Agent skill

Drug Docking Vina

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Dock small-molecule ligands into a protein receptor using AutoDock Vina (Python API) and save ranked poses + docking metadata for reproducible virtual screening.

MITAuto-check passedResearch & Science

Install Drug Docking Vina

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-vina -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-docking-vina --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/drug-docking-vina .claude/skills/drug-docking-vina && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "drug-docking-vina" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-docking-vina into .claude/skills/drug-docking-vina/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-docking-vina", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-docking-vina

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-vina -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-docking-vina --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/drug-docking-vina .agents/skills/drug-docking-vina && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "drug-docking-vina" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-docking-vina into .agents/skills/drug-docking-vina/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-docking-vina", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-vina -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-docking-vina --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/drug-docking-vina .cursor/skills/drug-docking-vina && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "drug-docking-vina" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-docking-vina into .cursor/skills/drug-docking-vina/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-docking-vina", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/drug-docking-vina

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-vina -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-docking-vina --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/drug-docking-vina .gemini/skills/drug-docking-vina && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "drug-docking-vina" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-docking-vina into .gemini/skills/drug-docking-vina/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-docking-vina", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-docking-vina

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-vina -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/drug-docking-vina .github/skills/drug-docking-vina && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "drug-docking-vina" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-docking-vina into .github/skills/drug-docking-vina/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-docking-vina", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-vina -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-docking-vina --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/drug-docking-vina .opencode/skills/drug-docking-vina && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "drug-docking-vina" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-docking-vina into .opencode/skills/drug-docking-vina/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-docking-vina", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: drug-docking-vina
GitHub stars: 176
Token cost: ~2.3k tokens
SKILL.md length: 716 words
Files: 10 (incl. scripts)
Skills in repo: 129
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Dock small-molecule ligands into a protein receptor using AutoDock Vina (Python API) and save ranked poses + docking metadata for reproducible virtual screening.

Works in 6 steps: Prepare receptor and ligand (recommended) → Define the docking search box (center +… → Run docking (single ligand) → …
Tasks that involve Drug discovery and cheminformatics
SKILL.md covers Goal, Instructions, Examples and Constraints, plus 1 more section
Runs Python scripts from its folder

What it does

Drug Docking Vina is an agent skill from learningmatter-mit/AtomisticSkills. Dock small-molecule ligands into a protein receptor using AutoDock Vina (Python API) and save ranked poses + docking metadata for reproducible virtual screening.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts (for example `examples/hiv1_protease/README.md`, `examples/hiv1_protease/output/docking_results.json` and `scripts/collect_results.py`).

It sits in Research & Science, covering Drug discovery and cheminformatics. It works with Python. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

When your agent uses it

Tasks that involve Drug discovery and cheminformatics

Example prompts

“/drug-docking-vina”

Requirements

Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

Prepare receptor and ligand (recommended)
Define the docking search box (center + size)
Run docking (single ligand)
Run docking (batch mode / virtual screening)
Collect results into a ranked CSV
Validation & interpretation (strongly recommended)

What it can do on your machine

Read from SKILL.md and the folder at commit 6257444. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- doi.org
- github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Drug Docking Vina loads about 2.3k tokens when it runs. Until then it costs about 45 tokens; SKILL.md has 716 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~45
When it runs · the whole SKILL.md, loaded when a task matches: ~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from learningmatter-mit/AtomisticSkills at commit 6257444, republished under its MIT licence (© learningmatter-mit). 716 words, ~2,305 tokens.

Download SKILL.mdSave it as .claude/skills/drug-docking-vina/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.

name: drug-docking-vina
description: Dock small-molecule ligands into a protein receptor using AutoDock Vina (Python API) and save ranked poses + docking metadata for reproducible virtual screening.
metadata.category: drug-discovery
metadata.venv: cpu

docking-vina

Goal

To perform molecular docking of one or more small-molecule ligands into a protein receptor using AutoDock Vina (>= 1.2.x) via its Python API, producing:

Ranked binding poses (PDBQT)
Docking scores (kcal/mol) and pose RMSDs
A machine-readable JSON report with full docking parameters for reproducibility

This skill is intended for pose generation and relative ranking, not rigorous binding free energy prediction. Please refer to the original Vina method (Trott & Olson, https://doi.org/10.1002/jcc.21334) and the AutoDock Vina repo (https://github.com/ccsb-scripps/AutoDock-Vina) for more details.

