## 3 Results and Discussion
### Structure-Activity Relationships  
The PCA approach was employed to explore the relationships between observed values of analytical and physicochemical parameters for the **6a-g** set (\(m/z_{\text{measured}}\), log \(\varepsilon_{\text{2 (Ch-T)}}\), and log \(k_{\text{w}}\)), in silico descriptors (\(I_{\text{CPI}}\), \(I_{\text{MPI}}\), \(I_{\text{CADT}}\), \(I_{\text{B}}\), \(I_{\text{P}}\), \(I_{\text{T}}\), and \(I_{\text{QTp}}\)) related to **5a-g**, and the calculated data based on in vitro biological screening of the compounds **6a-g**, i.e., log (1/MIC (M)) and log (1/IC\({}_{\text{50}}\) (M)), respectively. Firstly, all analyzed indices were precisely transformed using several data pretreatment techniques [87, 88, 89]. Secondly, a scree plot was constructed to correctly set a number of relevant principal components (PCs). The scree plot described adequately the relationships between calculated eigenvalues (\(\lambda_{\text{e}}\)) and number of PCs. The Kaiser-Guttman rule [50], as the most common stopping rule in PCA, aimed to limit the number of PCs--only PCs with \(\lambda_{\text{e}}\) > 1.00 were considered. The PC 1, PC 2, and PC 3, which indicated the mathematical possibility of the existence of \"real\" parameters, accounted for 86.84% of the total variance, and their contributions were as follows: PC 1 (64.85%, \(\lambda_{\text{e}}\) = 9.73), PC 2 (15.00%, \(\lambda_{\text{e}}\) = 2.25), and PC 3 (6.99%, \(\lambda_{\text{e}}\) = 1.05). The relationship between PC 1 and PC 2 allowed for the distribution of **6a-g** into several subcategories. The sum of these PCs expressed the majority of the data variability (79.85% in total), as shown in Figure 5(a),b. The PC 1 served as a suitable component to separate quite clearly the antimycobacterially active and inactive derivatives. The subgroup defined by PC 1 \(\leq-\)0.286 included **6e-g**, which were relatively effective (MIC \(\leq\) 30.36 \(\upmu\)M) against all in vitro screened mycobacteria. The substances with MIC \(\geq\) 64.73 \(\upmu\)M against at least one mycobacterial strain **(6a-d)** were described with PC 1 \(\geq\) 0.481. The lower PC 1 descriptor indicated more active antimicrobial compound. On the other hand, higher PC 1 parameter was connected with lower activity of a derivative. The combination of both negative PC 1 and PC 2 (Figure 5(a),b) can be used to characterize the most active molecule, **6g** (PC 1 = \(-\)6.365 and PC 2 = \(-\)1.817). The PC 1 \(\geq\) 2.988 together with PC 2 \(\geq-\)1.468 defined antimycobacterially inactive compounds (**6a** and **6c**). Other differences in the compounds' properties mirrored in both PC 1 and PC 2 as well as other PCs are provided in the Supplementary Materials (see Table S5). The distance between particular variables onto a plane and circle of correlation (Figure 5(a),b) expressed the quality of their 2D representation. The loadings of given variables (i.e., observed and extrapolated analytical and physicochemical descriptors, the data resulted from in vitro biological evaluations as well as parameters from the PASS Online predictor) defined the contribution (\"measure\") of each original variable to the chosen PCs [51]. These loadings are represented by color-coded vectors labeled following their position in a 2D score plot (Figure 5(a)). Figure 6: Graphical presentation of SARs for the **6a–g** set: (**a**) two-dimensional (2D) score plot, which shows the (i) principal component 1 (PC 1) and 2 (PC 2) scores; (ii) loadings of particular variables; (**b**) 2D mapping of loadings of variables, which indicated their (i) positions within a circle of correlation; (ii) relationships with both PC 1 and PC 2.