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diffdock-nim

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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.

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Source SKILL.md: https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/blob/HEAD/plugins/bionemo-agent-toolkit/skills/diffdock-nim/SKILL.md

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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/.