## 4 Conclusions  
Half-space proximal networks were successfully introduced to project the pairwise alignment-free similarities of the reported ABFPs. Particularly, an HSPN model was obtained by applying an optimal similarity cutoff of 0.65, which allowed an effective delineation of network communities with the respective identification of the most central (relevant) and atypical peptides among the ABFPs. From the topology of the HSPN model, a reduced subset of 66 ABFPs, resulting from the union of the harmonic and hub-bridge centralities, was extracted by the scaffold extraction algorithm from the StarPep toolbox. As these 66 peptides, made up of both the most relevant and atypical ABFPs, were not only selected by the centrality criteria but also considering their topological distribution and coverage on the original space, they can be considered representatives of the ABFP chemical space. Alongside this previously mentioned procedure, the metadata associated with both the most central and atypical peptides were analyzed by the visual mining of complex networks, integrating additional relevant properties for the antibiofilm action and for the discovery/design of next-generation antimicrobial agents able to combat MDR infections. On the other hand, the proposed network-assisted motif discovery allowed the identification of ABFP motifs by AB and AF approaches within the communities of the HSPN model. In short, the network-based identification of the most central to atypical ABFP scaffolds bearing promising antimicrobial activities on MDR targets was mostly transferred to the representative subset of 66 ABFPs. Therefore, this subset together with the discovered ABFP motifs is recommended for use in the mapping and design of new ABFPs as privileged scaffolds for the development of next-generation antimicrobials. This is the first work that illustrates how complex networks can be integrated in the discovery pipeline of more effective antimicrobial agents from the existing data. As an emerging methodology in the field, it still has many uncovered potentialities and will need the support of existing methodologies, as stated here. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/antibiotics12040747/s1](https://www.mdpi.com/article/10.3390/antibiotics12040747/s1)]([https://www.mdpi.com/article/10.3390/antibiotics12040747/s1](https://www.mdpi.com/article/10.3390/antibiotics12040747/s1)), e.g., File S1: HSPN_Parameters Cutoff Selection & Node Degree; File S2: HSPNs Clusters and Centralities & 10-ToP Central ABFPs; File S3: Subsets HCUHB 6, 7 and 8 & Starting ABFPs Sets fastsa files; File S4: 66 Rep. ABFPs Description & HSFN; File S5: All vs all global alignments 14 ABFPs vs 66 Rep.; Figure S1: Overlapping Subsets 6, 7, 8 HC-HB & HC-U-HB for Selection; Figure S2: Heat Maps All vs All Pairwise Global Identities by Community; Figure S3: Motif Detection by Sequence Alignments per Network Clusters; also available at Zenodo (DOI 10.5281/zenodo.7706555): [[https://zenodo.org/record/7706555#Z.Aef_y8Q1X0](https://zenodo.org/record/7706555#Z.Aef_y8Q1X0)]([https://zenodo.org/record/7706555#Z.Aef_y8Q1X0](https://zenodo.org/record/7706555#Z.Aef_y8Q1X0)) (accessed on 22 February 2023)  
**Author Contributions:** [PERSON] and [PERSON] worked mainly on the conceptualization, formal analysis, supervision, validation, writing and reviewing of the manuscript. [PERSON], [PERSON] and [PERSON] worked on data curation and the design/implementation of the HSPNs and METNs visualization. [PERSON] and [PERSON] were responsible for the motif discovery and peptide diversity analyses. [PERSON], and [PERSON] were participated in funding acquisition, writing, and reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.