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Molecular networking and chemical similarity

MolNotator

A Python package that interprets LC-MS/MS ion relationships and spectral evidence to infer molecule-centric networks rather than networks of individual ion signals.

LC-MSUntargeted metabolomicsPython packageOpen source
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Overview

MolNotator groups LC-MS/MS ions into putative neutral-molecule nodes using adduct/fragment relationships and combines these with spectral dereplication. Its outputs separate molecule-level hypotheses from redundant measured ions in networks.

What can this tool do?

Ion identity networkingSpectral-library matchingNetwork visualization

Common uses

  • Collapse ion-level evidence into predicted molecule nodes.
  • Dereplicate candidates using spectral, exact-mass, retention-time, and adduct information.

Scientific fit

Primary function

Molecular networking and chemical similarity

Other functions

Metabolite annotation and identification

Analysis types

Untargeted metabolomics

Data compatibility

Inputs

Feature or peak table, Mass spectra

Outputs

Compound list or annotations, Statistical results

Input formats

MGF spectra, CSV feature tables and YAML/adduct/reference tables

Output formats

CSV node/edge tables and simplified molecular-network results

Scope and considerations

Designed for

  • A Python package that interprets LC-MS/MS ion relationships and spectral evidence to infer molecule-centric networks rather than networks of individual ion signals.

Not designed for

  • Confirming exact molecular structures from neutral-mass triangulation or handling arbitrary multiply charged ions without method validation.

Important requirements

  • The documented Python environment, MZmine-style MGF/CSV exports with matching identifiers, parameter/adduct tables and any reference databases used for dereplication; positive and negative data can be combined.

Considerations

  • The documented implementation recommends singly charged ions and older dependency versions. Adduct rules and upstream feature quality affect inferred molecule nodes; the cited method remains a preprint.

Access and documentation

Primary accessDocumentation

MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested.

License: MIT

Evidence and review

Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md.

Core publications

Resource papers, direct updates and tool-specific methods. Selection policy.

  1. Preprint · not peer reviewed From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics. bioRxiv preprint (2021). Preprint; not peer reviewed. doi:10.1101/2021.12.21.473622

Related tools

ConCISESimilar function · ConCISE propagates consensus chemical-class annotations by combining molecular-network relationships, library matches and in-silico class predictions.GNPSSimilar function · A web-based mass-spectrometry ecosystem for molecular networking, spectral-library search, repository-scale search, interactive visualization, knowledgebase curation, and public-data integration.GNPS2 Classical Molecular NetworkingSimilar function · GNPS2 workflow for organizing MS/MS spectra into molecular families using pairwise spectral similarity and spectral-library searching.GNPS2 Feature-Based Molecular Networking workflowSimilar function · Dedicated GNPS2 workflow that combines an aligned LC-MS feature table with MS/MS spectra to build quantitative molecular networks.

Entry history

Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.

  1. 2026-10-27 · First committed entry

    Add 24 reviewed Enveda tools in a separate second batch

    View change · Entry at this revision

Full GitHub history