Boltz2 Nim logo

Boltz2 Nim

Organization
NVIDIA-BioNeMo
boltz2-nim

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.

Overview

PublisherNVIDIA-BioNeMo
Repositorybionemo-agent-toolkit
Skill nameboltz2-nim
Stars
502
Forks
78
Bundled files
9
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by NVIDIA-BioNeMo on GitHub. Read the source before you install it.

Installation

Install the Boltz2 Nim AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/nim-skills/boltz2-nim
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit.git /tmp/bionemo-agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/bionemo-agent-toolkit/nim-skills/boltz2-nim .claude/skills/boltz2-nim
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Boltz2 Nim in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Boltz2 Nim on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Boltz2 Nim is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

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 --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:

bash
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

python
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": 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.
python
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/.cache mount.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Boltz2 Nim AI skill do?

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.

Why use Boltz2 Nim on TypingMind?

Because you install it once and use it with any model. Boltz2 Nim is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Boltz2 Nim in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/nim-skills/boltz2-nim. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Boltz2 Nim?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Boltz2 Nim?

As many as you like. As long as a model supports skills, you can use Boltz2 Nim with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Boltz2 Nim AI skill free?

It is published on GitHub by NVIDIA-BioNeMo. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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