## 3 Data and Statistical Performance of PTML Models for Antibacterial Discovery
### PTML Models for Antibacterial Discovery of Small Organic Molecules  
Small molecule drug discovery is the area with the largest number of reports on PTML modeling for discovering multi-protein/multi-strain inhibitors against pathogenic bacteria. Most of the PTML models have been created for one of two potential applications. One of them is the _in silico_ prediction of antibacterial profiles, toxicity, and/or pharmacokinetics of investigational drugs or those approved by the Food and Drug Administration (FDA). The structures of these antibacterial drugs are shown in Figure 2.  
The other application is the _de novo_ design of new molecules virtually exhibiting potent multi-strain activities, low toxic effects, and adequate pharmacokinetic profiles. Following this, we will discuss the development of PTML models and their applications to the simultaneous predictions of multi-strain antibacterial activity, toxicity, and pharmacokinetics of wide-spectrum multi-strain investigational and FDA-approved drugs. A series of three works reported the development of PTML models to speed up the discovery of multi-strain antimycobacterial agents. In the first of these investigations, the created PTML-LDA model was able to simultaneously predict phenotypic antituberculosis activity and _in vivo_ toxicity against _Mus musculus_ and _Rattus norvegicus_[66]. In this work, the dataset included 12,096 cases, with two of the four descriptors used as inputs in the PTML-LDA model being _MLDs_ derived from the topological indices named bond-based spectral moments (_SM_s). The PTML-LDA model had _Sens_ and _Spec_ values above 91%. The second work, employing a dataset comprising 34,629 data points, applied PTML modeling to the simultaneous prediction of inhibitory multi-strain activity against different antimycobacterial strains, as well as _in vivo_ toxicity effects and pharmacokinetic properties [67]. Here, the PTML model, which was built from six _MLDs_ based on _SMs_, had _Acc_\(>\) 94%. This PTML model could calculate the quantitative contributions of different molecular fragments to the multi-strain antimyobacterial activity, toxicity, and pharmacokinetics. This model was also used to predict multiple (experimentally determined) activity, toxicity, and pharmacokinetic endpoints of bedaquiline, which was formerly known as TMC-207 (Figure 2). The PTML model could correctly predict all the aforementioned biological endpoints. The third work was applied to the early discovery of multi-strain inhibitors of _M. tuberculosis_ under 24 experimental conditions (combinations of different biological endpoints, diverse _M. tuberculosis_ strains, and many assay protocols containing multiple assay times) [68]. This PTML model, an ensemble of ANNs, was created using 1571 cases and twelve _MLDs_ derived from atom-based local stochastic quadratic indices. This model had _Acc_ > 85%. In a virtual screening scenario, the PTML model was applied to the prediction of the multi-strain antituberculosis activity of 8898 agency-regulated molecules (investigational and FDA-approved drugs, as well as chemicals with other uses); the PTML model could correctly identify several drugs with proven antituberculosis activity, including the multi-strain antituberculosis drug macozinone (Figure 2). The discovery of multi-strain inhibitors against Gram-positive bacteria was also explored by several works that developed PTML models [69; 70; 71; 72]. The most promising of these investigations intended to accelerate the search for multi-strain antibacterial inhibitors to prevent the emergence of both acquired and transferred resistance among Gram-positive cocci (_Staphylococcus_ spp., _Streptococcus_ spp., and _Enterococcus_ spp.) [72]. Towards this end, a dataset comprising 48,874 cases was used to create and validate a PTML-LDA model containing seven _MLDs_ derived from _SMs_. This model successfully predicted _in vitro_ and _in vivo_ endpoints associated with multi-strain anti-cocci activity, toxicity, and pharmacokinetics.