## 2 Results
### Prediction of the Biological Properties of the Compounds  
#### 2.3.1 In Silico Evaluation of the Molecular Mechanism of Action  
Based on 2D structural descriptors, the PASS application was used to calculate the probability of the target compounds **1 af**, **2a-f**, **3a**, **4a-f**, **5a-n**, and **6a-p** as being active (Pa) or inactive (Pi) on a large series of targets [66]. The analysis returned Pa values higher than the corresponding Pi values for 2097 pharmacological effects, of which 14 were directly related to antibacterial effects. The maximum and minimum predicted Pa values for the 49 compounds and the compounds with Pa values over 0.5 are displayed in Table 3.  
A total of 33 compounds presented Pa values over 0.3 for anti-infective effect, and 37 compounds were predicted to have antim mycobacterial effects with Pa value above 0.3.  
Figure 1 presents the plotted Pa values for anti-infective, antim mycobacterial, and antibacterial effects based on the main chemical scaffold.  
The prediction results indicated a clear correlation between the chemical scaffolds of the studied compounds and the potential to have an anti-effect, with \(N\)-acyl-\(\alpha\)-amino acids (scaffold 1) emerging as the most promising class (Figure 1b). In the case of antibacterial effects, the prediction indicated 4\(H\)-1,3-oxazol-5-one ring (scaffold 2) as the most favorable core structure (Figure 1d). The potential of the compounds to produce antim mycobacterial effects was not correlated with the chemical scaffolds (Figure 1c).  
The PASS application can be used to indicate a probable mechanism of action [67] of new compounds. Compounds **1a-d**, **2a**, and **2d** had significant Pa values for the inhibition of peptidoglycan glycosyltransferase, a valuable target for new antimicrobial therapies [68]. UDP-\(N\)-acetylmuramate-\(L\)-alanine ligase inhibitor (MurC) is a member of the Mur enzymes family and, similar to peptidoglycan glycosyltransferase, is involved in synthesis of peptidoglycan [69]. Compounds **1a-c**; **1e**,**f**; **2d**,**e**; and **3a** presented small but significant Pa values towards this possible mechanism.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target** & **Pa Max** & **Pa Min** &
\begin{tabular}{c} **Compounds with** \\ **Pa > 0.5** \\ \end{tabular} \\ \hline Anti-infective & 0.702 & 0.218 & **1a**, **1b**, **1c**, **1d**, **1e**, **1f**, **3a** \\ Antimycobacterial & 0.574 & 0.198 & **2c**, **2f**, **5c**, **5f** \\ Antituberculosis & 0.526 & 0.199 & **5c**, **5f** \\ Antibiotic glycopeptide-like & 0.403 & 0.083 & 0 \\ peptidoglycan glycosyltransferase inhibitor & 0.323 & 0.212 & 0 \\ Antibacterial & 0.312 & 0.168 & 0 \\ UDP-\(N\)-acetylmuramate-\(L\)-alanine ligase inhibitor (MurC) & 0.225 & 0.116 & 0 \\ Antibacterial, ophthalmic & 0.164 & 0.122 & 0 \\ Bacterial efflux pump inhibitor & 0.119 & 0.118 & 0 \\ Antispetic & 0.118 & 0.117 & 0 \\ Antibiotic & 0.106 & 0.106 & 0 \\ peptidoglycan beta-\(N\)-acetylmuramidase inhibitor & 0.093 & 0.067 & 0 \\ \(N\)-acetylmuramoyl-\(L\)-alanine amidase inhibitor & 0.083 & 0.054 & 0 \\ UDP-\(N\)-acetylmuramoylalanine-\(D\)-glutamate ligase inhibitor (MurD) & 0.079 & 0.059 & 0 \\ Bacterial leucyl aminopeptidase inhibitor & 0.064 & 0.051 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The probability (Pa) for the compounds to produce biological effects related to antibacterial action as predicted by the PASS application.