Overview
MASSISTANT provides research code for learning molecular SELFIES representations from electron-ionization mass spectra. Its documented workflow covers dataset cleaning, SELFIES featurization and model training for de novo structure prediction.
What can this tool do?
Common uses
- MASSISTANT provides research code for learning molecular SELFIES representations from electron-ionization mass spectra.
Scientific fit
Primary function
Metabolite annotation and identification
Other functions
Machine learning and prediction
Data compatibility
Inputs
Mass spectra
Outputs
Molecular structures
Input formats
Prepared EI spectrum/molecular-structure datasets
Output formats
Featurized training tables and trained SELFIES-prediction models
Scope and considerations
Designed for
- MASSISTANT provides research code for learning molecular SELFIES representations from electron-ionization mass spectra.
Not designed for
- Immediate inference with a distributed final model; the README still marks prediction instructions and a Docker image as forthcoming.
Important requirements
- A compatible research environment and appropriately licensed EI training spectra; the repository does not supply the commercial-NIST-trained models.
Considerations
- This is a training-oriented research release. Valid SELFIES syntax does not establish that a predicted molecule is the experimentally measured compound.
Access and documentation
Public research source, no repository-wide license grant established, and final NIST-trained models are not supplied. Users must obtain suitable data rights and train their own model.
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-batch3.md.
Core publications
Resource papers, direct updates and tool-specific methods. Selection policy.
- MASSISTANT: A deep learning model for De Novo molecular structure prediction from EI‑MS spectra via SELFIES encoding. Journal of Chromatography A (2025). doi:10.1016/j.chroma.2025.466216
Related tools
Entry history
Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.
- 2026-10-27 · First committed entry
Add 45 source-reviewed Enveda tool entries in third expansion batch
View change · Entry at this revision