In silico fragment prediction
Use when you have a collection of compound structures in SDF format (e.g., DNA adduct structures) and need to systematically generate predicted fragment spectra across a defined ionization level and mass range to populate a reference spectral database or validate experimental fragmentation patterns.From its SKILL.md
npx -y skills add HolobiomicsLab/asb-skill-collections --skill in-silico-fragment-predictionAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 15 stars15 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its file declares
Copied from the file, not written here
The file declares its own license as CC-BY-4.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
6.0 KB, 841 tokens by cl100k_base, as published. Nobody here has run it
in-silico-fragment-prediction
License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->
Summary
Generate predicted mass spectrometry fragment spectra computationally from compound chemical structures using CFM-ID, enabling rapid annotation and validation of experimental fragmentations for metabolites and DNA adducts without requiring wet-lab MS/MS experiments.
When to use
Apply this skill when you have a collection of compound structures in SDF format (e.g., DNA adduct structures) and need to systematically generate predicted fragment spectra across a defined ionization level and mass range to populate a reference spectral database or validate experimental fragmentation patterns.
When NOT to use
- Input compounds lack well-defined 2D/3D structure information — CFM-ID requires chemically valid structure representations
- Experimental fragment spectra are already available and validated — in-silico prediction is most useful for annotation of unknowns or high-throughput reference generation, not as a replacement for experimental data
- The goal is to identify which compounds are present in a sample — use this skill for spectral reference building, not for sample analysis
Inputs
- SDF format compound structure file containing molecular structures (e.g., DNA adduct compounds)
Outputs
- Structured predicted fragment spectra database indexed by compound identifier
- Individual predicted MS/MS spectra for each input compound
How to apply
Load all compound structures from an SDF format file containing your target compounds. Execute CFM-ID on each structure, specifying the appropriate ionization level and mass range relevant to your compounds. Compile the resulting predicted fragment spectra into a structured database matching the schema of your target resource (e.g., matching the format of online predicted-fragments databases). Validate completeness by confirming that all input compounds from the SDF file have corresponding predicted spectra entries in the output database.
Related tools
- CFM-ID (In-silico fragment spectrum prediction engine that processes compound structures and generates predicted MS/MS spectra at specified ionization levels)
Evaluation signals
- All compounds in the input SDF file have exactly one corresponding entry in the output predicted fragments database
- Predicted spectra follow expected mass fragmentation patterns consistent with the chemical structure and ionization method (no negative masses, fragments within specified mass range)
- Output database schema matches the format of the target resource (e.g., predicted-fragments online database format) with required fields populated
- Predicted spectra can be successfully cross-matched against experimental spectra using standard metrics (e.g., cosine similarity with experimental MS/MS data when available)
Limitations
- CFM-ID prediction accuracy depends on compound structure quality and chemical validity; invalid or poorly-drawn structures produce unreliable spectra
- Predicted spectra represent gas-phase fragmentation patterns and may not fully capture solution-phase or matrix-dependent effects observed in experimental data
- The skill requires specification of correct ionization level and mass range parameters; misaligned parameters will generate spectra outside the relevant analytical window
Evidence
- [other] Load compound structures from the SDF format file containing DNA adduct compounds: "Load compound structures from the SDF format file containing DNA adduct compounds"
- [other] Execute CFM-ID on each compound structure to predict fragment spectra at the appropriate ionization level and mass range: "Execute CFM-ID on each compound structure to predict fragment spectra at the appropriate ionization level and mass range"
- [other] Compile predicted fragment spectra into a structured database matching the format of the deposited predicted-fragments online resource: "Compile predicted fragment spectra into a structured database matching the format of the deposited predicted-fragments online resource"
- [other] Validate output by confirming all input compounds have corresponding predicted spectra entries: "Validate output by confirming all input compounds have corresponding predicted spectra entries"
- [other] The in-silico fragment prediction stage uses CFM-ID to process SDF compound structures and generate predicted fragment spectra: "The in-silico fragment prediction stage uses CFM-ID to process SDF compound structures and generate predicted fragment spectra"
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.