## 5 Future Perspectives of PTML Modeling for Antibacterial Discovery  
A recent review has been reported, containing detailed information on how some of the current antibacterial drugs act through different multi-target mechanisms of action [102]. However, the fact that none of the antibacterial agents mentioned in this report have been able to stop the rise of MDR indicates (a) their limited effectiveness against biomolecular bacterial targets (proteins) or biochemical pathways and (b) their chemical structures, which rely on limited chemotypes, enabling bacterial pathogens to adapt. All the sections discussed until now demonstrate the potentialities of PTML modeling in the context of MOOP for antibacterial discovery. Therefore, here we offer four elements that may improve the impact of PTML modeling toward the _in silico_ prioritization/generation (and future experimental validation) of versatile antibacterial agents. First, as discussed throughout the different subsections of the present review, PTML models have been applied to the simultaneous prediction of multi-target (multi-protein and/or multi-strain) antibacterial activity in the context of small organic molecules, bioactive peptides, and _MCNPs_. While these results open new horizons for multi-target antibacterial discovery, many chemistry domains remain unexplored and could constitute novel sources of multi-target antibacterial discovery. In this sense, PTML modeling could be applied to identify or design multi-target antibacterial agents from chemical classes such as micro-ribonucleic acids, aptamers, and dendrimers; these chemical families have emerged as novel sources of antibacterial agents [103; 104; 105; 106]. Furthermore, it could facilitate the discovery of hybrid chemotypes containing one or more moieties from these different chemical families. A second aspect is related to the potential of the PTML models to prioritize the search for multi-target/multi-strain antibacterial molecules. In the context of virtual screening, PTML models have the advantage of simultaneously predicting multiple multi-target antibacterial activity endpoints, as well as toxicity measures and pharmacokinetic profiles. However, as it occurs with any other computational approach or model, the predictions will be accurate to a certain extent because the chemical space contained in the datasets used to create the PTML models (although there can be tens or hundreds of thousands of data points/cases) will still be considerable smaller than the chemical space to be virtually screened. In this sense, in addition to the potential of performing virtual screening, we recommend the application of PTML models as tools for _de novo_ design. This can be accomplished by applying the FBTD approach to the PTML models; FBTD enables the direct physicochemical and substructural interpretation of the _MLDs_ (derived from topological indices) used as inputs in the PTML models [29; 59]. Following these interpretations, it is possible to quantitatively analyze the contribution of many molecular fragments to multiple endpoints and subsequently connect/fuse the most suitable one to design new molecules with the desired biological profiles [29; 59]. In the context of multi-target antibacterial discovery, the application of FBTD to PTML models would enable the rational design of molecules virtually exhibiting potent multi-target (multi-protein and multi-strain) inhibitory activity against different bacterial pathogens, as well as decreased toxicity and adequate pharmacokinetics. To the best of our knowledge, reference [77] is the first and only work reported in this area, which means that the potentialities of FBTD in MOOP for antibacterial discovery remain to be fully exploited. Third, although PTML models have been able to integrate chemical and antibacterial data at different levels of diversity and complexity, the availability of clinical data is a limiting factor preventing PTML models from reaching their full applicability.