rfdiffusion-nim

RFDiffusion NIM

Design protein backbone PDBs for de novo proteins, motif scaffolds, and binders. Use this SKILL.md for first-pass hosted/local usage; load supplemental files only when needed:

• references/api.md: exact endpoints, schemas, Docker flags, response fields.
• references/science.md: design modes, strengths, limits, and handoffs.
• references/parameters.md: contigs, hotspots, steps, and seeds.
• references/validation.md: PDB, contig, and artifact sanity checks.
• references/examples.md: compact hosted/local request patterns.

Choose Mode

Ask only when context is unclear:

Hosted NVIDIA API or local Docker NIM?

• Hosted: https://health.api.nvidia.com/v1/biology/ipd/rfdiffusion/generate
• Local: http://localhost:8000/biology/ipd/rfdiffusion/generate

Local inference paths do not include /v1/. 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 before docker login, docker run, readiness, and the no-auth local request. Do not replace it with a simple : "${NGC_API_KEY:?Set NGC_API_KEY}" check, do not invent a cache default, and do not drop the NVIDIA_API_KEY fallback. Default setup is single GPU device=0.

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

mkdir -p "${LOCAL_NIM_CACHE}"
chmod 777 "${LOCAL_NIM_CACHE}"

docker run -it \
  --runtime=nvidia \
  --gpus "device=0" \
  -e NGC_API_KEY \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/ipd/rfdiffusion:2

Readiness:

until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done

Contigs DSL

contigs defines what to keep and what to generate.

• "100": generate exactly 100 residues.
• "80-120": generate 80-120 residues.
• "A25-35": keep chain A residues 25-35 from input_pdb.
• "A25-35/0 50-80": keep A25-35, insert chain break /0, generate 50-80.

Design modes:

• De novo: contigs="80-120"; live hosted validation requires a non-empty input_pdb or input_pdb_asset, so inline requests should include the dummy PDB below.
• Motif scaffolding: read target.pdb, pass input_pdb, use a contig like "A25-35/0 50-80".
• Binder design: pass target input_pdb, contig with target and binder segment, and hotspot_res=["A50", "A51", ...] in ChainResidue string format.

DUMMY_PDB = (
    "CRYST1    1.000    1.000    1.000  90.00  90.00  90.00 P 1           1\n"
    "ATOM      1  CA  ALA A   1       0.000   0.000   0.000  1.00  0.00           C\n"
    "END\n"
)

Request Pattern

import os
from pathlib import Path
import requests

HOSTED = True
url = (
    "https://health.api.nvidia.com/v1/biology/ipd/rfdiffusion/generate"
    if HOSTED else "http://localhost:8000/biology/ipd/rfdiffusion/generate"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
    headers["Authorization"] = f"Bearer {os.environ['NGC_API_KEY']}"

payload = {
    "input_pdb": DUMMY_PDB,
    "contigs": "80-120",
    "diffusion_steps": 50,
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()
Path("designed_backbone.pdb").write_text(result["output_pdb"])

Motif scaffold:

payload = {
    "input_pdb": Path("target.pdb").read_text(),
    "contigs": "A25-35/0 50-80",
    "diffusion_steps": 50,
}

Binder design:

payload = {
    "input_pdb": Path("target.pdb").read_text(),
    "contigs": "A1-100/0 50-100",
    "hotspot_res": ["A50", "A51", "A52", "A53", "A54"],
    "diffusion_steps": 50,
}

Save And Interpret Output

Save result["output_pdb"] as a PDB artifact and report elapsed_ms when present. Generated backbones are not final proteins; feed them to ProteinMPNN for sequence design, then validate sequences/structures with Boltz2 or OpenFold3. For PDB and contig checks, read references/validation.md.

Limits And Troubleshooting

• diffusion_steps: 1-50; 50 is maximum quality, fewer is faster.
• Single GPU; minimum GPU VRAM is about 12 GB.
• hotspot_res uses strings like "A50", not tuples.
• 422 usually means chain IDs in contigs/hotspot_res do not match input_pdb, a malformed contig, or omitted input_pdb for hosted de novo.
• Local URL 404 usually means an accidental /v1/ prefix.