multirow{2}{*}{23} & \multirow{2}{*}{6} & 96.73 & 94.44 & 97.98 \\  & Prediction & & & 84.49 & 81.48 & 87.50 \\ \hline \multirow{2}{*}{7.4} & CV & \multirow{2}{*}{47} & \multirow{2}{*}{6} & 97.38 & 97.56 & 97.18 \\  & Prediction & & & 96.08 & 92.16 & 100.00 \\ \hline \multirow{2}{*}{9} & CV & \multirow{2}{*}{47} & \multirow{2}{*}{7} & 98.68 & 98.85 & 98.46 \\  & Prediction & & & 92.36 & 87.50 & 97.22 \\ \hline \multirow{2}{*}{IP} & CV & \multirow{2}{*}{34} & \multirow{2}{*}{5} & 99.35 & 99.06 & 100.00 \\  & Prediction & & & 93.86 & 100.00 & 87.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PLS-DA of the five investigated datasets after variable selection by CovSel: Number of selected descriptors (nVar), number of latent variables (LVs), overall accuracy and correct classification rate (CCR%) on the training data in cross-validation (CV) and on the test set in prediction.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available at QsarDB repository, reference number [13].  
**Conflicts of Interest:** The authors declare no conflict of interest.