Metabolite Similarity Scoring

metabolite-similarity-scoring

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.

Summary

Score and rank candidate metabolites by computing semantic similarity between an unknown compound's mass spectrum and a reference database of known metabolite spectra using deep-learned mass spectral language encodings. This enables prediction of structurally related metabolites and assists in ranking molecular structure database candidates.

When to use

You have an unknown compound's mass spectrum (m/z peaks and intensities) in .mgf or equivalent format with mandatory PRECURSOR_MZ and IONMODE tags, and you need to identify structurally related metabolites from a reference database to constrain chemical space and rank candidates for structure elucidation.

When NOT to use

• Input mass spectrum lacks mandatory PRECURSOR_MZ or IONMODE metadata tags — DeepMASS2 requires these for filtering and model selection.
• Ion mode is unknown or mixed — the tool requires a single consistent polarity to select the correct deep-learning model.
• Reference database is unavailable or incompatible — the pre-indexed pickle and binary index files must be downloaded and placed in the correct data and model directories.

Inputs

• Unknown compound mass spectrum (.mgf file with PRECURSOR_MZ and IONMODE tags)
• Mass spectral peak list (m/z and intensity pairs)
• Ion mode (positive or negative)
• Precursor m/z value
• Optional: molecular formula

Outputs

• Ranked list of candidate metabolites with semantic similarity scores
• CSV file mapping unknown spectrum to top-ranked structurally related metabolites
• Semantic similarity scores (cosine similarity metric)

How to apply

First, validate input mass spectral data contains required metadata: precursor m/z value and ion mode polarity (positive or negative). Load the unknown spectrum and encode it into a semantic vector representation using the DeepMASS2 deep-learning model (Ms2Vec) trained on the appropriate ion mode. Compute cosine similarity or equivalent distance metric between the encoded spectrum and a pre-indexed reference database of known metabolite spectra (references_spectrums_positive.pickle or references_spectrums_negative.pickle). Rank candidate metabolites by similarity score in descending order. Optionally, constrain the chemical space by filtering candidates using molecular formula if available, which significantly improves ranking accuracy. Return the top-ranked structurally related metabolites with their corresponding similarity scores.

Related tools

• DeepMASS2 (Deep-learning encoder and semantic similarity search engine; encodes mass spectra into learned language vectors and computes cosine similarity against indexed reference spectra) — https://github.com/hcji/DeepMASS2_GUI

Evaluation signals

• Returned similarity scores range from 0 to 1 (or appropriate metric bounds) and are sorted in descending order.
• Top-ranked candidates match known structurally related metabolites when available (manual validation against literature or ground truth).
• Output CSV file is successfully generated with COMPOUND_NAME and contains ranked metabolite identifiers and similarity scores.
• Semantic similarity scores are reproducible across multiple runs with identical inputs and model weights.
• Filtering by molecular formula (when provided) reduces candidate list while preserving correct metabolite in top ranks, indicating chemical space constraint is effective.

Limitations

• Accuracy depends on quality and completeness of the reference database; structurally related metabolites outside the database cannot be retrieved.
• Deep-learning model performance is ion-mode specific; incorrect IONMODE assignment will use wrong model and reference spectra, degrading results.
• Similarity scoring is agnostic to retention time or other orthogonal metadata; mass spectral language alone may not distinguish isomers or closely related structures.
• The tool requires manual download and placement of large model and reference files (≥1 GB); missing or corrupted files will cause runtime failures.
• No changelog documented; version history is sparse (only v0.99.0 and v0.99.1 released), limiting visibility into bug fixes and feature improvements.

Evidence

• [other] Encode → Compute → Rank workflow: "1. Load the unknown compound's mass spectral data (m/z peaks and intensities). 2. Encode the mass spectrum into a semantic representation using the DeepMASS2 deep-learning model trained on mass"
• [intro] Purpose of structurally related metabolites: "By considering the chemical space, these structurally related metabolites provide valuable information about the potential location of the unknown metabolites and assist in ranking candidates"
• [readme] Required metadata tags: "Precursor m/z - Required This tag specifies the precursor ion mass-to-charge ratio (m/z). This is a fundamental requirement for the search engine to filter candidates within the structural"
• [readme] Ion mode requirement: "Ion Mode - Required This specifies the polarity of the data, ensuring DeepMASS2 utilizes the correct deep-learning model (Positive vs. Negative) and reference libraries."
• [readme] Optional molecular formula constraint: "Adding the molecular formula helps the semantic similarity engine constrain potential chemical space, significantly improving the ranking accuracy of structurally related metabolites."
• [readme] Reference data requirements: "put the following files into data folder: DeepMassStructureDB-v1.1.csv, references_index_negative_spec2vec.bin, references_index_positive_spec2vec.bin, references_spectrums_negative.pickle,"