## 3 Data and Statistical Performance of PTML Models for Antibacterial Discovery
### PTML Modeling for Virtual Design of Versatile Antibacterialerial Peptides  
There is a plethora of reports devoted to accelerating the search for antibacterial peptides [80; 81; 82; 83; 84; 85; 86; 87]. However, most of these works are limited to relatively short peptides (usually \(\leq\)50 amino acids in the peptides' sequences), and the antibacterial activity is remarkably dependent on the composition of the peptides, particularly the presence and amount of positively charged amino acids (e.g., lysine, arginine, etc.) [80; 81; 82; 83; 84; 85; 86; 87]. Furthermore, antibacterial activity is often predicted generically without specifying the bacterial strains targeted by the peptides [80; 84; 85; 86; 87]. With such limitations, it is not possible to fully exploit the chemical diversity of peptides. In addition, although the presence and number of positively charged amino acids are beneficial to the increase of antibacterial activity, there is a detrimental effect in mammal cell lines (e.g., induction of hemolysis in erythrocytes) [88; 89]. In this sense, there have been two investigations focused on the use of PTML modeling for the design and prediction of antibacterial peptides that overcome the aforementioned disadvantages (Figure 4). The first of these studies developed a PTML model to identify bioactive peptides as multi-strain inhibitors of Gram-positive bacteria [90]. Before generating the PTML model, a dataset containing 2488 cases was considered. By using six _MLDs_ based on Kier-Hall connectivity indices derived from the peptides' amino acid sequences and LDA, the PTML achieved _Sens_ > 92% and _Spec_ > 94%. This work implemented for the first time two approaches that permitted the calculation of the quantitative contribution of an amino acid (in a defined position of the peptide's amino acid sequence) to the antibacterial activity across multiple  
Figure 4: Summarized workflow on PTML modeling for the discovery of multi-strain antibacterial peptides. For more information on details steps, please check Figure 1.  
Figure 3: General structures of the molecules that were designed by applying the FBTD approach to the PTML model. The designed molecules were predicted to virtually exhibit potent multi-strain antibacterial activity, low toxicity, and suitable pharmacokinetic properties. Here, \(X_{1}=-OH\), \(-CN\), \(-NH_{2}\), or \(-OCH_{3}\); \(X_{2}=-OH\), \(-CN\), or \(-NH_{2}\); \(X_{3}=-OH\) or \(-NH_{2}\); \(Y_{1}=-COOH\); \(Y_{2}=-OH\); \(Z_{1}=-OH\); \(Z_{2}=-NH_{2}\). bacterial strains. This approach provided deeper theoretical insights into the structural modifications that could enhance a peptide's multi-strain antibacterial activity. The second work was based on the creation of a PTML-LDA model to design antibacterial peptides as multi-strain inhibitors [91]. The dataset used to develop the model included 3592 cases of peptides with assay information focused on activity against Gram-negative pathogens, cytotoxicity in diverse mammalian cell lines, and hemotoxicity in erythrocytes from various animal species. The four _MLDs_ used as inputs for the PTML-LDA model were derived from the Broto-Moreau autocorrelations calculated from the peptides' amino acid sequences. For this PTML-LDA model, _Acc_ > 97% was obtained. A similar procedure to the one described in the first report mentioned above was implemented for the calculation of quantitative contributions of amino acids to the increase in the activity and the diminution of the cytotoxicity and hemotoxicity. The physicochemical and sequence-based interpretation of the PTML-LDA model enabled the design of ten new peptides; these were predicted by the PTML-LDA model to display high multi-strain activity, as well as reduced cytotoxicity and hemotoxicity. Accelerating Multi-Strain Antibacterial Discovery of Metal-Containing Nanoparticles via PTML Modeling  
The role of MCNPs in drug research has been discussed in a series of recent reports, with applications in drug delivery, direct therapeutic action, or diagnostic agents [92; 93; 94; 95; 96].