diffdock-nim

name	diffdock-nim
description	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.
license	Apache-2.0 AND CC-BY-4.0
compatibility	requests>=2.28
allowed-tools	Bash, Read, Write, AskUserQuestion

DiffDock NIM

Predict protein-ligand binding poses with blind docking. Use this SKILL.md 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 local setup answers, copy the preflight below exactly. Keep the optional .env load, NVIDIA_API_KEY fallback, LOCAL_NIM_CACHE, NVIDIA_VISIBLE_DEVICES=0 default, --shm-size=2G, and both --ulimit flags.

set -a
[ -f .env ] && . ./.env
set +a

if [ -z "${NGC_API_KEY:-}" ] && [ -n "${NVIDIA_API_KEY:-}" ]; then
  export NGC_API_KEY="$NVIDIA_API_KEY"
fi
: "${NGC_API_KEY:?Set NGC_API_KEY or NVIDIA_API_KEY}"
: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"

echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

export NIM_TEST_GPU="${NIM_TEST_GPU:-0}"
mkdir -p "${LOCAL_NIM_CACHE}"
chmod 777 "${LOCAL_NIM_CACHE}"

docker run --rm -it --name diffdock-nim \
  --runtime=nvidia \
  -e NVIDIA_VISIBLE_DEVICES="${NIM_TEST_GPU}" \
  --shm-size=2G \
  --ulimit memlock=-1 \
  --ulimit stack=67108864 \
  -e NGC_API_KEY \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/mit/diffdock:2.2.0

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.environ['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.

poses = result["ligand_positions"]
scores = result["position_confidence"]
for rank, (pose_sdf, score) in enumerate(zip(poses, scores), start=1):
    filename = f"pose_{rank}_conf{score:.3f}.sdf"
    with open(filename, "w", encoding="utf-8") as handle:
        handle.write(pose_sdf)
    print(f"pose {rank}: confidence={score:.4f} saved={filename}")
print(f"best pose confidence: {scores[0]:.4f}")

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/.