Molecular Networking as a Dereplication Strategy

Molecular networking (MN) has redefined the discovery of natural products (NPs) by leveraging MS/MS spectral similarity to streamline the dereplication of compounds from crude extracts. Originally conceived as a technique for clustering-related spectra, it has transformed into a versatile suite of methods incorporating fragmentation patterns, feature alignment, and bioactivity data to enhance molecular annotation. This chapter details the theoretical foundations of molecular networks, including cosine-based scoring and graph-based assembly on current platforms. It further evaluates different MS-based MN approaches and their applications in plant, microbial, and marine metabolomics, illustrating how distinct network motifs expedite the recognition of known metabolites and strategically direct the isolation of novel entities. In addition, current advances in MN for NP dereplication are discussed. Challenges such as incomplete spectral libraries, network fragmentation, and heterogeneous data formats are examined, and the discussion highlights how FAIR data principles and formats like mzML can help overcome these hurdles. Finally, the chapter highlights the roles in automating annotations to enhance dereplication workflows and reproducibility.