Predict biomolecular structure and binding affinity with Boltz2 NIM — protein, protein-ligand, DNA and RNA complexes from SMILES or CCD ligands, returning mmCIF and pIC50 scores, via the hosted NVIDIA API or local Docker.

Boltz2 NIM

Predict biomolecular structures and optional ligand affinity. 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: purpose, strengths, limitations, and handoffs.
references/parameters.md: prediction, sampling, MSA, template, affinity tuning.
references/validation.md: mmCIF, confidence, affinity, and chemistry checks.
references/examples.md: compact hosted/local payload 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/boltz2/predict
Local: http://localhost:8000/biology/mit/boltz2/predict

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 before docker login, docker run, readiness, and the no-auth local request. Do not invent a cache default or drop the .env load or NVIDIA_API_KEY fallback.

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 --rm --name boltz2 --gpus all \
  --shm-size=16G \
  -e NGC_API_KEY \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/mit/boltz2:1.6.0

Readiness:

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

First startup downloads about 30 GB of model weights.

Request Pattern

import os
import requests

HOSTED = True
url = (
    "https://health.api.nvidia.com/v1/biology/mit/boltz2/predict"
    if HOSTED else "http://localhost:8000/biology/mit/boltz2/predict"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
    headers["Authorization"] = f"Bearer {os.environ['NGC_API_KEY']}"

payload = {
    "polymers": [{
        "id": "A",
        "molecule_type": "protein",
        "sequence": "MTEYKLVVVGACGVGKSALTIQLIQNHFVDEYDPT",
    }],
    "recycling_steps": 3,
    "sampling_steps": 50,
    "diffusion_samples": 1,
    "step_scale": 1.638,
    "output_format": "mmcif",
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()

Payload essentials:

Protein polymer: {"molecule_type": "protein", "sequence": "..."}.
DNA/RNA polymer: add another polymer with molecule_type "dna" or "rna".
Ligand by SMILES: {"id": "L1", "smiles": "CC(=O)OC1=CC=CC=C1C(=O)O"}.
Ligand by CCD: {"id": "L1", "ccd": "ATP"}.
Affinity: set "predict_affinity": True on exactly one ligand; report affinity_pic50, affinity_pred_value, and affinity_probability_binary.
Precomputed A3M MSA goes under the protein polymer. The A3M record uses alignment, format, and rank; do not use a stale data field.

protein_with_msa = {
    "id": "A",
    "molecule_type": "protein",
    "sequence": "MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT",
    "msa": {"msa_search": {"a3m": {
        "alignment": ">query\nMTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT",
        "format": "a3m",
        "rank": 0,
    }}},
}

Save And Report Output

for i, structure in enumerate(result["structures"], start=1):
    with open(f"structure_{i}.cif", "w", encoding="utf-8") as handle:
        handle.write(structure["structure"])
for i, score in enumerate(result.get("confidence_scores", []), start=1):
    print(f"structure {i} confidence {score:.4f}")
if "affinities" in result:
    for ligand_id, aff in result["affinities"].items():
        print(ligand_id, aff["affinity_pic50"][0], aff["affinity_pred_value"][0], aff["affinity_probability_binary"][0])

Save every .cif artifact. Visualize in PyMOL, ChimeraX, or UCSF Chimera. For confidence/affinity sanity checks, read references/validation.md.

Limits And Troubleshooting