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Agent skill

Diffdock Molecular Docking

by aipoch in aipoch/medical-research-skills

Diffusion-based molecular docking to predict 3D ligand–protein binding poses (blind docking) with confidence scoring; use when you need pose prediction for drug discovery or virtual screening.

MITAuto-check passedResearch & Science

Install Diffdock Molecular Docking

skills CLI
$ npx skills add aipoch/medical-research-skills --skill diffdock-molecular-docking -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills diffdock-molecular-docking --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Evidence Insight/diffdock-molecular-docking' .claude/skills/diffdock-molecular-docking && 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/

Facts

Skill name
diffdock-molecular-docking
GitHub stars
2k
Token cost
~776 tokens
SKILL.md length
301 words
Files
6 (incl. scripts, references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Diffusion-based molecular docking to predict 3D ligand–protein binding poses (blind docking) with confidence scoring; use when you need pose prediction for drug discovery or virtual screening.

  • Works in 3 steps: Verify the Environment → Run Standard Inference (Single Docking) → Outputs
  • You need pose prediction for drug discovery
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Runs Python scripts from its folder; calls python

What it does

Diffdock Molecular Docking is an agent skill from aipoch/medical-research-skills. Diffusion-based molecular docking to predict 3D ligand–protein binding poses (blind docking) with confidence scoring; use when you need pose prediction for drug discovery or virtual screening.

Its SKILL.md is about 780 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `diffdock-molecular-docking_audit_result_v1.json`, `references/confidence_and_limitations.md` and `references/parameters_reference.md`).

It sits in Research & Science, covering Drug discovery and cheminformatics. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • You need pose prediction for drug discovery
  • Virtual screening

Example prompts

  • “/diffdock-molecular-docking”

Requirements

  • Python 3

Workflow steps

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

  1. Verify the Environment
  2. Run Standard Inference (Single Docking)
  3. Outputs

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    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

Diffdock Molecular Docking loads about 776 tokens when it runs, and up to ~1.3k if it reads all its reference files. Until then it costs about 55 tokens; SKILL.md has 301 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~776
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.3k

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 aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 301 words, ~776 tokens.

Download SKILL.mdSave it as .claude/skills/diffdock-molecular-docking/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
diffdock-molecular-docking
description
Diffusion-based molecular docking to predict 3D ligand–protein binding poses (blind docking) with confidence scoring; use when you need pose prediction for drug discovery or virtual screening.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

DiffDock Molecular Docking

When to Use

  • Blind docking when you have a protein structure (PDB) and a ligand (SMILES) but no known binding site.
  • Pose prediction to generate multiple plausible 3D binding conformations and rank them.
  • Virtual screening support to quickly evaluate candidate ligands by predicted binding poses and confidence.
  • Drug discovery workflows where you need automated docking outputs (SDF poses + scores) for downstream analysis.
  • Batch/advanced docking when running many ligand–protein pairs or using alternative inputs (e.g., sequence-based workflows; see references/workflows_examples.md).

Key Features

  • Diffusion generative sampling to produce diverse ligand binding poses.
  • Confidence model scoring to rank predicted poses.
  • Simple CLI inference for single protein–ligand docking.
  • Batch/advanced workflows documented in references/workflows_examples.md.
  • Structured outputs including ranked SDF pose files and a confidence score report.

Dependencies

  • Python (version not specified)
  • PyTorch (version not specified)
  • PyTorch Geometric / PyG (version not specified)
  • RDKit (version not specified)
  • ESM (version not specified)

Example Usage

1) Verify the Environment
bash
python scripts/setup_check.py
2) Run Standard Inference (Single Docking)

Dock a single ligand (SMILES) to a protein structure (PDB) and write results to an output directory:

bash
python scripts/inference_runner.py \
  --protein ./data/protein.pdb \
  --ligand "CC(=O)Oc1ccccc1C(=O)O" \
  --out_dir ./results

Arguments

  • --protein: Path to the protein PDB file.
  • --ligand: Ligand SMILES string.
  • --out_dir: Output directory (default: results/).
3) Outputs

After inference, the tool produces:

  • Ranked SDF pose files (e.g., rank1.sdf, rank2.sdf, ...), each containing a predicted 3D binding pose.
  • Confidence score report: confidence_scores.txt, listing the score for each ranked pose.

