Overview
LC-MS2Struct uses a structured support vector machine to combine relative elution order with spectral candidate scores across a set of features. It returns candidate rankings after preprocessing and candidate generation; it does not generate an end-to-end workflow from raw files.
Also known as: LC-MS²Struct
What can this tool do?
Common uses
- Re-rank ambiguous candidates using the retention order shared across an LC-MS/MS run.
Scientific fit
Primary function
Metabolite annotation and identification
Data compatibility
Inputs
Compound list or annotations, Feature or peak table
Outputs
Compound list or annotations
Input formats
Candidate structures, MS/MS scores and retention times via the Python interface
Output formats
Per-feature molecular candidate scores and rankings
Scope and considerations
Designed for
- Jointly rank molecular candidates using LC retention order and precomputed MS/MS scores.
Not designed for
- Replacing experimental confirmation of compound identity.
- Raw-data feature detection or chromatographic alignment.
Important requirements
- Linux and the documented Conda environment; the README does not support other operating systems.
- The separate msmsrt_scorer dependency, suitable model/training data, per-feature candidate structures and MS/MS scores, and measured retention times.
- Prepare inputs using the linked manuscript experiment scripts; this is a research Python implementation.
Considerations
- The published evaluation uses reversed-phase LC data; evaluate transfer to your chromatographic conditions.
- Candidates absent from the input sets cannot be recovered by re-ranking.
- Joint scores depend on candidate and model quality; a high rank does not establish stereochemistry.
Access and documentation
MIT license verified in the official repository. Software was not installed or execution-tested; dependencies and external services have their own requirements.
License: MIT
Evidence and review
Enveda intake reviewed against the primary publication, official repository documentation and license on 2026-10-05. Editorial review only; no execution or independent benchmark.
Core publications
Resource papers, direct updates and tool-specific methods. Selection policy.
- Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data. Nature Machine Intelligence (2022). doi:10.1038/s42256-022-00577-2
Related tools
Entry history
Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.
- 2026-10-06 · First committed entry
Add annotation software and link existing Strategy implementations
View change · Entry at this revision