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chem-conformer-search

Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.

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가격 미확인★ 161 GitHub 스타목록 업데이트 · 2026년 9월 6일agent-skill

개요

Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.

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Molecular Conformer Search & Ranking

Goal

Generate a diverse ensemble of low-energy conformers for a given molecule. The workflow combines:

  1. Stochastic sampling using RDKit's ETKDG algorithm (Experimental Torsion Distance Geometry).
  2. High-accuracy relaxation using Machine Learning Interatomic Potentials (MLIPs) to get near-DFT quality geometries and energies.
  3. Deduplication and Boltzmann weighting to identify the most relevant conformers at finite temperature.

[!IMPORTANT] This skill is optimized for organic molecules and uses MACE-OFF23 models by default. For inorganic clusters, switch to MACE-OMAT or MatGL models.

  • MACE-OFF23: MACE-OFF23-small (default), MACE-OFF23-medium — trained on organic molecules (Env: mace-agent)
  • MACE-MH: MACE-MH-1 with head omol — multi-head model with molecular head (Env: mace-agent)
  • UMA: uma-s-1p1 with head omol — general molecular model (Env: fairchem-agent)

1. Prerequisites

  • Conda Environment: mace-agent (recommended as it includes both mace and rdkit).
  • Input: SMILES string or a structure file (.xyz, .sdf, .mol2, .pdb).

2. Methodology

  1. Generation: Generate N initial conformers using RDKit's EmbedMultipleConfs with ETKDGv3.
  2. Relaxation: Optimize the geometry of each conformer using the selected MLIP (fmax = 0.01 eV/Å).
  3. Deduplication/Clustering: Filter redundant conformers by simple RMSD thresholding (default), Hierarchical clustering, or K-Means clustering. Only the lowest-energy conformer in each cluster is kept.
  4. Ranking: Sort unique conformers by energy.
  5. Boltzmann Weighting: Calculate population probability $P_i$ at temperature $T$: $$P_i = \frac{e^{-(E_i - E_{min}) / k_B T}}{\sum_j e^{-(E_j - E_{min}) / k_B T}}$$

3. Usage

Basic Usage (SMILES)

Generate 30 conformers for a molecule (e.g., aspirin) and relax with MACE-OFF23:

# Env: mace-agent
python .agents/skills/chem-conformer-search/scripts/conformer_search.py \
    --smiles "CC(=O)Oc1ccccc1C(=O)O" \
    --num_conformers 30 \
    --output_dir research/aspirin_conformers
Advanced Usage (Structure File + Options)
# Env: mace-agent
python .agents/skills/chem-conformer-search/scripts/conformer_search.py \
    --structure my_molecule.sdf \
    --num_conformers 100 \
    --rms_threshold 0.5 \
    --dedup_threshold 0.1 \
    --temperature 298.15 \
    --model_type mace \
    --model_name MACE-OFF23-small \
    --device cuda \
    --output_dir research/my_molecule_search
Key Parameters
ArgumentDefaultDescription
--smiles-SMILES string of the molecule
--structure-Path to input structure file (alternative to SMILES)
--num_conformers50Number of initial conformers to generate with RDKit
--rms_threshold0.2RDKit pruning threshold (Å) to discard similar initial conformers
--clusteringrmsdMethod to filter conformers: rmsd, hierarchical, or kmeans
--dedup_threshold0.1Post-relaxation RMSD threshold (Å) to merge identical conformers or cut for hierarchical
--num_clusters5Number of clusters if --clustering kmeans is used
--energy_threshold0.5Max energy above global minimum (eV) to keep before RMSD comparison. Set to 0 to disable
--fmax0.01Force convergence criterion for relaxation (eV/Å)
--temperature298.15Temperature (K) for Boltzmann weighting
--model_typemaceMLIP backend (mace, matgl, fairchem)
--model_nameMACE-OFF23-smallSpecific model checkpoint to use

4. Output Files

The output directory will contain:

  1. conformer_results.json: A summary file containing:
    • List of unique conformers with their energies, relative energies, and Boltzmann weights.
    • Paths to the corresponding XYZ files.
    • Metadata (model used, parameters).
  2. conf_000.xyz, conf_001.xyz, ...: The relaxed structures of the unique conformers, sorted by energy (000 is the global minimum found).

