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.
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. 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/.