Matchms

Purpose and Scope

Matchms is a Python package for importing, cleaning, processing, and comparing tandem mass spectra. This skill targets matchms 0.33.1, released 2026-06-08, verified again on 2026-10-01. Older tutorials can use removed names; see the migration reference.

Use matchms for:

• MS/MS library search and query-versus-reference scoring
• Metadata harmonization, adduct/precursor handling, and peak filtering
• Cosine, modified-cosine, neutral-loss, approximate, and entropy scoring
• Structured score matrices, top-hit extraction, and spectral networks
• MGF, MSP, mzML, mzXML, JSON, mzSpecLib, and metabolomics-USI workflows

Do not use matchms as a replacement for:

• LC-MS feature detection, chromatographic alignment, peptide identification, or protein quantification — use pyopenms
• Vendor raw-file conversion — convert to mzML/mzXML first
• A validated compound-identification protocol — similarity is evidence, not proof of identity

Install the Verified Release

Create or activate an environment, then install the release used by this skill:

uv pip install "matchms==0.33.1"

Verify the runtime:

uv run python -c "import matchms; print(matchms.__version__)"

Matchms 0.33.1 supports Python 3.10-3.14 and installs RDKit as a regular dependency. The old matchms[chemistry] extra is not part of the current package metadata.

Operating Workflow

1. Inspect the inputs. Record format, spectrum count, MS level, precursor coverage, ion mode, peak counts, and identifier fields.
2. Load with metadata harmonization enabled by default. Retain original files: disabling the flag in 0.33.1 still harmonizes metadata keys.
3. Apply the same peak-processing steps to query and reference spectra. Keep metadata enrichment separate when reference annotations are richer.
4. Drop invalid spectra explicitly. Many require_* filters return None.
5. Choose the score from the scientific question, not from convenience. Modified and neutral-loss scores require valid precursor_mz.
6. Estimate len(references) * len(queries) before scoring. A sparse result container does not automatically avoid computing every requested pair.
7. Report score settings and evidence. Include tolerance and units, preprocessing, score name, number of matched peaks when available, and candidate metadata. The cosine-family tolerance is an absolute m/z window in Da, not ppm; a precursor filter with tolerance_type="ppm" does not change fragment tolerance.
8. Validate top hits visually and chemically. Use mirror plots, precursor agreement, ion/adduct compatibility, and orthogonal evidence.

Current API Guardrails

These points prevent the most common failures from pre-0.33 examples:

• Use ModifiedCosineGreedy or ModifiedCosineHungarian; ModifiedCosine was removed in 0.32.0.
• Do not call add_losses(). It was removed in 0.27.0; use spectrum.losses, spectrum.compute_losses(...), or NeutralLossesCosine directly.
• SpectrumProcessor is not callable. Use process_spectrum() or process_spectra(). The single-spectrum call mutates by default; pass a clone when preserving the original.
• process_spectra() returns (processed_spectra, processing_report).
• Scores.scores is a StackedSparseArray, often with separate structured fields such as CosineGreedy_score and CosineGreedy_matches.
• scores_by_query() returns (reference_spectrum, score_record) pairs, not reference indices.
• Prefer spectra in parameter names. The legacy spelling spectrums is deprecated.
• Never load pickle files from an untrusted source; unpickling can execute code.

See references/migration.md for a complete old-to-current mapping.

Quick Start: Clean and Search a Library

from math import isfinite

from matchms import SpectrumProcessor, calculate_scores
from matchms.filtering import (
    default_filters,
    normalize_intensities,
    require_minimum_number_of_peaks,
    require_precursor_mz,
    select_by_relative_intensity,
)
from matchms.importing import load_spectra
from matchms.similarity import ModifiedCosineGreedy


def load_and_process(path):
    spectra = [default_filters(spectrum) for spectrum in load_spectra(path)]
    processor = SpectrumProcessor(
        [
            (require_precursor_mz, {"minimum_accepted_mz": 10.0}),
            normalize_intensities,

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