Matchms

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,

and corrects several breaking API changes that older tutorials do not reflect.

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

• Inspect the inputs. Record format, spectrum count, MS level, precursor

coverage, ion mode, peak counts, and identifier fields.

• Load with metadata harmonization enabled unless preserving source keys is

a deliberate requirement.

• Apply the same peak-processing steps to query and reference spectra.

Keep metadata enrichment separate when reference annotations are richer.

• Drop invalid spectra explicitly. Many require_* filters return None.
• Choose the score from the scientific question, not from convenience.

Modified and neutral-loss scores require valid precursor_mz.

• Estimate len(references) * len(queries) before scoring. A sparse result

container does not automatically avoid computing every requested pair.

• Report score settings and evidence. Include tolerance, preprocessing,

score name, number of matched peaks when available, and candidate metadata.

• 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().

• 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 matchms import SpectrumProcessor, calculate_scores
from matchms.filtering import (
    default_filters,
    normalize_intensities,
    require_minimum_number_of_peaks,
    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(
        [
            normalize_intensities,
            (select_by_relative_intensity, {"intensity_from": 0.01}),
            (require_minimum_number_of_peaks, {"n_required": 5}),
        ]
    )
    processed, _ = processor.process_spectra(
        spectra,
        progress_bar=False,
        create_report=False,
    )
    return processed


references = load_and_process("library.msp")
queries = load_and_process("queries.mgf")

metric = ModifiedCosineGreedy(tolerance=0.02)
scores = calculate_scores(
    references=references,
    queries=queries,
    similarity_function=metric,
)

score_name = "ModifiedCosineGreedy_score"
matches_name = "ModifiedCosineGreedy_matches"
for query in queries:
    ranked = scores.scores_by_query(query, name=score_name, sort=True)
    for reference, values in ranked[:5]:
        print(
            query.get("spectrum_id", query.get("id")),
            reference.get("compound_name", reference.get("spectrum_id")),
            float(values[score_name]),
            int(values[matches_name]),
        )

SpectrumProcessor automatically orders built-in filters according to matchms's

filter order. The aggregate default_filters callable is not in that registry,

so run it first as above or expand its nine component filters. Inspect

processor.processing_steps and preserve it with results.

Pair Scoring

Similarity classes expose pair() for one reference/query pair. Cosine-family

results are structured NumPy scalars:

from matchms.similarity import CosineGreedy

result = CosineGreedy(tolerance=0.02).pair(reference, query)
similarity = float(result["score"])
matched_peaks = int(result["matches"])

Use calculate_scores() for matrix-oriented methods such as

FlashSimilarity; its single-pair path is supported but intentionally not the

optimized path.

Choose a Similarity Method

• CosineGreedy — standard peak cosine with greedy peak assignment.
• CosineHungarian — exact assignment; slower, useful for benchmarks.
• CosineLinear — current linear-scaling cosine implementation.
• ModifiedCosineGreedy — permits precursor-delta-shifted matches; common for

analog search.

• ModifiedCosineHungarian — exact modified-cosine assignment.
• NeutralLossesCosine — compares losses computed from precursor and fragments.
• BlinkCosine — fast BLINK-style cosine approximation for larger matrices.
• FlashSimilarity — optimized matrix scoring using spectral entropy or cosine

with fragment, neutral-loss, or hybrid matching.

• BinnedEmbeddingSimilarity — binned spectral vectors and optional approximate

nearest-neighbor indexing.

• PrecursorMzMatch, ParentMassMatch, MetadataMatch — candidate masks or

metadata constraints, not rich spectral scores.

• FingerprintSimilarity — molecular-structure similarity; it is not spectral

similarity and requires fingerprints prepared from valid structures.

Read references/similarity.md before choosing a fast method, combining scores,

or interpreting structured outputs.

Large Comparisons

For all-vs-all scoring of one collection, set is_symmetric=True:

scores = calculate_scores(
    references=spectra,
    queries=spectra,
    similarity_function=CosineGreedy(tolerance=0.02),
    array_type="sparse",
    is_symmetric=True,
)

For a precursor-gated search, compute and filter PrecursorMzMatch first, then

calculate the spectral metric only on retained coordinates through Pipeline

or Scores.calculate(...). See references/workflows.md.

Do not choose a universal "identification threshold." Score distributions

depend on preprocessing, mass accuracy, collision conditions, library quality,

and metric. At minimum, retain both score and matched-peak count for

cosine-family methods.

Bundled Library-Search CLI

scripts/library_search.py provides a reproducible query-versus-library search

with current score extraction, pair-count limits, preprocessing, and CSV output:

uv run python scripts/library_search.py \
  queries.mgf library.msp hits.csv \
  --metric modified \
  --tolerance 0.02 \
  --top-k 10 \
  --min-score 0.6 \
  --min-matches 5

Run --help for fast metrics, preprocessing options, identifier fields,

overwrite control, and the explicit large-matrix override.

Spectrum Objects and Visualization

import numpy as np
from matchms import Spectrum

spectrum = Spectrum(
    mz=np.array([100.0, 150.0, 200.0]),
    intensities=np.array([0.2, 1.0, 0.4]),
    metadata={"spectrum_id": "query-1", "precursor_mz": 250.5},
)

print(spectrum.peaks.mz)
print(spectrum.get("precursor_mz"))
losses = spectrum.compute_losses(loss_mz_from=5.0, loss_mz_to=200.0)
spectrum.plot()
spectrum.plot_against(reference_spectrum)

References

Read only the reference needed for the task:

• references/importing_exporting.md — formats, return types, generic I/O,

mzSpecLib, score serialization, and pickle safety

• references/filtering.md — current filter catalog, clone/None semantics,

default filters, ordering, and SpectrumProcessor

• references/similarity.md — all current similarity classes, outputs,

candidate masking, performance, and interpretation

• references/workflows.md — library search, sparse gating, Pipeline, networks,

plotting, and provenance

• references/migration.md — breaking changes and deprecated APIs
• references/sources.md — authoritative docs, release notes, user guides, and

scientific publications used for this refresh

Non-Negotiable Checks

• Never compare raw queries against differently processed references.
• Never use modified or neutral-loss scoring without valid precursor metadata.
• Never assume a Scores value is a plain float; inspect score_names.
• Never treat a high similarity score alone as confirmed identification.
• Never deserialize untrusted pickle data.
• Never launch an unbounded all-pairs comparison without estimating pair count.