rfdiffusion-nim

Files (13)

• BENCHMARK.md
• SKILL.md
• skillspector-baseline.yml · config/
• config.yml · evals/
• evals.json · evals/
• trigger_evals.json · evals/
• api.md · references/
• examples.md · references/
• parameters.md · references/
• science.md · references/
• validation.md · references/
• skill-card.md
• skill.oms.sig

SKILL.md (first lines)

# RFDiffusion NIM

Design protein backbone PDBs for de novo proteins, motif scaffolds, and binders.
Use this guide 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`.

```bash
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 "${LOCAL_NIM_CACHE}"
chmod 755 "${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:

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

## Contigs DSL

`contigs` defines what to keep and what to generate. For the full pattern
syntax (fixed length, ranges, kept chain segments, chain breaks), see
[`references/api.md`](references/api.md) under **Contigs Language Reference**.

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.

```python
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

```python
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.getenv('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:

From NVIDIA/skills, Apache-2.0 AND CC-BY-4.0.

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