5. Examples

See examples/aspirin for a complete example run on Acetylsalicylic acid.

6. Constraints

  • Environment: Use the conda environment matching the chosen model: mace-agent (MACE), fairchem-agent (FairChem/UMA), or matgl-agent (MatGL). All include RDKit.
  • Input: Either --smiles or --structure must be provided, but not both.
  • Molecule Type: Optimized for organic molecules. For inorganic clusters, switch to MACE-OMAT or MatGL models.
  • Non-periodic: All conformers are treated as non-periodic (isolated molecules).

7. References

  • Riniker, S.; Landrum, G. A., "Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation", J. Chem. Inf. Model., 2015, 55, 2562. DOI

Author: Bowen Deng Contact: GitHub @learningmatter-mit

소스 보기 ↗
파일 메타데이터
name: chem-conformer-search
description: Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
category: [chemistry]
원문 보기
---
name: chem-conformer-search
description: Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
category: [chemistry]
---

# Molecular Conformer Search & Ranking

## Goal

Generate a diverse ensemble of low-energy conformers for a given molecule. The workflow combines:
1.  **Stochastic sampling** using RDKit's ETKDG algorithm (Experimental Torsion Distance Geometry).
2.  **High-accuracy relaxation** using Machine Learning Interatomic Potentials (MLIPs) to get near-DFT quality geometries and energies.
3.  **Deduplication** and **Boltzmann weighting** to identify the most relevant conformers at finite temperature.

> [!IMPORTANT]
> This skill is optimized for **organic molecules** and uses `MACE-OFF23` models by default. For inorganic clusters, switch to `MACE-OMAT` or `MatGL` models.

### Recommended Models

- **MACE-OFF23**: `MACE-OFF23-small` (default), `MACE-OFF23-medium` — trained on organic molecules (Env: `mace-agent`)
- **MACE-MH**: `MACE-MH-1` with head `omol` — multi-head model with molecular head (Env: `mace-agent`)
- **UMA**: `uma-s-1p1` with head `omol` — general molecular model (Env: `fairchem-agent`)

## 1. Prerequisites

- **Conda Environment**: `mace-agent` (recommended as it includes both `mace` and `rdkit`).
- **Input**: SMILES string or a structure file (`.xyz`, `.sdf`, `.mol2`, `.pdb`).

## 2. Methodology

1.  **Generation**: Generate `N` initial conformers using RDKit's `EmbedMultipleConfs` with ETKDGv3.
2.  **Relaxation**: Optimize the geometry of *each* conformer using the selected MLIP (fmax = 0.01 eV/Å).
3.  **Deduplication/Clustering**: Filter redundant conformers by simple RMSD thresholding (default), Hierarchical clustering, or K-Means clustering. Only the lowest-energy conformer in each cluster is kept.
4.  **Ranking**: Sort unique conformers by energy.
5.  **Boltzmann Weighting**: Calculate population probability $P_i$ at temperature $T$:
    $$P_i = \frac{e^{-(E_i - E_{min}) / k_B T}}{\sum_j e^{-(E_j - E_{min}) / k_B T}}$$

## 3. Usage

### Basic Usage (SMILES)

Generate 30 conformers for a molecule (e.g., aspirin) and relax with MACE-OFF23:

```bash
# Env: mace-agent
python .agents/skills/chem-conformer-search/scripts/conformer_search.py \
    --smiles "CC(=O)Oc1ccccc1C(=O)O" \
    --num_conformers 30 \
    --output_dir research/aspirin_conformers
```

