Complex Networks in Bioactive Peptide Research: A Methodological Review

Bioactive peptides constitute a highly diverse and therapeutically relevant molecular class, yet their systematic exploration remains challenging because of the vast size, heterogeneity, and fragmented annotation of peptide chemical space. In this context, complex networks have emerged as a complementary computational framework for organizing, analyzing, and exploiting peptide diversity. This methodological review examines the main components of graph-based peptide informatics, from graph-based data integration and curated repositories to descriptor-based representations, similarity-driven network construction, and topology-informed analysis. We describe how peptide sequences can be projected into multidimensional reference spaces using molecular descriptors, aggregation operators, and unsupervised feature selection, and how these representations support the construction of Chemical Space Networks, Half-Space Proximal Networks, and Metadata Networks. Special attention is given to topological analysis, including threshold selection, community detection, and centrality-based identification of representative peptides and scaffolds. We also review the development of Multi-query Similarity Searching Models as training-independent, topology-guided alternatives to conventional supervised predictors. Finally, we highlight the implementation of these methodologies in computational resources such as StarPepDB, StarPep Toolbox, and StarPepWeb, which illustrate the transition of peptide network science from conceptual workflows to accessible, scalable, and reproducible infrastructures. Overall, complex networks are presented as a mature and interpretable paradigm for the structured exploration, analysis, and discovery of bioactive peptides.