跳到正文
cccskills
免费在 GitHub 公开

boltz2-nim

Predict biomolecular structures and estimate how strongly a ligand binds using Boltz2 NIM. Submit proteins, DNA, RNA, and ligand inputs through NVIDIA's hosted API or local Docker, then save structures and inspect confidence and affinity scores.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

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.

集成工具Docker
查看安装方法

适用场景

  • Predicting protein structures and DNA or RNA complexes
  • Providing ligand inputs with SMILES or CCD identifiers
  • Estimating binding affinity for one ligand
  • Running predictions through a hosted API or local Docker

包含的文件(13)

  • SKILL.md5.4 KB
  • BENCHMARK.md6.8 KB
  • config/skillspector-baseline.yml865 B
  • evals/config.yml117 B
  • evals/evals.json6.7 KB
  • evals/trigger_evals.json2.3 KB
  • references/api.md5.7 KB
  • references/examples.md2.0 KB
  • references/parameters.md1.7 KB
  • references/science.md1.7 KB
  • references/validation.md1.3 KB
  • skill-card.md4.1 KB
  • skill.oms.sig6.3 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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": 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

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.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Move pandas-style table processing to NVIDIA GPUs using cuDF. Choose minimal-change acceleration or explicit GPU code, handle joins and aggregations, compare results with pandas, and use dask-cuDF when data exceeds GPU memory.

中文简介正在准备中。当前显示英语简介。

  • Accelerate existing pandas processing with minimal changes
  • Move joins and aggregations to GPU code
  • Compare missing values and ordering with pandas
NVIDIA/skills36832026年10月21日 更新

Customize NVIDIA Nemotron Voice Agent’s Generic Pipecat example for appointment scheduling, five-field patient intake, or a custom healthcare conversation. Build prompts and tool handlers without a separate backend, then validate conversation behavior, services, and voice interaction.

中文简介正在准备中。当前显示英语简介。

  • Build an appointment-scheduling voice agent
  • Collect five patient-intake fields through conversation
  • Design a custom healthcare conversation with tool calls
NVIDIA/skills36832026年10月21日 更新

Calibrate live cameras by recording their RTSP video streams through VIOS and processing the clips with AutoMagicCalib. The workflow imports settings and alignment files, checks readiness, tracks calibration progress, and retrieves results.

中文简介正在准备中。当前显示英语简介。

  • Calibrate cameras from live RTSP streams
  • Import settings and alignment files for a camera scene
  • Retrieve evaluation statistics using ground truth data
NVIDIA/skills36832026年10月21日 更新

Test a running AMC service with the bundled four-camera warehouse dataset. Run camera calibration from upload to completion, then check distance and image projection errors against ground-truth data to verify the installation.

中文简介正在准备中。当前显示英语简介。

  • Verify a newly installed AMC service
  • Check the sample's calibration errors against ground truth
  • Walk through the sample workflow in Swagger UI
NVIDIA/skills36832026年10月21日 更新

Calibrate multiple cameras from recorded MP4 files through the AutoMagicCalib REST API. The skill handles project creation, uploads, input verification, progress checks, and evaluation when reference data is supplied, with optional VGGT refinement.

中文简介正在准备中。当前显示英语简介。

  • Calibrating cameras from synchronized MP4 recordings
  • Uploading local settings and alignment files
  • Evaluating calibration against ground-truth data
NVIDIA/skills36832026年10月21日 更新

Set up AutoMagicCalib from NGC release containers using Docker Compose. The skill handles registry authentication, port and path configuration, optional VGGT model download, service startup, and readiness checks for the backend and web interface.

中文简介正在准备中。当前显示英语简介。

  • Launching the AutoMagicCalib backend and web interface
  • Configuring service ports and network access
  • Adding the optional VGGT model during setup
NVIDIA/skills36832026年10月21日 更新

查看 NVIDIA 的全部技能

报告此技能的问题