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diffdock-nim

diffdock-nim

github.com/nvidia-bionemoOther502257 installsUpdated 2d ago
Repository

About diffdock-nim

diffdock-nim is a skill guide for predicting protein–ligand binding poses with blind docking through hosted NVIDIA APIs or a local Docker NIM. It covers input preparation, pose output, and validation.

Key features

  1. 01Hosted and local docking guidance
  2. 02Protein and ligand input preparation
  3. 03Ranked pose output handling
  4. 04Pose confidence and validation checks

Use cases

  1. 01Estimate ligand poses for a protein
  2. 02Run docking through hosted or local NIM
  3. 03Save and inspect ranked pose SDF files

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nvidia-bionemo/bionemo-agent-toolkit --skill diffdock-nim

After installing, type/diffdock-nimin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

BENCHMARK.md
skill-card.md
skill.oms.sig
config/
skillspector-baseline.yml
evals/
config.yml
evals.json
trigger_evals.json
evals/files/
protein.pdb
references/
api.md
examples.md
parameters.md
science.md
validation.md

Details

From the project README

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. 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/.

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