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

Overview

Predict 1H NMR spectra from SMILES strings via NMRdb.org SPINUS neural network prediction and nmrsim quantum mechanical spin simulation.

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1H NMR Spectrum Prediction

When to Use This Skill

The agent should use this skill when:

A SMILES string is known and the agent needs a predicted 1H NMR spectrum (ppm vs intensity) for that compound.
The agent needs a signal list (chemical shifts, multiplicities, coupling constants, proton counts) for a compound.
Reference spectra are needed for mixture deconvolution (called by the chem-nmr-analysis skill).
The user wants to compare a predicted spectrum against an experimental one for structure confirmation.

When NOT to Use This Skill

The user already has an experimental or digitized spectrum file — no prediction is needed; the agent should use the existing file directly.
The user has a compound name but not a SMILES — the agent should first resolve the name to SMILES using the drug-db-pubchem skill, then call this skill.
13C NMR prediction — this skill predicts 1H NMR only. The NMRdb.org SPINUS endpoint does not support 13C.
Polymers, organometallics, or molecules with >50 heavy atoms — the SPINUS neural network may not produce reliable predictions, and nmrsim QM simulation is limited to ~11 coupled spins per spin system.
The user asks about reaction products or mixture composition — the agent should use chem-nmr-analysis instead, which calls this skill internally.

Workflow: SMILES → Predicted 1H NMR

Step 1 — Ensure SMILES Are Available

If the user provides compound names instead of SMILES, the agent should first resolve them:

# Env: base-agent
python .agents/skills/drug-db-pubchem/scripts/query_pubchem.py \
  --name "camphor" --outdir <research_dir>/pubchem/

The agent should extract CanonicalSMILES from the JSON output.

If PubChem returns no results, the agent should try alternate names or ask the user to provide the SMILES directly.

Step 2 — Predict NMR Spectra

# Env: nmr-agent
python .agents/skills/chem-nmr-predict/scripts/predict_nmr.py \
  --smiles "<smiles_1>" "<smiles_2>" \
  --names "compound1" "compound2" \
  --field_mhz 400 \
  --output_dir <research_dir>/nmr_predictions/

Arguments:

--smiles (required): one or more SMILES strings.
--names: human-readable labels for filenames. If omitted, defaults to comp0, comp1, etc. The agent should always provide meaningful names.
--field_mhz: spectrometer frequency in MHz (default: 400). The agent should match the field strength of the user's experimental spectrum if known.
--linewidth: Lorentzian FWHM in Hz (default: 1.0). The agent should increase this (e.g., 2.0–5.0) if the user's experimental spectrum has broad lines.
--n_points: spectrum resolution (default: 8192). The agent should not change this unless the user requests higher resolution.
--output_dir: where to save results.

Outputs per compound:

<name>.xy — two-column tab-separated file (ppm, intensity), descending ppm. Compatible with all NMR processing tools and the chem-nmr-analysis deconvolution scripts.
<name>_signals.csv — signal table with columns: shift_ppm, multiplicity, J_Hz, nH.
predictions.json — manifest listing all found/failed compounds and parameters.

Step 3 — Verify Predictions

After prediction, the agent must:

Check the manifest (predictions.json) for any failed compounds.
Read the signal table (_signals.csv) and verify it is chemically reasonable:
- The total number of protons across all signals should match the molecular formula.
- Chemical shifts should be in expected ranges (e.g., alkyl 0–2 ppm, aromatic 6–8 ppm, aldehyde 9–10 ppm).
If the user has an experimental spectrum, the agent should overlay them using chem-nmr-analysis's plot.py for visual comparison.

If SPINUS returns no atoms for a SMILES → the SMILES may be invalid, the molecule may lack hydrogen atoms (e.g., CCl4), or the molecule may be too complex. The agent should:

Verify the SMILES is valid (try parsing with RDKit).
Check if the molecule actually has hydrogen atoms.
If valid but SPINUS fails, inform the user that prediction is unavailable for this compound.

If nmrsim simulation fails → the script falls back to a stick spectrum (chemical shifts only, no multiplet structure). The agent should note this in its response — the predicted spectrum will lack splitting patterns but chemical shifts will still be approximate.

If/Then: Field Strength Matching

If the user's experimental spectrum was recorded at 300 MHz → the agent should set --field_mhz 300. Second-order effects are more pronounced at lower field, and nmrsim handles these correctly.
If the user's experimental spectrum was recorded at 600 MHz → the agent should set --field_mhz 600. Peaks will be better resolved.
If the field strength is unknown → the agent should use the default (400 MHz) and note this assumption.

If/Then: Linewidth

If the user's spectrum shows sharp, well-resolved peaks → use default --linewidth 1.0.
If the user's spectrum shows broad peaks (e.g., viscous sample, paramagnetic species) → increase to --linewidth 3.0 or higher.
If predicting for deconvolution against a digitized reference → use --linewidth 1.0 (digitized spectra typically have natural linewidths).

