## 3 Results and Discussion
### The PTML-MLP Model
Notice that Table 2 contains information regarding both training and test sets, particularly, the number of compounds annotated as active and inactive (\(N_{\text{Active}}\) and \(N_{\text{Inactive}}\), respectively). It can be seen that the number of molecules correctly classified as active (\(CC_{\text{Active}}\)) and inactive (\(CC_{\text{Inactive}}\)) is relatively high, which translates into \(Sn\) values above 77% and \(Sp\) values higher than 82%. In addition, the \(MCC\) values are closer to one, which indicates a relatively strong correlation between the observed activity values \(ALC(e)\) and their corresponding predicted counterparts _Pred[ALC(e)]_. Detailed information on the performance of the PTML-MLP model can be found in Supporting Information S2. A great benefit of the PTML-MLP model developed in this work is that the anti-lung-cancer activity can be predicted by considering important biological aspects of the experimental conditions \(ej\), which comprise the type (\(cf\)) and the mutability capacity (\(mb\)) of each lung cancer cell line. Thus, the PTML-MLP model can predict anti-lung-cancer activity by considering nine different experimental conditions \(ej\) (Table 3). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Code a** & **Symbol** & **Definition b** \\ \hline \multirow{2}{*}{\(MGBI13\)} & \multirow{2}{*}{\(D[Ne(C)5]mb\)} & Multi-label index derived from the normalized bond-based connectivity of order five focused on cluster \(SG\). This depends on the structure of the molecule and the cell’s mutability. \\ \hline \multirow{2}{*}{\(MGBI14\)} & \multirow{2}{*}{\(D[NSM(Psa)2]mb\)} & Multi-label index derived from the normalized bond-based spectral moment of order two weighted by the polar surface area. This depends on the structure of the molecule and the cell’s mutability. \\ \hline \multirow{2}{*}{\(MGBI15\)} & \multirow{2}{*}{\(D[NSM(Mo)1]mb\)} & Multi-label index derived from the normalized bond-based spectral moment of order one weighted by molar refractivity. This depends on the structure of the molecule and the cell’s mutability. \\ \hline \multirow{4}{*}{\({}^{\text{a}}\) Codes for the \(D[GBI]ej\) descriptors; for the sake of simplicity, we will use these codes throughout the entire manuscript. b For each \(D[GBI]ej\) descriptor containing the symbology “\(SM\)”, the order (denoted in the Materials and Methods section as \(k\)) is the maximum number of bonds (without counting bond multiplicity) present in a defined fragment. At the same time, for each \(D[GBI]ej\) descriptor containing the symbols “\(X\)”, “\(X\)”, or “\(e\)”, the order (denoted in the Materials and Methods section as \(m\)) indicates the exact number of bonds (without counting bond multiplicity) present in a defined fragment. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\) \begin{table}
\begin{tabular}{c c c} \hline \hline
**SYMBOLS a** & **Training Set** & **Test Set** \\ \hline \(N_{\text{Active}}\) & 2315 & 771 \\ \hline \(CC_{\text{Active}}\) & 1850 & 597 \\ \hline \(Sn\) & 79.91\% & 77.43\% \\ \hline \(N_{\text{Inactive}}\) & 3220 & 1073 \\ \hline \(CC_{\text{Inactive}}\) & 2826 & 883 \\ \hline \(Sp\) & 87.76\% & 82.29\% \\ \hline \multicolumn{2}{c}{\(MCC\)} & 0.680 & 0.596 \\ \hline \hline \end{tabular}  
* \(N_{\text{Active}}\)—molecules labeled as active; \(N_{\text{Inactive}}\)—molecules annotated as inactive; \(CC_{\text{Active}}\)—molecules correctly classified as active; \(CC_{\text{Inactive}}\)—molecules correctly classified as inactive; \(Sn\)—sensitivity (percentage of molecules correctly classified as active); \(Sp\)—specificity (percentage of molecules correctly classified as inactive); \(MCC\)— Matthews’ correlation coefficient.  
\end{table} 
Table 2: Global statistical performance of the PTML-MLP model. From Table 3, one can also see that the PTML-MLP model can be employed to predict the anti-lung-cancer activity of any molecule in a consensus manner.