openfold3-nim

OpenFold3 NIM

Predict biomolecular structures with OpenFold3. It supports proteins, DNA, RNA, small-molecule ligands, and multi-entity assemblies. Use this guide for basic hosted and local NIM use; load supplemental files only when the task needs deeper context:

• references/api.md: exact endpoints, schemas, Docker flags, response fields.
• references/science.md: purpose, strengths, limitations, and model handoffs.
• references/parameters.md: molecule fields, MSAs, templates, samples, tuning.
• references/validation.md: artifact checks and scientific sanity checks.
• references/examples.md: compact hosted and local request patterns.

Choose Mode

Ask only when context is unclear:

Hosted NVIDIA API or local Docker NIM?

• Hosted URL: https://health.api.nvidia.com/v1/biology/openfold/openfold3/predict
• Local URL: http://localhost:8000/biology/openfold/openfold3/predict
• Local readiness: http://localhost:8000/v1/health/ready

Mode difference: the local prediction path has no /v1/ prefix. 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, so bind the host port to loopback with -p 127.0.0.1:8000:8000. Warm-cache key-free startup varies by image version and should not be assumed.

Auth And Environment

Use credentials already supplied in the environment or injected by a secret manager. Do not load credential files, print keys, or enable shell tracing. Confirm keys exist with shell tests.

Hosted needs NGC_API_KEY in the request header. Local startup needs NGC_API_KEY, or NVIDIA_API_KEY as a fallback, plus LOCAL_NIM_CACHE.

Local Docker

Use the official OpenFold3 NIM image and mount LOCAL_NIM_CACHE at /opt/nim/.cache. Before executing setup, explain that registry authentication sends the key to the NVIDIA registry at https://nvcr.io and first startup downloads about 10–15 GB of model weights into the cache. Run deployment only when requested; for a setup guide, provide the commands without running them.

When writing local setup commands, copy the preflight below exactly. Do not replace it with a simple : "${NGC_API_KEY:?Set NGC_API_KEY}" check, do not drop NVIDIA_API_KEY, and do not invent a default LOCAL_NIM_CACHE; those lines are the repo's local NIM env contract. The default single-GPU launch should show the literal --gpus "device=0"; choose a different device only when the user asks.

set +x

if [ -z "${NGC_API_KEY:-}" ] && [ -n "${NVIDIA_API_KEY:-}" ]; then
  NGC_API_KEY="$NVIDIA_API_KEY"
fi
: "${NGC_API_KEY:?Set NGC_API_KEY or NVIDIA_API_KEY}"
export NGC_API_KEY
: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"

mkdir -p "${LOCAL_NIM_CACHE}"
chmod 755 "${LOCAL_NIM_CACHE}"

printf '%s\n' "$NGC_API_KEY" | \
  docker login nvcr.io --username '$oauthtoken' --password-stdin && \
docker run --rm --name openfold3 \
  --runtime=nvidia \
  --gpus "device=0" \
  --shm-size=16g \
  -e NGC_API_KEY \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 127.0.0.1:8000:8000 \
  nvcr.io/nim/openfold/openfold3:latest

Readiness check:

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

Request Pattern

Use requests.post(..., json=payload, timeout=300). For local Docker tasks, set hosted = False after the readiness check passes.

import os
import requests

hosted = True
url = (
    "https://health.api.nvidia.com/v1/biology/openfold/openfold3/predict"
    if hosted
    else "http://localhost:8000/biology/openfold/openfold3/predict"
)
headers = {"Content-Type": "application/json"}
if hosted:
    headers["Authorization"] = f"Bearer {os.getenv('NGC_API_KEY')}"

seq = "MKTVRQERLKSIVR"
payload = {
    "inputs": [{
        "input_id": "prediction_1",
        "output_format": "pdb",
        "molecules": [{
            "type": "protein",
            "id": "A",
            "sequence": seq,
            "diffusion_samples": 1,
            "msa": {
                "main": {
                    "a3m": {
                        "alignment": f">query\n{seq}",
                        "format": "a3m"
                    }
                }
            }
        }]
    }]
}

response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()

Payload gotchas:

• Top level is {"inputs": [...]} and OpenFold3 accepts exactly one input.
• molecules can contain 1-32 objects with type: protein, dna, rna,

or ligand.

• Protein/RNA MSAs are optional but, when supplied, alignment must start with

a FASTA header such as >query\nSEQUENCE.

• Ligands use either smiles or ccd_codes, for example

{"type": "ligand", "id": "L", "ccd_codes": "ATP"}.

• DNA/RNA entities use sequence, for example

{"type": "dna", "id": "B", "sequence": "ATCGATCG"}.

• diffusion_samples is 1-5. output_format is pdb or cif.

Save And Interpret Output

Save every returned structure as a scientific artifact. Main response path: result["outputs"][0]["structures_with_scores"].

output = result["outputs"][0]
for i, sample in enumerate(output["structures_with_scores"], start=1):
    fmt = sample["format"]
    with open(f"openfold3_structure_{i}.{fmt}", "w", encoding="utf-8") as fh:
        fh.write(sample["structure"])
    print("confidence_score", sample.get("confidence_score"))
    print("complex_plddt_score", sample.get("complex_plddt_score"))
    print("ptm_score", sample.get("ptm_score"))
    print("iptm_score", sample.get("iptm_score"))
    print("complex_pde_score", sample.get("complex_pde_score"))

Higher confidence_score, complex_plddt_score, ptm_score, and iptm_score are generally better; lower complex_pde_score is generally better. Treat toy or very short sequences as API smoke tests, not meaningful structural biology. For why and when OpenFold3 is scientifically appropriate, read references/science.md.

Common Limits

• Inputs per request: 1.
• Molecules per input: 1-32.
• Diffusion samples: 1-5.
• TensorRT path supports shorter sequences; PyTorch path can support longer

sequences, but long inputs need much more GPU memory.

• Sequences over roughly 1800 residues require at least 80 GB GPU memory.
• Local NIM is single-GPU only; choose the target device in the Docker flag.

Troubleshooting

• 401: missing, expired, or unauthorized NGC API key.
• 422: invalid molecule type, invalid sequence characters, bad MSA shape, or

diffusion_samples outside 1-5.

• MSA errors: ensure the alignment starts with >query\n.
• Local 404: remove /v1/ from the prediction URL.
• Local startup stalls: first run may be downloading 10-15 GB of model weights

into LOCAL_NIM_CACHE.

• Memory errors: shorten the sequence, reduce samples, or use a larger GPU.