## 2 Materials and Methods
### Dataset and Calculation of Topological Descriptors
It is important to highlight that all the topological descriptors mentioned until now are also known as graph-based indices (_GBI_) because they are numbers obtained from algebraic operations performed when a molecule is represented as a graph. We also calculated size-independent topological descriptors denoted as _NTD_; each of these was computed as the _TD_/_NB_ ratio, with _NB_ being the number of bonds in a molecule without counting bond multiplicity. We divided our dataset into two training (~75%) and test (~25%) sets. When doing so, \(\text{GI}_{50}\) values for each lung cancer cell line were sorted in increasing order. Then, for each lung cancer cell line, the first three molecules were labeled to belong to the training set while the fourth was assigned to the test set. The procedure was repeated until all the molecules reported for each lung cancer cell line were annotated to belong to one of the aforementioned sets. Following, we applied the Box-Jenkins approach to our dataset [35, 46, 63]:  
\[avg[GBI]ej=\frac{1}{n(ej)}\times\sum_{a=1}^{n(ej)}GBI_{a} \tag{1}\]  
In Equation (1), _GBI_ refers to any of the graph-based indices mentioned above, i.e., GBI is either _TD_ or _NTD_. In Equation (1), _avg[GBI]ej_ is a conditional average. Also, _n(e)_ represents the number of molecules labeled as active (in the training set), which were experimentally assayed by considering the same element of _ej_ (_ct_ or _mb_). This means that Equation (1) is applied to _ct_ and _mb_, separately. Notice that if _ej_ = _ct_; then, _avg[GBI]ej_ = _avg[GBI]ct_,and therefore, \(n(e)=n(ct)\). This means that the aforementioned definitions are applied to each type of lung cancer cell line (\(ct\)). Consequently, both \(n(ct)\) and \(avg[GBI]ct\) have nine different values each corresponding to the nine different lung cancer cell lines. The same procedure is used for the element \(mb\) (\(ej=mb\)); \(n(mb)\) and \(avg[GBI]mb\) have three different values each due to the presence of the three labels (mentioned above) associated with \(mb\). Following, the second mathematical formalism of the Box-Jenkins approach was applied to each molecule of the dataset:  
\[D[GBI]ej=\left[\frac{GBI-avg[GBI]ej}{Sdv[GBI]}\right]\sqrt{ps(ej)} \tag{2}\]  
In Equation (2), we have \(Sdv[GBI]\), which is the standard deviation calculated for each \(GBI\) descriptor by considering only the \(GBI\) values of the molecules contained in the training set. Also, \(ps(ej)\) is a probabilistic factor expressed as the \(n(ej)/N(ej)\) ratio, with \(n(ej)\) being as defined above and \(N(ej)\) being the number of molecules in the training set that were experimentally assayed by considering the same element of the condition \(ej\). As in the case of Equation (1), Equation (2) was also applied to the elements \(ct\) or \(mb\), separately. In the case of the multi-label graph indices \(D[GBI]ej\), they consider both the chemical structure and one of the aspects of the condition \(ej\) (either \(ct\) or \(mb\)); they measure how much a query molecule structurally and physicochemically deviates from a set of molecules tested under a defined element of the condition \(ej\) (\(ct\) or \(mb\)).