Implementation Details

  • Pose generation: Uses a diffusion-based generative model to sample multiple candidate ligand poses relative to the protein target.
  • Ranking: A separate confidence model assigns a score to each sampled pose; poses are sorted by this score and saved as rank*.sdf.
  • Parameterization:
    • For the complete CLI argument list and defaults, see references/parameters_reference.md.
    • For confidence interpretation, known limitations, and expected accuracy/scope, see references/confidence_and_limitations.md.
  • Advanced workflows: Batch processing and alternative input configurations are documented in references/workflows_examples.md.

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in scientific-skills/Evidence Insight/diffdock-molecular-docking of aipoch/medical-research-skills.

  • SKILL.md
  • diffdock-molecular-docking_audit_result_v1.json
  • references/confidence_and_limitations.md
  • references/parameters_reference.md
  • scripts/inference_runner.py
  • scripts/setup_check.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Diffdock Molecular Docking 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.

Diffdock Molecular Docking compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Diffdock Molecular Docking this skillaipoch/medical-research-skills2k—~776Automated safety check: PassMIT
MolecodeAtomFlow-AI/MoleCode305—~1.9kAutomated safety check: PassMIT
Drug DiscoveryTommy-yw/RunbookHermes5461 repos~2.3kAutomated safety check: PassMIT
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills48k1 repos~3kAutomated safety check: NotesMIT
Biomedical Analysis Dispatchxjtulyc/MedgeClaw6171 repos~2kAutomated safety check: PassNone
Edu Chem Reactionwy51ai/edulab1.4k—~1.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Diffdock Molecular Docking

What does Diffdock Molecular Docking do?

Diffusion-based molecular docking to predict 3D ligand–protein binding poses (blind docking) with confidence scoring; use when you need pose prediction for drug discovery or virtual screening. Diffdock Molecular Docking is an agent skill from aipoch/medical-research-skills. Diffusion-based molecular docking to predict 3D ligand–protein binding poses (blind docking) with confidence scoring; use when you need pose prediction for drug discovery or virtual screening.

When should I use Diffdock Molecular Docking?

Diffdock Molecular Docking fits situations like: you need pose prediction for drug discovery; virtual screening.

How do I install Diffdock Molecular Docking in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill diffdock-molecular-docking -a claude-code`. Or copy the skill folder (scientific-skills/Evidence Insight/diffdock-molecular-docking in aipoch/medical-research-skills) into .claude/skills/diffdock-molecular-docking in your project. Claude Code loads it when a task matches its description.

How do I install Diffdock Molecular Docking in Codex?

Run `npx skills add aipoch/medical-research-skills --skill diffdock-molecular-docking -a codex`. Or copy the skill folder (scientific-skills/Evidence Insight/diffdock-molecular-docking in aipoch/medical-research-skills) into .agents/skills/diffdock-molecular-docking in your project. Codex loads it when a task matches its description.

Can I use Diffdock Molecular Docking 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 aipoch/medical-research-skills --skill diffdock-molecular-docking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/diffdock-molecular-docking, .gemini/skills/diffdock-molecular-docking, .github/skills/diffdock-molecular-docking and .opencode/skills/diffdock-molecular-docking in your project.

What does Diffdock Molecular Docking need to run?

Going by SKILL.md and its folder, Diffdock Molecular Docking needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Diffdock Molecular Docking access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Diffdock Molecular Docking 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 Diffdock Molecular Docking use?

Diffdock Molecular Docking is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Diffdock Molecular Docking use?

About 776 tokens (SKILL.md is roughly 3.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 557 tokens, read only when the agent opens those files.

What are the alternatives to Diffdock Molecular Docking?

Skills that share tags, products or a category with Diffdock Molecular Docking: Molecode (AtomFlow-AI/MoleCode, 305 stars), Drug Discovery (Tommy-yw/RunbookHermes, 546 stars), DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars) and Biomedical Analysis Dispatch (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Diffdock Molecular Docking?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.