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chem-msms-predict

Predict LC-MS/MS (MS2, tandem mass spectra) from SMILES via ICEBERG, a two-stage deep neural network. Outputs predicted m/z vs intensity spectrum, fragment ion SMILES, and a spectrum plot.

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Preis unbestätigt★ 161 GitHub-StarsVerzeichnis aktualisiert · 6. Sept. 2026agent-skill

Übersicht

Predict LC-MS/MS (MS2, tandem mass spectra) from SMILES via ICEBERG, a two-stage deep neural network. Outputs predicted m/z vs intensity spectrum, fragment ion SMILES, and a spectrum plot.

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LC-MS/MS Spectrum Prediction

Goal

Predict the LC-MS/MS (tandem mass) spectrum of a molecule given its SMILES string using ICEBERG — a two-stage GNN that first generates a fragmentation DAG (fragment ions) and then predicts their intensities. Output is a predicted spectrum (m/z, intensity) with optional fragment SMILES assignments per peak.

When to Use This Skill

  • A SMILES string is known and a predicted LC-MS/MS spectrum (m/z vs intensity) is needed.
  • Fragment ion assignments (SMILES per peak) are required.
  • No reference spectrum exists, or comparison to a predicted spectrum is desired.
  • Companion skill chem-spectrum-matcher can compare predicted vs experimental spectra.

When NOT to Use This Skill

  • Experimental spectrum already available — use it directly; no prediction needed.
  • Only compound name known — first resolve to SMILES via drug-db-pubchem, then call this skill.
  • GC-MS or other MS types — ICEBERG is trained on LC-MS/MS only; flag a warning before proceeding.
  • Organometallics or MW > 1000 — predictions may be unreliable or fail due to unsupported element types.

Prerequisites

1. Download ICEBERG checkpoints

Download from coleygroup/ms-pred releases and place in downloads/:

downloads/
├── iceberg_dag_gen_msg_best.ckpt     # generator (stage 1)
└── iceberg_dag_inten_msg_best.ckpt   # intensity predictor (stage 2)

Flag error and stop if either checkpoint is missing.

2. Set up the conda environment
bash conda-envs/msms-agent/install.sh

The ms_pred Python package is installed from GitHub automatically by the install script.

Instructions

Step 1 — Run inference and generate spectrum
# Env: ms-gen
python .agents/skills/chem-msms-predict/scripts/predict_msms.py \
    --smiles "c1ccccc1C(=O)OCCN" \
    --gen_ckpt downloads/iceberg_dag_gen_msg_best.ckpt \
    --inten_ckpt downloads/iceberg_dag_inten_msg_best.ckpt \
    --collision_energies 20 40 \
    --adduct "[M+H]+" \
    --instrument "Orbitrap" \
    --output_dir results/msms_prediction

Key parameters:

  • --smiles — input molecule as SMILES string
  • --gen_ckpt / --inten_ckpt — paths to ICEBERG checkpoints
  • --collision_energies — one or more collision energies in eV (e.g. 20 40 60); model was trained on absolute eV values
  • --adduct — supported adducts: [M+H]+, [M-H]-, [M+Na]+, [M+NH4]+, and others from ms_pred.common.ion2mass
  • --instrument — instrument type for intensity prediction (e.g. "Orbitrap", "QTOF")
  • --threshold — confidence cutoff for DAG fragment generator (default 0.1; lower = more fragments)
  • --sparse_k — maximum number of peaks returned (default 100)
  • --cuda_devices — GPU device IDs (e.g. "0" or "0,1"); omit or set to None for CPU

Outputs written to --output_dir:

FileDescription
spectrum.pngStem plot of predicted spectrum, one panel per collision energy
fragments.jsonJSON list per CE: {mz, intensity, fragment_smiles} sorted by intensity
input_configs.yamlAll run parameters for reproducibility
Step 2 — Inspect fragment assignments (optional)

fragments.json maps each predicted peak to the fragment ion SMILES responsible for it:

{
  "20": [
    {"mz": 122.0600, "intensity": 1.0, "fragment_smiles": "c1ccccc1C=O"},
    ...
  ]
}

Use this to rationalize which bonds fragment at which energy.

Step 3 — Compare with experimental spectrum (optional)

If an experimental spectrum is available, use the companion skill:

→ chem-spectrum-matcher

Examples

2-Aminoethyl benzoate (c1ccccc1C(=O)OCCN)
# Env: ms-gen
python .agents/skills/chem-msms-predict/examples/predict_smiles.py \
    --gen_ckpt downloads/iceberg_dag_gen_msg_best.ckpt \
    --inten_ckpt downloads/iceberg_dag_inten_msg_best.ckpt \
    --output_dir .agents/test/msms_example

Expected output:

  • spectrum.png — two-panel spectrum (20 eV + 40 eV)
  • fragments.json — fragment assignments for both energies
  • Precursor [M+H]+ ≈ 166.087 Da

