Diffdock Nim logo

Diffdock Nim

Organization
NVIDIA-BioNeMo
diffdock-nim

Run DiffDock molecular docking via NVIDIA NIM to predict small-molecule binding poses against protein targets. Use for DiffDock, molecular docking, ligand docking, blind docking, SMILES or SDF ligands, ranked poses, confidence scores, hosted NVIDIA API, or local Docker deployment.

Overview

PublisherNVIDIA-BioNeMo
Repositorybionemo-agent-toolkit
Skill namediffdock-nim
Stars
502
Forks
78
Bundled files
10
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by NVIDIA-BioNeMo on GitHub. Read the source before you install it.

Installation

Install the Diffdock Nim AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/nim-skills/diffdock-nim
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit.git /tmp/bionemo-agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/bionemo-agent-toolkit/nim-skills/diffdock-nim .claude/skills/diffdock-nim
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Diffdock Nim in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Diffdock Nim on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Diffdock Nim is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DiffDock NIM

Predict protein-ligand binding poses with blind docking. Use this guide for first-pass hosted/local usage; load supplemental files only when needed:

  • references/api.md: exact hosted/local endpoints, schemas, Docker flags.
  • references/science.md: docking use cases, limits, and handoffs.
  • references/parameters.md: ligand formats, pose counts, diffusion controls.
  • references/validation.md: receptor, ligand, pose, and confidence checks.
  • references/examples.md: compact hosted/local and pose-saving patterns.

Choose Mode

Ask only when context is unclear:

Hosted NVIDIA API or local Docker NIM?

  • Hosted: https://health.api.nvidia.com/v1/biology/mit/diffdock
  • Local: http://localhost:8000/molecular-docking/diffdock/generate

The hosted and local paths differ. Local has no /v1/ prefix and uses the /molecular-docking/ route. Hosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for registry login, entitlement checks, and first-run model downloads; pass it into the container with -e NGC_API_KEY. Local inference requests use no auth header after readiness. Warm-cache key-free startup varies by image/version and should not be assumed.

Local Docker

For the exact local preflight (.env load, NVIDIA_API_KEY fallback, LOCAL_NIM_CACHE, NVIDIA_VISIBLE_DEVICES=0, --shm-size=2G, both --ulimit flags, docker login, and the docker run for nvcr.io/nim/mit/diffdock:2.2.0), copy the command block in references/api.md under Docker Reference verbatim.

Readiness:

bash
until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done

Prepare Inputs

Protein receptor must be ATOM records only. Strip headers, water, and HETATM.

python
from pathlib import Path
raw_pdb = Path("protein.pdb").read_text()
protein = "\n".join(line for line in raw_pdb.splitlines() if line.startswith("ATOM"))
if not protein:
    raise ValueError("protein.pdb has no ATOM records")

Ligand options:

  • SMILES: ligand = "CC(=O)OC1=CC=CC=C1C(=O)O"; ligand_file_type = "txt".
  • SDF: ligand = Path("ligand.sdf").read_text(); ligand_file_type = "sdf".
  • MOL2: ligand_file_type = "mol2".

Do not use "smiles" as ligand_file_type; SMILES is "txt".

Request Pattern

python
import os
import requests

HOSTED = True
url = (
    "https://health.api.nvidia.com/v1/biology/mit/diffdock"
    if HOSTED else "http://localhost:8000/molecular-docking/diffdock/generate"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
    headers["Authorization"] = f"Bearer {os.getenv('NGC_API_KEY')}"

payload = {
    "protein": protein,
    "ligand": ligand,
    "ligand_file_type": ligand_file_type,
    "num_poses": 10,
    "time_divisions": 20,
    "steps": 18,
    "save_trajectory": False,
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()

Save And Report Output

ligand_positions and position_confidence are parallel ranked lists. position_confidence[0] is the rank-1 pose confidence.

Save the ranked pose SDFs using the snippet in references/examples.md under Save Ranked Poses.

View pose SDF files with the receptor in PyMOL, ChimeraX, or UCSF Chimera. For pose sanity checks and confidence caveats, read references/validation.md.

Limits And Troubleshooting

  • Max num_poses: 100. Max time_divisions: 20. Max steps: 18.
  • Single GPU; local minimum is about 24 GB VRAM.
  • 422: invalid ligand_file_type, invalid SMILES/SDF, or no ATOM records.
  • Empty poses: validate receptor ATOM records and ligand parseability.
  • Local URL 404 usually means the wrong hosted path or an accidental /v1/.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Diffdock Nim AI skill do?

Run DiffDock molecular docking via NVIDIA NIM to predict small-molecule binding poses against protein targets. Use for DiffDock, molecular docking, ligand docking, blind docking, SMILES or SDF ligands, ranked poses, confidence scores, hosted NVIDIA API, or local Docker deployment.

Why use Diffdock Nim on TypingMind?

Because you install it once and use it with any model. Diffdock Nim is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Diffdock Nim in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/nim-skills/diffdock-nim. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Diffdock Nim?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Diffdock Nim?

As many as you like. As long as a model supports skills, you can use Diffdock Nim with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Diffdock Nim AI skill free?

It is published on GitHub by NVIDIA-BioNeMo. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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