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LLMs-Universal-Life-Science-and-Clinical-Skills- BioNeMo_Framework

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name: nvidia-bionemo-framework description: Stand up NVIDIA BioNeMo Framework + NIM microservices to train, fine-tune, and deploy generative biomolecular models for drug discovery. keywords:

  • generative-ai
  • drug-discovery
  • protein-design
  • nvidia
  • bionemo measurable_outcome: Launch BioNeMo Framework from NGC, run one inference (NIM) and one fine-tuning recipe on DGX Cloud or on-prem GPUs within a single working day. license: Apache-2.0 (framework) / NVIDIA AI Enterprise (enterprise support) metadata: author: Computational Pharmacology Guild version: "2026.03" compatibility:
  • system: NVIDIA GPU Compute Capability >= 8.0
  • system: DGX Cloud or on-prem DGX/HGX clusters allowed-tools:
  • docker
  • run_shell_command
  • web_fetch

NVIDIA BioNeMo Framework Skill

Use BioNeMo when you need enterprise-ready generative AI tooling for omics, protein, and small-molecule pipelines without assembling dozens of bespoke repos.

Core Components

  1. BioNeMo Framework – OSS toolkit (Apache 2.0) with training recipes, dataloaders, and model zoo (Evo2, DiffDock, MolMIM, AlphaBind). Downloaded as NGC containers (Refs. 1–3).
  2. BioNeMo NIMs – Optimized inference microservices (Docker) that expose REST/gRPC endpoints to deploy models in under five minutes (Ref. 1).
  3. DGX Cloud / NVIDIA AI Enterprise – Managed supercomputing tier for multi-node training with enterprise support; optional, framework also runs on self-managed clusters (Refs. 1,4).

Setup Checklist

  1. Create/verify NGC account and request access to 
    bionemo-framework
    org.
  2. Pull the framework container: 
    docker login nvcr.io
docker pull nvcr.io/nvidia/clara/bionemo-framework:2.2
  3. Launch training workspace (single node): 
    docker run --gpus all -it --rm \
  -v $PWD:/workspace \
  -e WANDB_API_KEY=$WANDB_API_KEY \
  nvcr.io/nvidia/clara/bionemo-framework:2.2 /bin/bash
  4. Set up DGX Cloud (optional) using 
    dgxcloud cluster create
    for multi-node jobs; attach encrypted NVMe scratch per recipe.
  5. Install CLI helpers if you plan to run OSS repo directly: 
    git clone https://github.com/NVIDIA/bionemo-framework.git
cd bionemo-framework
uv sync
pre-commit install

Standard Workflow

  1. Data staging – Convert PDB/FASTA/SMILES to BioNeMo dataloaders using 
    recipes/*/prepare_data.py
    .
  2. Fine-tune (example: DiffDock): 
    cd recipes/diffdock
bash scripts/train.sh config/train.yaml
  3. Package inference microservice via NIM: 
    docker pull nvcr.io/nim/bionemo/diffdock:latest
docker run --gpus all -p 8000:8000 nvcr.io/nim/bionemo/diffdock:latest
  4. Integrate with BioNeMo Blueprints for turnkey workflows (virtual screening, protein design) when you need reference pipelines instead of bespoke notebooks (Ref. 4).
  5. Monitor + trace using NVIDIA Base Command, Weights & Biases, or your own MLFlow deployment.

Governance & Contribution Notes

  • BioNeMo Framework is OSS (Apache 2.0). Follow CONTRIBUTING guidelines for CI labels (
    ciflow:*
    ) before submitting PRs (Refs. 2,5).
  • Enterprise deployments fall under NVIDIA AI Enterprise license; coordinate with legal before shipping regulated workloads (Refs. 1,4).
  • Hardware baseline: Hopper/Blackwell class GPUs with ≥80 GB HBM for full-recipe throughput; smaller GPUs supported for inference only (Ref. 3).

References

  1. NVIDIA, BioNeMo – Generative AI Platform (product overview, framework vs. NIM breakdown). https://www.nvidia.com/en-us/clara/bionemo/
  2. NVIDIA Docs, BioNeMo Framework FAQ & User Guide. https://docs.nvidia.com/bionemo-framework/2.2
  3. NVIDIA Docs, DiffDock recipe (model details + scripts). https://docs.nvidia.com/bionemo-framework/1.10/models/diffdock.html
  4. NVIDIA, BioNeMo for Biopharma (Blueprint workflows + DGX Cloud guidance). https://www.nvidia.com/en-au/clara/drug-discovery/
  5. NVIDIA Docs, Contributing to BioNeMo Framework. https://docs.nvidia.com/bionemo-framework/2.5/user-guide/contributing