## 3 Results and Discussion
### Predicting the Anti-Lung-Cancer Activity of the Designed Molecules
We would like to reiterate (see the _\"The PTML-MLP Model\"_ subsection) that a molecule is considered a versatile anti-lung-cancer agent if the PTML-MLP model predicts it as active in 5 of the 9 experimental conditions _ej_. In the case of Table 5, the four designed molecules were predicted as versatile anti-lung-cancer molecules, which means that these molecules should have a predicted anti-lung-cancer activity with \(\text{GI}_{50}\leq 2000\) nM (cutoff value used to build the PTML-MLP model) against at least 5 out of 9 lung cancer cells. We would like to highlight that except for the molecule ASP-VALC-03, the other three molecules designed were predicted as active by the PTML-MLP model in all nine experimental conditions _ej_ (i.e., against the nine lung cancer cell lines). Structurally speaking, the fact that ASP-VALC-03 was predicted as active against a smaller number of lung cancer cell lines means that it is more hydrophobic (detrimental augmentation of the value of the descriptor _MGBI03_) than the other three molecules mainly because of the presence of the 2,6-difluorophenyl moiety. Also, because of the distribution of the polar atoms (nitrogen and oxygen), ASP-VALC-03 has a polar surface area that is unfavorably larger (_MGBI07_ and _MGBI14_) than that of the other three molecules. It is important to highlight that our PTML-MLP model does not predict the effect of the stereochemistry in the designed molecules. In any case, the prediction results depicted in Table 5 demonstrate that the procedure of designing the 2D patterns corresponding to the four molecules was correct. Besides the predictions performed by our PTML-MLP model, Table 5 contains the prediction results obtained by the CLC-Pred 2.0 webserver [78], which served as a second confirmatory tool for the anti-lung-cancer activity of our four designed molecules. The CLC-Pred 2.0 webserver can predict the anticancer activity of a query molecule against 60 different cancer cell lines. By using Bayesian models derived from multilevel atomic neighborhoods (a type of fragment-based similarity descriptors), CLC-Pred 2.0 predicts the non-additive probabilities of being active (\(P_{a}\)) and inactive (\(P_{i}\)) by considering different \(\text{GI}_{50}\) activity cutoffs (e.g., 100 nM, 10 nM, or 1 nM). In this sense, according to the description and interpretation of the prediction performed by CLC-Pred 2.0, any molecule with \(P_{a}>P_{i}\) is considered to be active [78]. In this study, we predicted the anti-lung-cancer activity of our four designed molecules by using the CLC-Pred 2.0 webserver and the cutoffs \(\text{GI}_{50}\leq 100\) nM and \(\text{GI}_{50}\leq 10\) nM. Because of the difference in terms of the machine learning approaches and molecular descriptors used, the predictions of anti-lung-cancer activity performed by CLC-Pred 2.0 and our PTML-MLP model are not necessarily expected to fully converge. However, since CLC-Pred 2.0 and our PTML-MLP model are both based on the same activity endpoint (\(\text{GI}_{50}\)) and the same assay protocol, some degree of agreement in the predictions should be expected. When using CLC-Pred 2.0, Table 5 shows that the four molecules designed were all predicted by considering the experimental condition _ej_08, i.e., against the lung cancer cell line named HOP-92 (see Table 3). In this sense, for the four molecules designed, \(P_{a}>P_{i}\) when considering the activity cutoff of \(\text{GI}_{50}\leq 100\) nM. Notice that the cutoffs used by CLC-Pred 2.0 (\(\text{GI}_{50}\leq 100\) nM and \(\text{GI}_{50}\leq 10\) nM) are considerably more rigorous than the one that was used to build our PTML-MLP model (\(\text{GI}_{50}\leq 2000\) nM). Therefore, the fact that our four designed molecules were predicted against HOP-92 to exhibit \(\text{GI}_{50}\leq 100\) nM theoretically confirms their highly potent anti-lung-cancer activity. For other experimental conditions _ej_, many predictions for molecules ASP-VALC-01 and ASP-VALC-02 appear with hyphens.