Instructions

1. Prepare receptor and ligand (recommended)

Docking accuracy is strongly affected by structure preparation (protonation, missing residues, cofactors, waters, tautomer states, etc.). Use:

protein-prep to generate *_prepared.pdbqt
ligand-prep to generate ligand *.pdbqt (consider multiple protomers/tautomers)

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/../drug-protein-prep/scripts/prepare_protein.py \
  --pdb_id 1HSG \
  --heterogens none \
  --missing_residues ignore \
  --output_dir docking/inputs/

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/../drug-ligand-prep/scripts/prepare_ligand.py \
  --smiles "CC(=O)Oc1ccccc1C(=O)O" \
  --name aspirin \
  --output_dir docking/inputs/

Best practice: if you have a co-crystal ligand, keep it as a positive control for redocking validation.

You must define the docking region. The most common approaches:

Redocking / known pocket: center on the co-crystallized ligand
Known active site residues: center on key catalytic residues
Blind docking: large box spanning the protein (slower and less reliable, so use cautiously)

If you have a reference ligand already positioned in the binding site (PDBQT), compute a reasonable box automatically:

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/compute_box_from_pdbqt.py \
  docking/inputs/reference_ligand.pdbqt \
  --padding 6.0 \
  --min_size 20.0 \
  --output_json docking/inputs/docking_box.json

This writes center_x/y/z and size_x/y/z you can paste into the docking command.

3. Run docking (single ligand)

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu+docking python ${CLAUDE_SKILL_DIR}/scripts/run_docking.py \
  --receptor docking/inputs/1HSG_prepared.pdbqt \
  --ligand docking/inputs/aspirin.pdbqt \
  --center_x 16.0 --center_y 25.0 --center_z 2.0 \
  --size_x 20 --size_y 20 --size_z 20 \
  --scoring vina \
  --exhaustiveness 32 \
  --n_poses 10 \
  --energy_range 3.0 \
  --min_rmsd 1.0 \
  --seed 42 \
  --cpu 0 \
  --output_dir docking/results/

Outputs:

docking/results/aspirin_docked.pdbqt
docking/results/docking_results.json

4. Run docking (batch mode / virtual screening)

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu+docking python ${CLAUDE_SKILL_DIR}/scripts/run_docking.py \
  --receptor docking/inputs/1HSG_prepared.pdbqt \
  --ligand_dir docking/inputs/ligands_pdbqt/ \
  --center_x 16.0 --center_y 25.0 --center_z 2.0 \
  --size_x 20 --size_y 20 --size_z 20 \
  --scoring vina \
  --exhaustiveness 16 \
  --n_poses 5 \
  --seed 42 \
  --cpu 0 \
  --output_dir docking/screening_results/

Tip: for batch docking, the script will compute Vina maps once (before loading ligands) to reduce repeated setup overhead.

5. Collect results into a ranked CSV

run_docking.py writes a machine-readable JSON that is good for reproducibility but not directly consumable by downstream analysis tools (such as drug-docking-analysis). Use collect_results.py to produce a ranked CSV that joins the docking scores with library metadata (SMILES, labels, microstate/parent IDs).

bash

# (stdlib only, venv/cpu project works)

# Combined JSON from run_docking.py
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/collect_results.py \
  --results docking/results/docking_results.json \
  --library_csv library/library_master.csv \
  --output_dir docking/analysis/

# Or a directory of per-ligand *_result.json files (SLURM array workflows)
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/collect_results.py \
  --results docking/results/ \
  --library_csv library/library_master.csv \
  --output_dir docking/analysis/

Library CSV requirements: must have a compound_id column. The compound_id value must match the ligand field in the docking JSON, which is the PDBQT filename stem set by drug-ligand-prep (e.g. indinavir.pdbqt -> indinavir). Any of these columns, when present, are passed through to the ranked CSV and are picked up by downstream analysis tools:

smiles (used by drug-docking-analysis for ligand efficiency metrics)
label (used for retrospective enrichment)
parent_compound_id and microstate_id (used for microstate aggregation when protomers/tautomers were enumerated during ligand prep)
pchembl (passed through for reference)

Output: docking_ranked.csv sorted by best_affinity (most negative first) with columns rank, compound_id, best_affinity, [passthrough columns], n_poses, runtime_s. Also writes docking_collect_summary.json with counts and the top 10.

