Overview
IDSL_MINT provides transformer-based workflows connecting tandem mass spectra and molecular representations. Documented modules include MS/MS-to-fingerprint prediction, structure generation and reverse spectrum prediction, with YAML-controlled training and prediction workflows.
What can this tool do?
Common uses
- IDSL_MINT provides transformer-based workflows connecting tandem mass spectra and molecular representations.
Scientific fit
Primary function
Machine learning and prediction
Other functions
Spectral processing and comparison
Data compatibility
Inputs
Mass spectra
Outputs
Molecular structures, Mass spectra, Statistical results
Input formats
Module-specific MSP spectra, molecular representations and YAML config
Output formats
Fingerprints, candidate structures or predicted spectra by module
Scope and considerations
Designed for
- IDSL_MINT provides transformer-based workflows connecting tandem mass spectra and molecular representations.
Not designed for
- Definitive structure confirmation or evidence that every documented module was evaluated in the original fingerprint-prediction paper.
Important requirements
- Python with PyTorch, RDKit and documented dependencies; prepared MSP/structure data, a module-specific YAML configuration and suitable trained weights or training data.
Considerations
- Select the representation and model for the task. Fingerprint accuracy, structure generation and predicted spectra have different validation needs and chemical-domain limits.
Access and documentation
Source is publicly available, but a repository-wide license grant was not established from the reviewed metadata; model and dataset terms must be checked separately.
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.
- IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra. Journal of Cheminformatics (2024). doi:10.1186/s13321-024-00804-5
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