chem-spectrum-matcher

Spectrum Matcher

Goal

To retrieve or generate reference spectra for a set of candidate molecules and rank them by similarity to an experimental query spectrum. The skill abstracts a common three-component pattern:

1. Prediction — generate reference spectra from structure (SMILES) via empirical predictors or QM.
2. Reference DB — cache computed spectra locally; fall back to public databases for known compounds.
3. Similarity metric — score each candidate against the query and rank.

This pattern applies to any spectral modality: 1H NMR, 13C NMR, IR, mass spectrometry, UV-Vis, Raman. The concrete implementation here covers 1H NMR and IR, with the NMR path fully implemented and IR sketched for extension.

When to Use This Skill

• Confirm a proposed structure against an experimental spectrum.
• Screen a shortlist of candidates and rank by spectral similarity.
• Avoid re-running expensive predictions by retrieving cached spectra from the local catalog.
• Fetch experimental reference spectra from public databases (NMRShiftDB2, NIST WebBook) before committing to a prediction run.

When NOT to Use This Skill

• Unknown structure elucidation from scratch — this skill requires a candidate list. For open-ended structure identification, use general-query-literature-database first.
• Mixture deconvolution — use chem-nmr-analysis (Wasserstein deconvolution) for quantifying component ratios.
• 13C, 19F, 31P NMR prediction — chem-nmr-predict (SPINUS) covers 1H only. Extension needed.
• Mass spectrometry — not yet implemented. Scaffold is in place; add a predictor and similarity metric.

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Architecture

SMILES  ──► [Predictor]  ──► predicted spectrum (.xy / .jdx)
                                        │
                                        ▼
                              [Local Catalog]  ◄──  register_spectrum.py
                                        │
        [Public DB fallback]  ──────────┤  (NMRShiftDB2, NIST WebBook)
                                        │
                                        ▼
            Experimental query  ──► [match_spectrum.py]  ──► ranked candidates