Constraints

  • Environment: All scripts require the ms-gen conda environment. ms_pred is installed automatically from GitHub by conda-envs/msms-agent/install.sh.
  • Checkpoints required: Script raises FileNotFoundError if --gen_ckpt or --inten_ckpt are missing.
  • Collision energy units: Use absolute eV values. To convert NCE → eV, set nce=True in iceberg_prediction() directly.
  • Non-binned output only: This skill uses binned_out=False (high-precision m/z). Binned output disables fragment assignment.
  • Single-compound inference: Provide one SMILES per call. For batch prediction, loop over SMILES and use separate output dirs.
  • Unsupported elements: Molecules containing metals, lanthanides, or rare main-group elements may fail or produce low-quality predictions.
  • MW limit: ICEBERG is unreliable for MW > 1000 Da.

References

Author: Magdalena Lederbauer Contact: GitHub @mlederbauer

Quelle ansehen ↗
Dateimetadaten
name: chem-msms-predict
description: Predict LC-MS/MS (MS2, tandem mass spectra) from SMILES via ICEBERG, a two-stage deep neural network. Outputs predicted m/z vs intensity spectrum, fragment ion SMILES, and a spectrum plot.
category: [chemistry, drug-discovery]
Originaltext anzeigen
---
name: chem-msms-predict
description: Predict LC-MS/MS (MS2, tandem mass spectra) from SMILES via ICEBERG, a two-stage deep neural network. Outputs predicted m/z vs intensity spectrum, fragment ion SMILES, and a spectrum plot.
category: [chemistry, drug-discovery]
---

# LC-MS/MS Spectrum Prediction

## Goal

Predict the LC-MS/MS (tandem mass) spectrum of a molecule given its SMILES string using ICEBERG — a two-stage GNN that first generates a fragmentation DAG (fragment ions) and then predicts their intensities. Output is a predicted spectrum (m/z, intensity) with optional fragment SMILES assignments per peak.

## When to Use This Skill

- A SMILES string is known and a predicted LC-MS/MS spectrum (m/z vs intensity) is needed.
- Fragment ion assignments (SMILES per peak) are required.
- No reference spectrum exists, or comparison to a predicted spectrum is desired.
- Companion skill `chem-spectrum-matcher` can compare predicted vs experimental spectra.

## When NOT to Use This Skill

- **Experimental spectrum already available** — use it directly; no prediction needed.
- **Only compound name known** — first resolve to SMILES via `drug-db-pubchem`, then call this skill.
- **GC-MS or other MS types** — ICEBERG is trained on LC-MS/MS only; flag a warning before proceeding.
- **Organometallics or MW > 1000** — predictions may be unreliable or fail due to unsupported element types.

## Prerequisites

### 1. Download ICEBERG checkpoints

Download from [coleygroup/ms-pred releases](https://github.com/coleygroup/ms-pred) and place in `downloads/`:

```
downloads/
├── iceberg_dag_gen_msg_best.ckpt     # generator (stage 1)
└── iceberg_dag_inten_msg_best.ckpt   # intensity predictor (stage 2)
```

**Flag error and stop** if either checkpoint is missing.

### 2. Set up the conda environment

```bash
bash conda-envs/msms-agent/install.sh
```

The `ms_pred` Python package is installed from GitHub automatically by the install script.

## Instructions

### Step 1 — Run inference and generate spectrum

```bash
# Env: ms-gen
python .agents/skills/chem-msms-predict/scripts/predict_msms.py \
    --smiles "c1ccccc1C(=O)OCCN" \
    --gen_ckpt downloads/iceberg_dag_gen_msg_best.ckpt \
    --inten_ckpt downloads/iceberg_dag_inten_msg_best.ckpt \
    --collision_energies 20 40 \
    --adduct "[M+H]+" \
    --instrument "Orbitrap" \
    --output_dir results/msms_prediction
```

**Key parameters:**
- `--smiles` — input molecule as SMILES string
- `--gen_ckpt` / `--inten_ckpt` — paths to ICEBERG checkpoints
- `--collision_energies` — one or more collision energies in eV (e.g. `20 40 60`); model was trained on absolute eV values
- `--adduct` — supported adducts: `[M+H]+`, `[M-H]-`, `[M+Na]+`, `[M+NH4]+`, and others from `ms_pred.common.ion2mass`
- `--instrument` — instrument type for intensity prediction (e.g. `"Orbitrap"`, `"QTOF"`)
- `--threshold` — confidence cutoff for DAG fragment generator (default `0.1`; lower = more fragments)
- `--sparse_k` — maximum number of peaks returned (default `100`)
- `--cuda_devices` — GPU device IDs (e.g. `"0"` or `"0,1"`); omit or set to `None` for CPU

**Outputs written to `--output_dir`:**
| File | Description |
|------|-------------|
| `spectrum.png` | Stem plot of predicted spectrum, one panel per collision energy |
| `fragments.json` | JSON list per CE: `{mz, intensity, fragment_smiles}` sorted by intensity |
| `input_configs.yaml` | All run parameters for reproducibility |

### Step 2 — Inspect fragment assignments (optional)

`fragments.json` maps each predicted peak to the fragment ion SMILES responsible for it:

```json
{
  "20": [
    {"mz": 122.0600, "intensity": 1.0, "fragment_smiles": "c1ccccc1C=O"},
    ...
  ]
}
```

Use this to rationalize which bonds fragment at which energy.

