chem-msms-predict: Reusable for AI Agents | OpenAgentSkill

Overview

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:

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:

{
  "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

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

Author: Magdalena Lederbauer Contact: GitHub @mlederbauer

View source ↗

File metadata

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]

View original text

---
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)

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: 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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Source & usage notes

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Source repository: learningmatter-mit/AtomisticSkills
License: MIT
Version: 1.0.0
Last GitHub push: Sep 3, 2026
Registry updated: Sep 6, 2026
Instruction path: .agents/skills/chem-msms-predict/SKILL.md @ d1f7ecfda2cd

Version reported in registry metadata; check source releases before relying on it.

Quality

69/100

Promising

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59/100

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      "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."
    }
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  "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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Creator: learningmatter-mit
Source: learningmatter-mit/AtomisticSkills
Indexed by: OpenAgentSkill community index

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