Boltz2 NIM
Predict protein structures and ligand binding affinity using NVIDIA's AI model.
Installation
- Make sure Claude is on your device and in your terminal.
Skills load from
~/.claude/skills/when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then runclaudein any terminal to verify.One-time setupnpm i -g @anthropic-ai/claude-codeAlready have it? Skip ahead.
- Paste into Claude Code or into your terminal.
This copies the whole skill folder into
~/.claude/skills/boltz2-nim-nvidia/— the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.Faster alternative (instruction-only skills)
Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.
Quick install (SKILL.md only)Sign up to copy - Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from
~/.claude/skills/). New skills are picked up on startup. - Just ask Claude.
Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.
Prefer to read the source first? Open on GitHub.
When Claude uses it
Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.
What this skill does
Boltz2 NIM
Predict biomolecular structures and optional ligand affinity. 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: 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.
Instructions
Read credentials from the environment only when needed. Check presence with
bool(os.getenv("NGC_API_KEY")); keep key values and Authorization headers out of
terminal output, logs, saved artifacts, and the final response. Avoid environment
dumps when diagnosing authentication. If the hosted key is absent, report the
missing variable before submitting a request.
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 "${LOCAL_NIM_CACHE}"
chmod 755 "${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.
Examples
Prediction Request
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:
api_key = os.getenv("NGC_API_KEY")
if not api_key:
raise SystemExit("NGC_API_KEY is required for the hosted API")
headers["Authorization"] = f"Bearer {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": Trueon exactly one ligand; reportaffinity_pic50,affinity_pred_value, andaffinity_probability_binary. - Precomputed A3M MSA goes under the protein polymer. The A3M record uses
alignment,format, andrank; do not use a staledatafield.
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
Save every .cif artifact and read the confidence/affinity fields using the
snippet in references/examples.md under Save
Structures And Affinity. Visualize in PyMOL, ChimeraX, or UCSF Chimera. For
confidence/affinity sanity checks, read references/validation.md.
Limits And Troubleshooting
- Polymers/request: 12. Ligands/request: 20. Chain length: 4096 residues.
- Affinity prediction supports one ligand per request and adds runtime.
422: invalid sequence, invalid CCD/SMILES, malformed MSA, or multiple affinity ligands.- Local URL/auth: local path has no hosted auth header; wait on
/v1/health/ready. - Local startup: use
--gpus all,--shm-size=16G, and the/opt/nim/.cachemount.
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