## 3 Discussion
As anticipated, several key features--such as the aliphatic index, hydrophobic ratio, isoelectric point, and sequence charge--exhibit higher median values for AMPs and non-haemolytic and non-toxic AMPs compared to their precursor forms. Additionally, these AMPs display a balanced Boman index and instability index, along with shorter sequence lengths and lower molecular weights. The primary reason we compared our predicted AMPs to DRAMP and StarPepDB was to ensure the identification of novel AMP scaffolds, which were reduced to 3130 CnSAs. By using complex network analysis, they formed five clusters exhibiting distinct physicochemical properties, suggesting potential differences in their functionalities. Each cluster may contain peptides tailored to specific antimicrobial roles or interactions within the host organism's immune system. Also, the size of these clusters is proportional to the content of the representative peptides obtained after centrality analysis (1935; union dataset), indicating that the reduction process, achieved through network-based rules, was effective. Finally, specific activities for our final AMP dataset were predicted by following the same strategy as before. The three prediction tools were applied for antibacterial, antiviral antifungal, and anticancer peptides, as well as strain/target-specific predictions. We provided all the datasets for researchers to test their activity in vitro. Due to the availability of three strain-specific prediction tools in DBSAASP [11] for ABPs, this allowed for a more thorough evaluation, where we ranked these peptides considering the robustness of the predictions driven by the intersection and union of the evaluated models. The predictions over multidrug-resistance ESKAPEE pathogens, which are major contributors to antibiotic resistance and healthcare-associated infections, were also prioritized [57]. This led to the proposal of 152 ABPs from Cnidaria, with 20 candidate ABPs with the potential to be tested in vitro. The higher availability of omics data from Anthozoa leads to a higher prediction of ABPs from this group. Also, the usage of tentacle samples was ideal as it accounted for 42/142 of the total ABPs predicted, and although we only used two transcriptomesfrom nematocysts, three peptides were still uncovered. AspN and GluC retrieved a higher amount of ABPs, while Trypsin did not, revealing the importance of using distinct proteases. Although most peptides displayed predicted activity against _B. subtilis_, it is necessary to prioritize the activity against the other ESKAPEE pathogens. In previous works, AMPs originating from cDNA expression (EST) libraries from the jellyfish _Aurelia aurita_ and the Ctenophore comb jelly _Mnemiopsis leidyi_ demonstrated antibiofilm properties [58], and four synthetic AMPs predicted from the transcriptome of _Hydracitinia symboliongicarpus_ were active against both Gram-positive and Gram-negative bacteria (including _P. aeruginosa_ and _S. aureus_), with little-to-no haemolytic effects [59]. So, our 20 candidate ABPs may be potentially active, and we strongly recommend them for in vitro testing. In this work, we show how digital solutions can streamline a massive dataset of 25,264,871 proteins into 1935 representatives of Cnidaria Singular AMPs (CnSA) and 20 candidate ABPs, which passed all stages of our rigorous mining process. We provide detailed datasets designed for various research needs, from broad-spectrum antimicrobial studies to strain-specific experimental validations. This approach can be applied not only to Cnidaria omics data but also to other marine invertebrates. Some limitations of this study included the reliance on publicly available omics data, which are biased towards Anthozoans. Additional handicaps may stem from the fact that some prediction tools may be more accurate than others, with some highly cited by the literature (ex: Macrel, AMPir, HemoPi and ToxinPred3) and some more recently published (ex: ToxTeller and CAPTP), although it may not be an indication of higher reliability.