## 3 Data and Statistical Performance of PTML Models for Antibacterial Discovery
### PTML Modeling for Virtual Design of Versatile Antibacterialerial Peptides
In the context of multi-target (multi-strain) antibacterial drug discovery, MCNPs have demonstrated great potential due to their unique mechanisms of action, which include (but are not limited to) the adherence to the surface of the bacterial membranes/cell walls, cell penetration and posterior interaction with cell organelles and biomolecules, and induction of oxidative stress and cytotoxicity-related processes [97]. In the discovery of MCNPs for multi-strain antibacterial solutions, PTML modeling has prioritized the search for seemingly potent and versatile antibacterial MCNPs by optimizing experimental aspects such as nanoparticle size and external morphology (shape), the presence and chemical structure of the coating agents and/or antibacterial molecules to synergistically enhance antibacterial activity, and the chemical composition of the metal-containing cores (Figure 5). The first of these works on multi-strain antibacterial discovery reported a pair-based PTML model to predict the inhibitory activity of MCNPs against both Gram-positive and Gram-negative bacterial strains [98]. This work considered not only the experimental aspects mentioned but also other elements, such as the conditions employed to measure the nanoparticle size and the assay times under which the different bacterial strains were exposed to MCNPs. The dataset used here involved 69,231 cases (MCNPs-MCNPs pairs), and through the use of six _MLDs_ (derived from physicochemical properties and topological indices), the pair-based PTML model achieved _Sens_ > 98% and _Spec_ > 97%. To demonstrate the usefulness in prioritizing the identification of MCNPs with multi-strain antibacterial profiles, the pair-based PTML model was employed to predict a previously tested copper-silver nanoalloy of different sizes and chemical compositions. The pair-based PTML model was applied to predict the multi-strain antibacterial activity of a previously tested copper-silver nanoalloy with varying sizes and chemical compositions. Another work was developed to enable the identification of molecule-MCNP antibacterial combinations [99]. This study involved a very large dataset containing more than 165,000 and 300 assays performed on organic molecules and MCNPs, respectively. The antibacterial activity endpoints were determined for multiple pathogenic bacterial strains. The _MLDs_ encompassed all the experimental aspects mentioned in the first work, plus additional elements associated with the chemical structures of the organic molecules present in the molecule-nanoparticle combinations. Different PTML models based on LDA, ANN, BN, and KNN were created; all the PTML models exhibited _Sens_ > 73% and _Spec_ > 88%. Due to its simplicity combined with good statistical performance, the PTML-LDA model was chosen to perform different simulations, including one with a prediction accuracy of 100% for 80 molecule-nanoparticle antibacterial combinations. A third report was carried out to search for MCNPs with multi-strain antibacterial activity by building diverse PTML models [100]. The dataset used in this work was formed by 5327 cases containing assays of 300 experimentally tested MCNPs against at least 1 out of 34 bacterial strains. As in the other reports mentioned above, the present research also considered multiple experimental aspects (coatings, measures of activity endpoints, etc.). Using _MLDs_ derived from Shannon entropy, the best linear PTML model, based on BLR, achieved _Sens_ > 79% and _Spec_ > 99%. Among non-linear models, the PTML-RF model outperformed others, achieving _Sens_ and _Spec_ values above 98%. A peculiarity of this investigation is that the information on the bacterial strains was numerically introduced in the PTML models in the form of bacterial metabolic reaction networks (MRNs). This factor enabled the prediction of the multi-strain antibacterial activity of MCNPs against new bacterial strains not present in the original dataset used to build the PTML models.