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Spectral processing and comparison

RASSP

A research implementation for rapid prediction of electron-ionization mass spectra from molecular structures using neural-network models.

GC-MSPython packageAPISource available with restrictions
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Overview

RASSP predicts electron-ionization mass spectra from candidate molecular structures using subset/formula-based neural models. Its research implementation includes pretrained-model inference, training and library-matching evaluation code.

What can this tool do?

In-silico spectrum prediction

Common uses

  • Predict EI spectra from molecular structures for GC-MS annotation research.

Scientific fit

Primary function

Spectral processing and comparison

Other functions

Machine learning and prediction

Data compatibility

Inputs

Molecular structures

Outputs

Mass spectra

Input formats

Molecular SMILES/InChI lists or prepared molecular Parquet datasets

Output formats

Predicted EI spectra as text or SQLite/pickled spectrum records

Scope and considerations

Designed for

  • A research implementation for rapid prediction of electron-ionization mass spectra from molecular structures using neural-network models.

Not designed for

  • Electrospray tandem-MS prediction or de novo structure determination from an experimental EI spectrum.

Important requirements

  • Compatible Python/PyTorch and compiled dependencies, model/meta files and molecular SMILES/InChI inputs; assess the documented model atom limits and CPU/GPU environment.

Considerations

  • FormulaNet and SubsetNet have different molecular-size limits. Older NumPy/compiled-library compatibility matters; predicted spectra and library-search results remain model-dependent hypotheses.

Access and documentation

Primary accessDocumentation

Public research implementation and linked model files; no explicit repository-wide software license was identified. README container routes are marked TBD and are not advertised here as available deployments.

Evidence and review

Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md.

Core publications

Resource papers, direct updates and tool-specific methods. Selection policy.

  1. Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization–Mass Spectrometry. Analytical Chemistry (2023). doi:10.1021/acs.analchem.2c02093

Related tools

BLINKSimilar function · A Python package for efficient cosine-based similarity scoring and matching of large collections of fragmentation mass spectra, including analog-search workflows.DreaMSSimilar function · A transformer-based framework for learning molecular representations from tandem mass spectra, supporting spectral similarity, clustering, molecular networking, and downstream prediction tasks.FastEISimilar function · Search electron-ionization spectra against a large predicted reference library.FragHubSimilar function · Standardize and combine reference mass-spectral libraries.

Entry history

Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.

  1. 2026-10-27 · First committed entry

    Add 24 reviewed Enveda tools in a separate second batch

    View change · Entry at this revision

Full GitHub history