Mass Spectrometry Spectrum Preprocessing

---
name: mass-spectrometry-spectrum-preprocessing
description: Use when when you have raw LC–QTOF wastewater spectra (or other real
  experimental mass spectrometry data) and need to feed them into a pretrained MSGO
  model for molecular structure prediction.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3214
  edam_topics:
  - http://edamontology.org/topic_0091
  - http://edamontology.org/topic_3172
  tools:
  - Python 3.7
  - Torch
  - Python
  - MSGO model
  techniques:
  - mass-spectrometry
  license_tier: open
  provenance_tier: literature
derived_from:
- doi: 10.1038/s42256-025-01140-5
  title: MSGo
evidence_spans:
- 'Python: 3.7'
- 'Torch: 1.7.1'
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_mist_chemical_formula_transformer_cq
    doi: 10.1038/s42256-023-00708-3
    title: MIST (chemical formula transformer)
  - build: coll_msgo_cq
    doi: 10.1038/s42256-025-01140-5
    title: MSGo
  dedup_kept_from: coll_msgo_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1038/s42256-025-01140-5
  all_source_dois:
  - 10.1038/s42256-025-01140-5
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# mass-spectrometry-spectrum-preprocessing

## Summary

Normalize and format experimental mass spectrometry spectra to match the input requirements of a trained neural encoder before inference. This is a critical preprocessing step that ensures real LC–QTOF spectra are compatible with deep learning models trained on pseudo SMILES-spectrum pairs.

## When to use

When you have raw LC–QTOF wastewater spectra (or other real experimental mass spectrometry data) and need to feed them into a pretrained MSGO model for molecular structure prediction. The trigger is a mismatch between raw spectrum format (raw m/z arrays, intensity values, metadata) and the model's expected tensor input shape and normalization convention.

## When NOT to use

- Input spectra are already pseudo SMILES-spectrum pairs generated by cfmid (use them directly for training, not preprocessing)
- You have no reference model or training protocol defining the expected spectrum format (preprocessing requires a known input schema)

## Inputs

- Raw LC–QTOF spectrum (CSV, NetCDF, or vendor format with m/z and intensity columns)
- Experimental wastewater sample spectra (300+ spectra in real datasets)
- Model configuration or metadata describing expected input shape and normalization

## Outputs

- Preprocessed spectrum tensor compatible with MSGO model input layer
- Normalized intensity values and aligned m/z ranges
- Preprocessed spectrum in CSV or HDF5 format suitable for batch inference

## How to apply

Load raw experimental spectra from CSV or vendor-specific formats (e.g., LC–QTOF output). Normalize intensity values and align m/z ranges to match the format used during MSGO model training on 30k+ pseudo SMILES-spectrum pairs. Apply any mass accuracy corrections or intensity scaling required by the model (e.g., log-intensity transformation, m/z binning). Remove or flag low-intensity noise below a model-specific threshold. Convert the formatted spectrum into a tensor representation compatible with Torch 1.7.1. Verify shape and data type match the model's input layer (typically a 2D array of m/z–intensity pairs or a binned mass spectrum vector). This ensures inference produces valid confidence-ranked SMILES predictions rather than numerical errors or out-of-domain outputs.

## Related tools

- **MSGO model** (Pretrained deep learning model that consumes preprocessed spectra to output ranked molecular structure predictions) — github.com/aaronma2020/MSGO
- **Torch** (Deep learning framework (version 1.7.1) used to load model and convert preprocessed spectra to tensors for inference)
- **Python** (Scripting language (version 3.7) for implementing spectrum loading, normalization, and tensor conversion workflows)

## Examples

```
# After downloading and extracting LC–QTOF wastewater spectra to ./data/wastewater.csv, preprocess as follows:
python tools/eval_standard.py --log_path ckpts/pfas --real_csv ./data/wastewater.csv --out_csv ./wastewater_results.csv --beam_size 500 --polar neg
```

## Evaluation signals

- Preprocessed spectrum tensor shape matches MSGO model's input_size parameter (e.g., [batch_size, spectrum_dim])
- Intensity values fall within model's expected range (e.g., [0, 1] if min-max normalized, or log-transformed to typical range observed in training data)
- m/z values are aligned to model's expected mass range (no NaN, inf, or out-of-bounds values)
- Inference on preprocessed spectrum produces valid confidence scores (non-zero, between 0 and 1) and ranked SMILES predictions rather than errors or trivial outputs
- Predictions on known reference compounds or literature-annotated wastewater compounds show non-negligible confidence (>0.01) for correct or near-correct structures

## Limitations

- Preprocessing assumes the model's training distribution (30k+ cfmid-generated pseudo spectra) is representative of real experimental LC–QTOF spectra; domain shift or unusual ionization modes may degrade downstream prediction quality
- Spectrum quality and instrument calibration directly impact preprocessing fidelity; poorly calibrated or noisy raw spectra may fail normalization or produce low-confidence predictions
- No single preprocessing formula is provided in the README; practitioners must infer normalization strategy from model training details or empirical validation against known compounds

## Evidence

- [other] Preprocess the experimental spectra to match the input format expected by the model.: "Preprocess the experimental spectra to match the input format expected by the model."
- [readme] For Training, we use 30k+ pseudo smiles-specturm pairs generated by cfmid: "For Training, we use 30k+ pseudo smiles-specturm pairs generated by cfmid"
- [readme] we use one LC–QTOF dataset for wastewater samples to verify our model: "we use one LC–QTOF dataset for wastewater samples to verify our model"
- [other] Load the pre-trained MSGO model (Torch 1.7.1) and the LC–QTOF wastewater dataset containing 300+ real spectra.: "Load the pre-trained MSGO model (Torch 1.7.1) and the LC–QTOF wastewater dataset containing 300+ real spectra."