### Strain-Specific Predictions Reveal Novel Candidate Antibacterial Peptides (ABPs)  
The union dataset of the 1935 Cnidaria AMPs obtained after centrality analysis was used for activity predictions of antibacterial (ABP), antiviral (AVP), antifungal (AFP), and anticancer (ACP) peptides. For the ABPs, antibacterial activity predictions were performed using three prediction tools, resulting in intersection and union datasets of 32 and 1012 candidate ABPs, respectively (Figure 8a). Besides ABPs, robust predictions for antifungal, antiviral, and anticancer activities were also retrieved, resulting in intersection datasets of 40; 166 and 6; and union datasets of 940, 1485, and 1179 for AFPs, AVPs, and ACPs, respectively (Figure 8b-d). The intersection and union datasets of ABPs were virtually tested against five distinct bacterial strains, demonstrating similar patterns between intersection and union candidates (Supplementary Figure S6a). After ranking the peptides (from I to IV, see methods Section 4.6) (Supplementary Figure S6b), a total of 152 ABPs were selected for further analysis. We obtained 5 peptides from Rank I, 8 from Rank II, 7 from Rank III, and 132 from Rank IV. Figure 6: (**a**) Half-Space Proximal Network (HSPN) clustering of the 3130 non-haemolytic and non-toxic CnSAs, with clusters distinguished by colour. (**b**) Distribution of the CnSAs across the identified clusters. (**c**) Venn diagram illustrating the union and intersection of CnSA datasets from Harmonic centrality (1505 peptides) and Hub-Bridge centrality (1447 peptides) analyses. The resulting datasets include 1935 peptides (union) and 1017 peptides (intersection). Figure 7: **(a)** Physicochemical characterization of the Cnidaria Singular AMPs (CnSA) clusters within the Half-Space Proximal Network (HSPN) chemical space. Boxplot colours indicate the percentage of representative AMPs—derived from the union dataset (1935 peptides)—in each cluster. Traces represent median values, while white circles denote mean values for the physicochemical properties within each cluster. **(b)** Number of peptides per cluster, alongside the mean values for each physicochemical property across the clusters. The sea anemones _Anthopleura elegantissima_ and _Exaiptasia diaphana_ bearded the highest number of ABPs (\(n=8\)), followed by the stony coral _Alveopora japonica_ (\(n=7\)) (Supplementary Figure S7a). Also, db3 retrieved the highest number of ABPs (\(n=91\)), followed by db5 (\(n=31\)), db4 (\(n=18\)), db6 (\(n=9\)), and db7 (\(n=3\)) (Supplementary Figure S7b). From these peptides, \(77.63\%\) (\(118/152\)) had predicted antibacterial activity against _Bacillus subtilis_, \(32.24\%\) (\(49/152\)) against _Escherichia coli_, \(9.21\%\) (\(14/152\)) against _Klebsiella pneumoniae_, \(9.21\%\) (\(14/152\)) against _Pseudomonas aeruginosa_, and \(30.92\%\) (\(47/152\)) against _Staphylococcus aureus_ (Figure 9). Although the majority of ABPs were retrieved from Whole Body/Non-Specific tissues (\(n=107\)), most ABPs were retrieved from tentacles (\(n=42\)), with some being retrieved also from nematocysts (\(n=3\)) (Supplementary Figure S7c). Additionally, only AspN (\(n=94\)), GluC (\(n=56\)) and Chymotrypsin (\(n=2\)) produced ABPs. The peptides generated with Trypsin and Protease K showed no robust predicted activity against bacteria (Supplementary Figure S7d). Hexacorallia contributed to the highest number of ABPs (\(n=109\)), with stony corals (Scleractinia) and sea anemones (Actiniaria) contributing most to \(53/109\) and \(42/109\) ABPs, respectively, followed by Octocorallia (\(n=16\)), Hydrozoa (\(n=12\)), Scyphozoa (\(n=9\)), Staurozoa (\(n=3\)), and Cubozoa (\(n=3\)) (Supplementary Figure S7e). All the information regarding the \(152\) ABPs is available in Supplementary Table S6. Detailed outputs from each prediction tool are provided in Dataset 10. The FASTA files corresponding to the predicted intersection and union datasets of ABPs, AFPs, AVPs, and ACPs are available in Dataset 11.