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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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Harga belum dikonfirmasi★ 161 Star GitHubDirektori diperbarui · 6 Sep 2026agent-skill

Ringkasan

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

Lihat sumber ↗

Metadata berkas

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]

Lihat teks asli

---
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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Lisensi: MIT
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Lisensi: 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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Sumber dan catatan penggunaan

Terindeks

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Repositori sumber: learningmatter-mit/AtomisticSkills
Lisensi: MIT
Versi: 1.0.0
Push GitHub terakhir: 3 Sep 2026
Direktori diperbarui: 6 Sep 2026
Jalur instruksi: .agents/skills/chem-docking-void/SKILL.md @ d1f7ecfda2cd

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

Kualitas

66/100

Menjanjikan

Kepercayaan

57/100

Do not auto-install

Audit

73/100

Perlu ditinjau

Buka audit lengkap Lihat laporan evaluasi

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: —
Hasil: —

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Detail lainnya

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    "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"
  }
}

Untuk kreator

Sumber listing

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Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: learningmatter-mit
Sumber: learningmatter-mit/AtomisticSkills
Diindeks oleh: Indeks komunitas OpenAgentSkill

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Listing Diindeks Registry ini dikaitkan dengan learningmatter-mit, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

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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)
[![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)

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Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.

Ringkasan

chem-docking-void

Goal

Instructions

1. Identify Inputs

2. Basic Docking Run

3. Tuning Hyperparameters

Meaning of Key Hyperparameters:

Constraints

References

Tinjau sumber

Harga dan biaya penggunaan

Mulai dengan tugas kecil

Sumber dan catatan penggunaan

Akses agent

Skill terkait

Untuk kreator

Diindeks Registry

Klaim listing skill ini

Tambahkan badge bukti ke README Anda

Sinyal komunitas