### Step 3 — Compare with experimental spectrum (optional)

If an experimental spectrum is available, use the companion skill:

→ [`chem-spectrum-matcher`](../chem-spectrum-matcher/SKILL.md)

## Examples

### 2-Aminoethyl benzoate (`c1ccccc1C(=O)OCCN`)

```bash
# Env: ms-gen
python .agents/skills/chem-msms-predict/examples/predict_smiles.py \
    --gen_ckpt downloads/iceberg_dag_gen_msg_best.ckpt \
    --inten_ckpt downloads/iceberg_dag_inten_msg_best.ckpt \
    --output_dir .agents/test/msms_example
```

Expected output:
- `spectrum.png` — two-panel spectrum (20 eV + 40 eV)
- `fragments.json` — fragment assignments for both energies
- Precursor `[M+H]+` ≈ 166.087 Da

## Constraints

- **Environment**: All scripts require the `ms-gen` conda environment. `ms_pred` is installed automatically from GitHub by `conda-envs/msms-agent/install.sh`.
- **Checkpoints required**: Script raises `FileNotFoundError` if `--gen_ckpt` or `--inten_ckpt` are missing.
- **Collision energy units**: Use absolute eV values. To convert NCE → eV, set `nce=True` in `iceberg_prediction()` directly.
- **Non-binned output only**: This skill uses `binned_out=False` (high-precision m/z). Binned output disables fragment assignment.
- **Single-compound inference**: Provide one SMILES per call. For batch prediction, loop over SMILES and use separate output dirs.
- **Unsupported elements**: Molecules containing metals, lanthanides, or rare main-group elements may fail or produce low-quality predictions.
- **MW limit**: ICEBERG is unreliable for MW > 1000 Da.

## References

- Alberts, M. et al., "Artificial intelligence for context-aware mass spectrometry", *Nature Methods*, 2025. [DOI:10.1038/s41592-025-02658-z](https://doi.org/10.1038/s41592-025-02658-z)
- ICEBERG source code: [github.com/coleygroup/ms-pred](https://github.com/coleygroup/ms-pred)

---

**Author:** Magdalena Lederbauer
**Contact:** [GitHub @mlederbauer](https://github.com/mlederbauer)

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Lizenz: MIT

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • The skill requires manual download of ICEBERG checkpoints from an external GitHub release, which introduces a supply-chain dependency that is not automated or verified.
  • The SKILL.md excerpt is truncated at the end ('Si'), but the full file likely contains complete constraints; no critical information appears missing.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository
learningmatter-mit/AtomisticSkills
Lizenz
MIT
Version
1.0.0
Letzter GitHub-Push
3. Sept. 2026
Verzeichnis aktualisiert
6. Sept. 2026
Anleitungspfad
.agents/skills/chem-msms-predict/SKILL.md @ d1f7ecfda2cd

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

66/100

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57/100

Do not auto-install

Audit

73/100

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Vollständiges Audit öffnenEval-Bericht ansehen
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • The skill requires manual download of ICEBERG checkpoints from an external GitHub release, which introduces a supply-chain dependency that is not automated or verified.
  • The SKILL.md excerpt is truncated at the end ('Si'), but the full file likely contains complete constraints; no critical information appears missing.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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Weitere Details
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      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 73,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "The skill requires manual download of ICEBERG checkpoints from an external GitHub release, which introduces a supply-chain dependency that is not automated or verified.",
      "The SKILL.md excerpt is truncated at the end ('Si'), but the full file likely contains complete constraints; no critical information appears missing.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 66,
    "label": "Promising"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "The skill requires manual download of ICEBERG checkpoints from an external GitHub release, which introduces a supply-chain dependency that is not automated or verified.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The SKILL.md excerpt is truncated at the end ('Si'), but the full file likely contains complete constraints; no critical information appears missing.",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use chem-msms-predict in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 65/100 Manual review",
      "Audit: 73/100 Needs review",
      "Safety: 33/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "learningmatter-mit-chem-msms-predict (chem-msms-predict)",
      "install_command": "npx skills add learningmatter-mit/AtomisticSkills --skill chem-msms-predict",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "learningmatter-mit-chem-msms-predict",
      "task": "Use chem-msms-predict in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/learningmatter-mit-chem-msms-predict",
    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-msms-predict",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-msms-predict/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-msms-predict&task=Use%20chem-msms-predict%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-msms-predict%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-msms-predict%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-msms-predict/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-msms-predict"
  }
}

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