Failure Modes

Failure	Symptom	Agent Action
Invalid SMILES	Script prints FAILED with "Invalid SMILES"	The agent should verify the SMILES with RDKit and correct it.
SPINUS returns no atoms	"SPINUS returned no atoms" error	Molecule may lack H atoms or be too complex. The agent should check and inform the user.
SPINUS network timeout	HTTP timeout error	The agent should retry once. If it fails again, NMRdb.org may be down. The agent should inform the user.
nmrsim QM simulation fails	WARNING in output, falls back to stick spectrum	Spin system too large (>11 spins) or numerical issue. The agent should note reduced accuracy.
Total nH in signals does not match molecular formula	Signal table has wrong proton count	Grouping heuristic may have failed. The agent should flag this to the user.

Environment

mamba activate nmr-agent

Install: conda-envs/nmr-agent/install.sh

Required packages: numpy, rdkit, requests, nmrsim.

References

Banfi, D. & Patiny, L., "www.nmrdb.org: Resurrecting and processing NMR spectra on-line", Chimia, 2008.
Aires-de-Sousa, J. et al., "SPINUS: prediction of 1H NMR spectra by neural networks", J. Chem. Inf. Model., 2002.
Sametz, G., "nmrsim: a Python library for NMR simulation", github.com/sametz/nmrsim.

Author: Jesus Diaz Sanchez Contact: GitHub @jdsanc

View source ↗

File metadata

name: chem-nmr-predict
description: Predict 1H NMR spectra from SMILES strings via NMRdb.org SPINUS neural network prediction and nmrsim quantum mechanical spin simulation.
category: chemistry

View original text

---
name: chem-nmr-predict
description: Predict 1H NMR spectra from SMILES strings via NMRdb.org SPINUS neural network prediction and nmrsim quantum mechanical spin simulation.
category: chemistry
---

# 1H NMR Spectrum Prediction

## When to Use This Skill

The agent should use this skill when:
- A SMILES string is known and the agent needs a predicted 1H NMR spectrum (ppm vs intensity) for that compound.
- The agent needs a signal list (chemical shifts, multiplicities, coupling constants, proton counts) for a compound.
- Reference spectra are needed for mixture deconvolution (called by the `chem-nmr-analysis` skill).
- The user wants to compare a predicted spectrum against an experimental one for structure confirmation.

## When NOT to Use This Skill

- **The user already has an experimental or digitized spectrum file** — no prediction is needed; the agent should use the existing file directly.
- **The user has a compound name but not a SMILES** — the agent should first resolve the name to SMILES using the `drug-db-pubchem` skill, then call this skill.
- **13C NMR prediction** — this skill predicts 1H NMR only. The NMRdb.org SPINUS endpoint does not support 13C.
- **Polymers, organometallics, or molecules with >50 heavy atoms** — the SPINUS neural network may not produce reliable predictions, and nmrsim QM simulation is limited to ~11 coupled spins per spin system.
- **The user asks about reaction products or mixture composition** — the agent should use `chem-nmr-analysis` instead, which calls this skill internally.

---

## Workflow: SMILES → Predicted 1H NMR

### Step 1 — Ensure SMILES Are Available

If the user provides compound names instead of SMILES, the agent should first resolve them:

```bash
# Env: base-agent
python .agents/skills/drug-db-pubchem/scripts/query_pubchem.py \
--name "camphor" --outdir <research_dir>/pubchem/
```

The agent should extract `CanonicalSMILES` from the JSON output.

**If PubChem returns no results**, the agent should try alternate names or ask the user to provide the SMILES directly.

### Step 2 — Predict NMR Spectra

```bash
# Env: nmr-agent
python .agents/skills/chem-nmr-predict/scripts/predict_nmr.py \
--smiles "<smiles_1>" "<smiles_2>" \
--names "compound1" "compound2" \
--field_mhz 400 \
--output_dir <research_dir>/nmr_predictions/
```

**Arguments:**
- `--smiles` (required): one or more SMILES strings.
- `--names`: human-readable labels for filenames. If omitted, defaults to `comp0`, `comp1`, etc. The agent should always provide meaningful names.
- `--field_mhz`: spectrometer frequency in MHz (default: 400). The agent should match the field strength of the user's experimental spectrum if known.
- `--linewidth`: Lorentzian FWHM in Hz (default: 1.0). The agent should increase this (e.g., 2.0–5.0) if the user's experimental spectrum has broad lines.
- `--n_points`: spectrum resolution (default: 8192). The agent should not change this unless the user requests higher resolution.
- `--output_dir`: where to save results.

**Outputs per compound:**
- `<name>.xy` — two-column tab-separated file (ppm, intensity), descending ppm. Compatible with all NMR processing tools and the `chem-nmr-analysis` deconvolution scripts.
- `<name>_signals.csv` — signal table with columns: `shift_ppm`, `multiplicity`, `J_Hz`, `nH`.
- `predictions.json` — manifest listing all found/failed compounds and parameters.