Show full SKILL.md (312 more words)Show less

6. Validation & interpretation (strongly recommended)

Docking is approximate; good practice is to validate your protocol for a given target:

Redocking test: dock the co-crystal ligand back into the pocket and check whether the top pose reproduces the experimental pose (often RMSD < 2 Angstrom is used as a sanity check, but interpret in context).
Multiple runs / convergence: Vina's search is non-deterministic; increasing exhaustiveness and/or running multiple seeds can improve reliability.
Controls: include known actives/inactives or decoys; don't rely on a universal "score threshold".

Examples

Example: HIV-1 protease docking (1HSG + indinavir)

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/../drug-protein-prep/scripts/prepare_protein.py \
  --pdb_id 1HSG \
  --heterogens none \
  --missing_residues ignore \
  --output_dir hiv_docking/inputs/

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/../drug-ligand-prep/scripts/prepare_ligand.py \
  --smiles "CC(C)(C)NC(=O)C1CC2CCCCC2CN1CC(O)C(CC1=CC=CC=C1)NC(=O)C(CC(N)=O)NC(=O)C1=CC2=CC=CC=C2N1" \
  --name indinavir \
  --output_dir hiv_docking/inputs/

${CLAUDE_SKILL_DIR}/../../venv/run cpu+docking python ${CLAUDE_SKILL_DIR}/scripts/run_docking.py \
  --receptor hiv_docking/inputs/1HSG_prepared.pdbqt \
  --ligand hiv_docking/inputs/indinavir.pdbqt \
  --center_x 16.0 --center_y 25.0 --center_z 2.0 \
  --size_x 20 --size_y 20 --size_z 20 \
  --exhaustiveness 32 \
  --n_poses 10 \
  --output_dir hiv_docking/results/

Constraints

Environment: Requires cpu; docking runs in cpu+docking and receptor preparation in cpu+openmm.
AutoDock Vina: Requires AutoDock Vina Python bindings (vina package; typically Vina >= 1.2.x).
Input format: Receptor and ligands must be PDBQT.
Search space selection: Box center/size strongly affects accuracy and runtime; avoid unnecessarily large "blind docking" boxes unless justified.
Stochastic search: Results can vary between runs; use an explicit --seed for reproducibility and consider higher --exhaustiveness for difficult systems.
Scoring: Vina scores are not experimental delta-G; treat as approximate scoring for ranking/pose generation.

References (recommended reading)

Trott, O.; Olson, A. J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. J. Comput. Chem. 2010, 31, 455–461. https://doi.org/10.1002/jcc.21334
Eberhardt, J.; Santos-Martins, D.; Tillack, A. F.; Forli, S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. J. Chem. Inf. Model. 2021, 61, 3891–3898. https://doi.org/10.1021/acs.jcim.1c00203
Forli, S. Charting a Path to Success in Virtual Screening. Molecules 2015, 20, 18732–18758. https://doi.org/10.3390/molecules201018732
Paggi, J. M.; Pandit, A.; Dror, R. O. The Art and Science of Molecular Docking. Annu. Rev. Biochem. 2024, 93, 389–410. https://doi.org/10.1146/annurev-biochem-030222-120000
Feinstein, W. P.; Brylinski, M. Calculating an Optimal Box Size for Ligand Docking and Virtual Screening against Experimental and Predicted Binding Pockets. J. Cheminform. 2015, 7, 18. https://doi.org/10.1186/s13321-015-0067-5

Author: Matthew Cox Contact: GitHub @mcox3406

Files

SKILL.md and 9 other files (scripts) in skills/drug-docking-vina of learningmatter-mit/AtomisticSkills.

