Install Diffdock Nim
Pick your agent. Every command is ready to paste.
npx skills add NVIDIA/skills --skill diffdock-nimThe open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.
01
Install it
Run the command above, or upload the folder in Claude.
02
Ask naturally
Describe the task. Your agent loads diffdock-nim when the request matches its description.
03
Name it if needed
If it does not trigger, say “use the diffdock-nim skill”. In Codex, type $diffdock-nim.
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:
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.
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
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. Maxtime_divisions: 20. Maxsteps: 18. - Single GPU; local minimum is about 24 GB VRAM.
422: invalidligand_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/.
Tips for Diffdock Nim
- • Start a fresh session after installing so your agent picks up the new skill.
- • It bundles reference files. The agent reads them only when needed, so they cost no context until then.
- • It is part of a pack with 30 skills. Install only the ones you need with --skill to keep triggers precise.
- • If two skills overlap, mention the one you want by name.
Questions about Diffdock Nim
What does the Diffdock Nim 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.
How do I install Diffdock Nim?
Run "npx skills add NVIDIA/skills --skill diffdock-nim" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.
Does Diffdock Nim work in Cursor and Codex?
Yes. Diffdock Nim uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.
Is Diffdock Nim free?
Diffdock Nim is open source on GitHub under the Apache-2.0 license. It is free to install; check the license before commercial use.
Who made Diffdock Nim?
Diffdock Nim is published by NVIDIA in the NVIDIA/skills repository, together with 29 other skills.

