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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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价格未确认★ 161 GitHub Stars目录更新于 · 2026年9月6日agent-skill

概览

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

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

Author: Mingrou Xie Contact: GitHub @mingrouxie

查看来源 ↗
文件元数据
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]
查看原始文本
---
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)

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许可证: 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
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从一个小任务开始

  1. 1阅读来源，确认输入、预期输出、依赖和权限。
  2. 2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
  3. 3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

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来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库
learningmatter-mit/AtomisticSkills
许可证
MIT
版本
1.0.0
最近 GitHub 推送
2026年9月3日
目录更新于
2026年9月6日
技能指令路径
.agents/skills/chem-docking-void/SKILL.md @ d1f7ecfda2cd

版本来自目录元数据，使用前请核实来源发布记录。

质量

66/100

有潜力

信任

57/100

Do not auto-install

审计

73/100

需审查

打开完整审计查看评估报告
  • 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
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    "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.",
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    "web": "https://www.openagentskill.com/skills/learningmatter-mit-chem-docking-void",
    "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"
  }
}

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[![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)
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[![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)
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