### Step 3 — Verify Predictions

After prediction, the agent must:

1. **Check the manifest** (`predictions.json`) for any failed compounds.
2. **Read the signal table** (`_signals.csv`) and verify it is chemically reasonable:
- The total number of protons across all signals should match the molecular formula.
- Chemical shifts should be in expected ranges (e.g., alkyl 0–2 ppm, aromatic 6–8 ppm, aldehyde 9–10 ppm).
3. **If the user has an experimental spectrum**, the agent should overlay them using `chem-nmr-analysis`'s `plot.py` for visual comparison.

**If SPINUS returns no atoms for a SMILES** → the SMILES may be invalid, the molecule may lack hydrogen atoms (e.g., CCl4), or the molecule may be too complex. The agent should:
1. Verify the SMILES is valid (try parsing with RDKit).
2. Check if the molecule actually has hydrogen atoms.
3. If valid but SPINUS fails, inform the user that prediction is unavailable for this compound.

**If nmrsim simulation fails** → the script falls back to a stick spectrum (chemical shifts only, no multiplet structure). The agent should note this in its response — the predicted spectrum will lack splitting patterns but chemical shifts will still be approximate.

---

## If/Then: Field Strength Matching

- **If the user's experimental spectrum was recorded at 300 MHz** → the agent should set `--field_mhz 300`. Second-order effects are more pronounced at lower field, and nmrsim handles these correctly.
- **If the user's experimental spectrum was recorded at 600 MHz** → the agent should set `--field_mhz 600`. Peaks will be better resolved.
- **If the field strength is unknown** → the agent should use the default (400 MHz) and note this assumption.

## If/Then: Linewidth

- **If the user's spectrum shows sharp, well-resolved peaks** → use default `--linewidth 1.0`.
- **If the user's spectrum shows broad peaks** (e.g., viscous sample, paramagnetic species) → increase to `--linewidth 3.0` or higher.
- **If predicting for deconvolution against a digitized reference** → use `--linewidth 1.0` (digitized spectra typically have natural linewidths).

---

## Failure Modes

| Failure | Symptom | Agent Action |
|---|---|---|
| Invalid SMILES | Script prints FAILED with "Invalid SMILES" | The agent should verify the SMILES with RDKit and correct it. |
| SPINUS returns no atoms | "SPINUS returned no atoms" error | Molecule may lack H atoms or be too complex. The agent should check and inform the user. |
| SPINUS network timeout | HTTP timeout error | The agent should retry once. If it fails again, NMRdb.org may be down. The agent should inform the user. |
| nmrsim QM simulation fails | WARNING in output, falls back to stick spectrum | Spin system too large (>11 spins) or numerical issue. The agent should note reduced accuracy. |
| Total nH in signals does not match molecular formula | Signal table has wrong proton count | Grouping heuristic may have failed. The agent should flag this to the user. |

---

## Environment

```bash
mamba activate nmr-agent
```

Install: `conda-envs/nmr-agent/install.sh`

Required packages: `numpy`, `rdkit`, `requests`, `nmrsim`.

---

## References

- Banfi, D. & Patiny, L., "www.nmrdb.org: Resurrecting and processing NMR spectra on-line", *Chimia*, 2008.
- Aires-de-Sousa, J. et al., "SPINUS: prediction of 1H NMR spectra by neural networks", *J. Chem. Inf. Model.*, 2002.
- Sametz, G., "nmrsim: a Python library for NMR simulation", [github.com/sametz/nmrsim](https://github.com/sametz/nmrsim).

---

**Author:** Jesus Diaz Sanchez
**Contact:** [GitHub @jdsanc](https://github.com/jdsanc)

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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 script uses `requests.get` without an explicit timeout, which could cause the agent to hang indefinitely if the NMRdb.org service is unresponsive.
Network errors (e.g., connection failure, HTTP errors) are not explicitly handled in the script; the agent may receive an unhandled exception instead of a graceful failure message.
The SKILL.md does not mention any rate‑limiting or usage policy for the NMRdb.org API, which could lead to unintended load or service abuse.
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 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-nmr-predict/SKILL.md @ d1f7ecfda2cd

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

Quality

69/100

Promising

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56/100

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    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 64/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "learningmatter-mit-chem-nmr-predict (chem-nmr-predict)",
      "install_command": "npx skills add learningmatter-mit/AtomisticSkills --skill chem-nmr-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-nmr-predict",
      "task": "Use chem-nmr-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-nmr-predict",
    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-nmr-predict",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-nmr-predict/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-nmr-predict&task=Use%20chem-nmr-predict%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-nmr-predict%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-nmr-predict%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-nmr-predict/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-nmr-predict"
  }
}

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

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