Map structural relationships between named and unnamed features across your samples. Cosine similarity and neutral-loss propagation turn isolated annotations into a connected molecular map.
Features linked by structural motifs, adduct variants, or common fragmentation pathways form a connected map, not isolated points.
When one node earns an MSI Level 1a identification, its neighbours inherit structural context automatically.
Unknown features become "dark matter" with a family name, narrowing the search space for subsequent annotation.
The MS² spectrum of each feature is compared pairwise across the dataset. A cosine score ≥ 0.85 between two spectra indicates they share a common substructure. VeroMass computes similarity in both ESI+ and ESI− independently, capturing complementary views of the same molecule.
Connected components in the similarity graph form molecular families: related glycosides, acylated derivatives, or oxidation products that differ by a known neutral loss. A single Level 1a assignment propagates structural knowledge to every member of the family.
Network edges are annotated with the mass difference between connected nodes. Neutral losses of 162.053 Da (hexose), 146.058 Da (deoxyhexose), and 176.032 Da (glucuronide) are automatically flagged — turning a collision cell into a structural classifier.
Cosine ≥ 0.85 + consistent mass shift ≤ 5 ppm + RT correlation → structural family assignment. In any untargeted metabolomics experiment, the majority of detected features have no match in any reference library. When an unknown feature shares high cosine similarity with a Level 1a node and exhibits a consistent mass shift, it inherits the structural class of its neighbour — moving from "unknown" to "likely flavonol diglycoside" or "probable hydroxycinnamate ester," a hypothesis that can be tested. Each propagated annotation carries a confidence score derived from the distance to the anchor node and the number of independent edges supporting the connection.
Nodes repel and edges attract — the graph automatically organises by structural similarity. Move, pin, and zoom any node. Layout parameters are adjustable for dense or sparse datasets.
Toggle between ESI+ and ESI− networks, overlay retention time gradients, colour by compound class, and filter by cosine threshold — all without regenerating the graph.
Click any node to pull up its MS¹ and MS² spectra, retention time, adduct series, and gate status. Click an edge to see the cosine alignment between the two spectra side by side.
The network graph, annotations, and edge tables are exportable in formats that feed directly into downstream analysis: