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chem-docking-void

Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes.

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価格未確認★ 161 GitHub スター登録情報の更新日 · 2026年9月6日agent-skill

概要

Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes.

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chem-docking-void

Goal

To perform molecular docking of a small-molecule ligand into a porous material structure (CIF format) using the VOID library. This skill aims to automatically generate a robust sampling of guest conformers using RDKit, optimize them, and then distribute them throughout the host framework using Voronoi-based cluster sampling and physics-informed collision filtering.

This will output:

Ranked docked complexes saved individually as standard CIF files.
A metadata summary (docking_results.json) capturing the generation parameters, associated RDKit conformer energies, and matched pose IDs.

Instructions

1. Identify Inputs

You will need:

The SMILES string of your guest molecule.
The CIF file path to your porous material (e.g. Zeolites, MOFs).

2. Basic Docking Run

A standard run accepts the chemical inputs and saves outputs to a designated folder.

# Env: atomistic-agent
python .agents/skills/chem-docking-void/scripts/run_docking.py \
  --smiles "CC12C3C4C5C6C1C7C2C3C4C5C67" \
  --host_cif /path/to/host/material.cif \
  --output_dir output/docked_poses \
  --num_conformers 5

(The SMILES here represents Adamantane or similar structures for testing.)

3. Tuning Hyperparameters

The clustering map and acceptance rates are highly sensitive to VOID's search parameters. Use the advanced arguments for dense loading or strict spatial tolerances:

python .agents/skills/chem-docking-void/scripts/run_docking.py \
  --smiles "CC(=O)Oc1ccccc1C(=O)O" \
  --host_cif /path/to/host/MOF.cif \
  --output_dir output/docked_poses \
  --num_conformers 10 \
  --threshold 1.8 \
  --attempts 2000 \
  --structs_per_loading 5 \
  --num_clusters 150 \
  --max_loading 1 \
  --max_subdock 200 \
  --remove_species "H2O" "Na"

Meaning of Key Hyperparameters:

--num_conformers: (RDKit) How many of the lowest-energy 3D geometries to test.
--threshold: The acceptable minimum distance (Å) between the host atoms and guest atoms. A lower value allows tighter squeezes but risks atomic clashes.
--attempts: How many random translation/rotation insertion guesses the Subdocker makes per BatchDocker queue limit.
--structs_per_loading: Maximum number of successful geometries to export out of all validated matches, per conformer tested.
--num_clusters & --min_radius: Settings for the VoronoiClustering sampler that determine the density and minimum pore volume of chosen docking nodes within the material.
--remove_species: Pre-cleans the CIF file of specified elements (like free solvent) before docking.

Constraints

Environment: Requires atomistic-agent conda environment where VOID, rdkit, and pymatgen are accessible.
Loading Size: By default, this script handles single-guest loadings per unit cell. Heavy multiple guest loading (--max_loading > 1) may scale exponentially in computational time depending on pore size.
Outputs: Everything is standardized to CIF files for compatibility with subsequent DFT or MLIP workflows.

References

VOID Library
Pymatgen pymatgen.core.Structure and pymatgen.core.Molecule
RDKit cheminformatics (MMFF94 structural optimization)

Author: Mingrou Xie Contact: GitHub @mingrouxie

ソースを見る ↗

ファイルのメタデータ

name: chem-docking-void
description: Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes.
category: [materials, chemistry]

元のテキストを表示

---
name: chem-docking-void
description: Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes.
category: [materials, chemistry]
---

# chem-docking-void

## Goal
To perform molecular docking of a small-molecule ligand into a porous material structure (CIF format) using the **VOID** library. This skill aims to automatically generate a robust sampling of guest conformers using RDKit, optimize them, and then distribute them throughout the host framework using Voronoi-based cluster sampling and physics-informed collision filtering.

This will output:
- Ranked docked complexes saved individually as standard CIF files.
- A metadata summary (`docking_results.json`) capturing the generation parameters, associated RDKit conformer energies, and matched pose IDs.

## Instructions

### 1. Identify Inputs
You will need:
- The **SMILES** string of your guest molecule.
- The **CIF** file path to your porous material (e.g. Zeolites, MOFs).

### 2. Basic Docking Run

A standard run accepts the chemical inputs and saves outputs to a designated folder.

```bash
# Env: atomistic-agent
python .agents/skills/chem-docking-void/scripts/run_docking.py \
--smiles "CC12C3C4C5C6C1C7C2C3C4C5C67" \
--host_cif /path/to/host/material.cif \
--output_dir output/docked_poses \
--num_conformers 5
```
*(The SMILES here represents Adamantane or similar structures for testing.)*

### 3. Tuning Hyperparameters

The clustering map and acceptance rates are highly sensitive to VOID's search parameters. Use the advanced arguments for dense loading or strict spatial tolerances:

```bash
python .agents/skills/chem-docking-void/scripts/run_docking.py \
--smiles "CC(=O)Oc1ccccc1C(=O)O" \
--host_cif /path/to/host/MOF.cif \
--output_dir output/docked_poses \
--num_conformers 10 \
--threshold 1.8 \
--attempts 2000 \
--structs_per_loading 5 \
--num_clusters 150 \
--max_loading 1 \
--max_subdock 200 \
--remove_species "H2O" "Na"
```

#### Meaning of Key Hyperparameters:
- `--num_conformers`: (RDKit) How many of the lowest-energy 3D geometries to test.
- `--threshold`: The acceptable minimum distance (Å) between the host atoms and guest atoms. A lower value allows tighter squeezes but risks atomic clashes.
- `--attempts`: How many random translation/rotation insertion guesses the `Subdocker` makes per `BatchDocker` queue limit.
- `--structs_per_loading`: Maximum number of successful geometries to export out of all validated matches, per conformer tested.
- `--num_clusters` & `--min_radius`: Settings for the `VoronoiClustering` sampler that determine the density and minimum pore volume of chosen docking nodes within the material.
- `--remove_species`: Pre-cleans the CIF file of specified elements (like free solvent) before docking.

## Constraints

* **Environment**: Requires `atomistic-agent` conda environment where `VOID`, `rdkit`, and `pymatgen` are accessible.
* **Loading Size**: By default, this script handles single-guest loadings per unit cell. Heavy multiple guest loading (`--max_loading > 1`) may scale exponentially in computational time depending on pore size.
* **Outputs**: Everything is standardized to CIF files for compatibility with subsequent DFT or MLIP workflows.

## References

1. VOID Library
2. Pymatgen `pymatgen.core.Structure` and `pymatgen.core.Molecule`
3. RDKit cheminformatics (MMFF94 structural optimization)

---
**Author:** Mingrou Xie
**Contact:** [GitHub @mingrouxie](https://github.com/mingrouxie)

ソースを確認

価格と実行コスト

Skill の入手: 価格未確認
実行: 実行要件は未確認です。Agent・API・サービス料金を提供元で確認してください。
ライセンス: MIT
価格未確認: 価格は未確認です。既存のソースとインストールリンクは利用できます。

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手順のパスを記録しています。実行テスト、安全保証、互換性認証ではありません。

インストール前にレビュー: 自動インストールを避ける

ライセンス: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Minor inconsistency: SKILL.md mentions 'atomistic-agent' environment, while example uses 'void-agent'.
The script is not fully shown in the excerpt, but the provided portion appears functional.
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

完全な監査を開く

ツール一覧はメタデータであり、互換性のテスト結果ではありません。プロンプトは提案です。

小さなタスクから始める

1ソースを読み、入力、出力、依存関係、権限を確認します。
2Agent に計画を求め、設定と費用を承認してから隔離環境でテストします。
3出力と変更ファイルを確認し、実行した結果だけを報告します。再現用にソースの版を保存します。

依存関係、API キー、外部サービスの料金をソースで確認してください。公開リポジトリでも全サービスが無料とは限りません。

出典と利用上の注意

登録済み

メタデータと審査情報は参考です。人気、ソースの発見、実行成功は別の事実です。

ソースリポジトリ: learningmatter-mit/AtomisticSkills
ライセンス: MIT
バージョン: 1.0.0
最終 GitHub プッシュ: 2026年9月3日
登録情報の更新日: 2026年9月6日
手順のパス: .agents/skills/chem-docking-void/SKILL.md @ d1f7ecfda2cd

登録されたバージョンです。ソースのリリース情報を確認してください。

品質

66/100

有望

信頼

57/100

Do not auto-install

監査

73/100

要レビュー

完全な監査を開く評価レポートを見る

Dependency or permission surface needs review
Permission surface may require sandboxing
Minor inconsistency: SKILL.md mentions 'atomistic-agent' environment, while example uses 'void-agent'.
The script is not fully shown in the excerpt, but the provided portion appears functional.
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

Verified installs: —
成果: —

コピーはインストールではありません。件数は成功報告に基づき、品質全体を保証しません。

Agent 接続

Registry API 経由で判断、信頼、監査、ユースケース、インストールのシグナルを提供し、UI をスクレイピングせずに Agent が順位付けできます。

Install API を開く LLM テキスト形式 Manifest を開く自動解決プラン

詳細情報

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    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-docking-void",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-docking-void/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-docking-void&task=Use%20chem-docking-void%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-docking-void%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-docking-void%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-docking-void/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-docking-void"
  }
}

クリエイター向け

掲載元

Registry により登録

申請可能

この掲載は公開ソースから登録されており、メンテナー申請が承認されるまで公式として表示されません。

作成者: learningmatter-mit
ソース: learningmatter-mit/AtomisticSkills
インデックス作成者: OpenAgentSkill コミュニティインデックス

帰属は公開リポジトリまたは作成者プロフィールにリンクされています。作成者は掲載を申請して所有権シグナルを更新できます。

このスキルを申請

所有者の申請

このスキル掲載を申請

この Registry により登録掲載は learningmatter-mit に帰属していますが、まだ公式として表示されていません。申請すると、確認済み所有者シグナルが追加され、今後の公開、インストール、監査更新の信頼性が高まります。

共有キット

クリエイター被リンクキット

README にエビデンスバッジを追加

開発者がリポジトリを評価する場所で、正規掲載、現在の信頼・監査シグナル、実際の Agent-Proven エビデンスを表示します。

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-docking-void?metric=listed&label=Listed)](https://www.openagentskill.com/skills/learningmatter-mit-chem-docking-void?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-docking-void?metric=trust&label=Trust)](https://www.openagentskill.com/skills/learningmatter-mit-chem-docking-void?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-docking-void?metric=audit&label=Audit)](https://www.openagentskill.com/skills/learningmatter-mit-chem-docking-void/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-docking-void?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/learningmatter-mit-chem-docking-void?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

バッジをプレビュー監査を開くクリエイターキット

コミュニティシグナル

このスキルが Agent ワークフローに役立つかを共有してください。集約されたフィードバックがランキングを改善します。

概要

chem-docking-void

Goal

Instructions

1. Identify Inputs

2. Basic Docking Run

3. Tuning Hyperparameters

Meaning of Key Hyperparameters:

Constraints

References

ソースを確認

価格と実行コスト

小さなタスクから始める

出典と利用上の注意

Agent 接続

関連スキル

クリエイター向け

Registry により登録

このスキル掲載を申請

README にエビデンスバッジを追加

コミュニティシグナル