SKILL.md
examples/hiv1_protease/README.md
examples/hiv1_protease/inputs/1HSG_prepared.pdb
examples/hiv1_protease/inputs/1HSG_prepared.pdbqt
examples/hiv1_protease/inputs/indinavir/indinavir.pdbqt
examples/hiv1_protease/output/docking_results.json
examples/hiv1_protease/output/indinavir_docked.pdbqt
scripts/collect_results.py
scripts/compute_box_from_pdbqt.py
scripts/run_docking.py

Open the folder on GitHubat commit 6257444

Compare with similar skills

Drug Docking Vina next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Drug Docking Vina compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Drug Docking Vina this skilllearningmatter-mit/AtomisticSkills	176	—	~2.3k	Automated safety check: Pass	MIT	today
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills	48k	1 repos	~3k	Automated safety check: Notes	MIT	3 days ago
Edu Chem Reactionwy51ai/edulab	1.4k	—	~1.2k	Automated safety check: Pass	Apache-2.0	10 days ago
RDKit Conformer Generatorjinzhezenggroup/computational-chemistry-agent-skills	148	1 repos	~2.4k	Automated safety check: Pass	LGPL-3.0	2 days ago
RDKit Descriptors and Fingerprintsjinzhezenggroup/computational-chemistry-agent-skills	148	1 repos	~2.3k	Automated safety check: Pass	LGPL-3.0	2 days ago
Rowanlamm-mit/scienceclaw	244	4 repos	~3.1k	Automated safety check: Warn	Proprietary	1 mo ago

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Questions about Drug Docking Vina

What does Drug Docking Vina do?

Dock small-molecule ligands into a protein receptor using AutoDock Vina (Python API) and save ranked poses + docking metadata for reproducible virtual screening. Drug Docking Vina is an agent skill from learningmatter-mit/AtomisticSkills. Dock small-molecule ligands into a protein receptor using AutoDock Vina (Python API) and save ranked poses + docking metadata for reproducible virtual screening.

When should I use Drug Docking Vina?

Drug Docking Vina fits situations like: tasks that involve Drug discovery and cheminformatics.

How do I install Drug Docking Vina in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-vina -a claude-code`. Or copy the skill folder (skills/drug-docking-vina in learningmatter-mit/AtomisticSkills) into .claude/skills/drug-docking-vina in your project. Claude Code loads it when a task matches its description.

How do I install Drug Docking Vina in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-vina -a codex`. Or copy the skill folder (skills/drug-docking-vina in learningmatter-mit/AtomisticSkills) into .agents/skills/drug-docking-vina in your project. Codex loads it when a task matches its description.

Can I use Drug Docking Vina in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-vina -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drug-docking-vina, .gemini/skills/drug-docking-vina, .github/skills/drug-docking-vina and .opencode/skills/drug-docking-vina in your project.

What does Drug Docking Vina need to run?

Going by SKILL.md and its folder, Drug Docking Vina needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Drug Docking Vina access the network?

SKILL.md names 2 domains. As links in the text: doi.org and github.com. This is read from the text; nothing was executed.

Is Drug Docking Vina safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Drug Docking Vina use?

Drug Docking Vina is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Drug Docking Vina use?

About 2.3k tokens (SKILL.md is roughly 9.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Drug Docking Vina?

Skills that share tags, products or a category with Drug Docking Vina: DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars), Edu Chem Reaction (wy51ai/edulab, 1.4k stars), RDKit Conformer Generator (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars) and RDKit Descriptors and Fingerprints (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drug Docking Vina?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 2026.

Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Drug Docking Vina

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

docking-vina

Goal

Instructions

1. Prepare receptor and ligand (recommended)

2. Define the docking search box (center + size)

3. Run docking (single ligand)

4. Run docking (batch mode / virtual screening)

5. Collect results into a ranked CSV

6. Validation & interpretation (strongly recommended)

Examples

Example: HIV-1 protease docking (1HSG + indinavir)

Constraints

References (recommended reading)

Files

Compare with similar skills

Similar skills

More from learningmatter-mit/AtomisticSkills

Related

Questions about Drug Docking Vina