### Advanced Usage (Structure File + Options)

```bash
# Env: mace-agent
python .agents/skills/chem-conformer-search/scripts/conformer_search.py \
    --structure my_molecule.sdf \
    --num_conformers 100 \
    --rms_threshold 0.5 \
    --dedup_threshold 0.1 \
    --temperature 298.15 \
    --model_type mace \
    --model_name MACE-OFF23-small \
    --device cuda \
    --output_dir research/my_molecule_search
```

### Key Parameters

| Argument | Default | Description |
|:---|:---|:---|
| `--smiles` | - | SMILES string of the molecule |
| `--structure` | - | Path to input structure file (alternative to SMILES) |
| `--num_conformers` | 50 | Number of initial conformers to generate with RDKit |
| `--rms_threshold` | 0.2 | RDKit pruning threshold (Å) to discard similar initial conformers |
| `--clustering` | `rmsd` | Method to filter conformers: `rmsd`, `hierarchical`, or `kmeans` |
| `--dedup_threshold` | 0.1 | Post-relaxation RMSD threshold (Å) to merge identical conformers or cut for hierarchical |
| `--num_clusters` | 5 | Number of clusters if `--clustering kmeans` is used |
| `--energy_threshold` | 0.5 | Max energy above global minimum (eV) to keep before RMSD comparison. Set to 0 to disable |
| `--fmax` | 0.01 | Force convergence criterion for relaxation (eV/Å) |
| `--temperature` | 298.15 | Temperature (K) for Boltzmann weighting |
| `--model_type` | `mace` | MLIP backend (`mace`, `matgl`, `fairchem`) |
| `--model_name` | `MACE-OFF23-small` | Specific model checkpoint to use |

## 4. Output Files

The output directory will contain:

1.  **`conformer_results.json`**: A summary file containing:
    -   List of unique conformers with their energies, relative energies, and Boltzmann weights.
    -   Paths to the corresponding XYZ files.
    -   Metadata (model used, parameters).
2.  **`conf_000.xyz`, `conf_001.xyz`, ...**: The relaxed structures of the unique conformers, sorted by energy (000 is the global minimum found).

## 5. Examples

See `examples/aspirin` for a complete example run on Acetylsalicylic acid.

## 6. Constraints

- **Environment**: Use the conda environment matching the chosen model: `mace-agent` (MACE), `fairchem-agent` (FairChem/UMA), or `matgl-agent` (MatGL). All include RDKit.
- **Input**: Either `--smiles` or `--structure` must be provided, but not both.
- **Molecule Type**: Optimized for organic molecules. For inorganic clusters, switch to `MACE-OMAT` or `MatGL` models.
- **Non-periodic**: All conformers are treated as non-periodic (isolated molecules).

## 7. References

- Riniker, S.; Landrum, G. A., "Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation", *J. Chem. Inf. Model.*, 2015, 55, 2562. [DOI](https://doi.org/10.1021/acs.jcim.5b00654)

---

**Author:** Bowen Deng
**Contact:** [GitHub @learningmatter-mit](https://github.com/learningmatter-mit)

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learningmatter-mit/AtomisticSkills
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MIT
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1.0.0
최근 GitHub 푸시
2026년 9월 3일
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  • Quality score needs review
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      "installSafety": "standard package or runtime install path",
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    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 76,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "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": "Research and knowledge work",
    "scenario": "RAG and knowledge",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "Financial research output is not financial advice; require human review before any live investment decision."
  ],
  "agent_contract": {
    "task_input": "Use chem-conformer-search 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: 73/100 Strong shortlist",
      "Audit: 76/100 Needs review",
      "Safety: 36/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "learningmatter-mit-chem-conformer-search (chem-conformer-search)",
      "install_command": "npx skills add learningmatter-mit/AtomisticSkills --skill chem-conformer-search",
      "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-conformer-search",
      "task": "Use chem-conformer-search 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-conformer-search",
    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-conformer-search",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-conformer-search/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-conformer-search&task=Use%20chem-conformer-search%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-conformer-search%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-conformer-search%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-conformer-search/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-conformer-search"
  }
}

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