NVIDIA BioNeMo
Official@nvidia-bionemo
Accelerated biomolecular structure prediction, small-molecule generation, and genomics analysis using GPU-optimized microservices for drug discovery and protein engineering.
Agent Skills by NVIDIA BioNeMo
Showing 33 vetted skills indexed across 1 GitHub repositories.
NVIDIA-BioNeMokermt-embed
Extract per-molecule embeddings from KERMT checkpoints for SMILES processing.
NVIDIA-BioNeMoopenfold2-nim
Predict monomer protein structures from amino-acid sequences via OpenFold2 NIM microservices.
NVIDIA-BioNeMocomplexa-evaluate-pdbs
Evaluate directories of PDB files by orchestrating refolding and structural analysis workflows.
NVIDIA-BioNeMoopenfold3-nim
Predict 3D biomolecular structures for proteins, DNA, RNA, and ligands via OpenFold3 NIM.
NVIDIA-BioNeMoboltz2-nim
Predict 3D biomolecular structures and ligand binding affinities for proteins, DNA, and RNA using NVIDIA NIM microservices.
NVIDIA-BioNeMomsa-structure-prediction-pipeline
Chain MSA-Search and OpenFold3 NIM microservices to predict protein structures.
NVIDIA-BioNeModiffdock-nim
Predict small-molecule binding poses against protein targets using DiffDock via NVIDIA NIM.
NVIDIA-BioNeMoparabricks
Route NVIDIA Parabricks pbrun tools with version-aware command guidance for genomics workflows.
NVIDIA-BioNeMocuequivariance
Construct group-equivariant neural network layers using irreducible representations and segmented tensor products.
NVIDIA-BioNeModrug-discovery-pipeline
Generate, dock, and score drug candidates via GenMol, DiffDock, and Boltz2 NIMs.
NVIDIA-BioNeMogenomics-workflow-acceleration
Map CPU-bound genomics workflow steps to NVIDIA Parabricks GPU equivalents.
NVIDIA-BioNeMogenmol-nim
Generate novel drug-like molecules via SAFE notation using GenMol NIM.
NVIDIA-BioNeMonvmolkit-usage
Run batched RDKit fingerprinting, similarity scoring, and conformer optimization on NVIDIA GPUs.
NVIDIA-BioNeMocomplexa-setup
Configure the Proteina-Complexa runtime environment with .env files, Python virtual environments, and model checkpoints.
NVIDIA-BioNeMokermt-setup
Verify Docker, NVIDIA Container Toolkit, and CUDA hardware, then build the kermt:latest image.
NVIDIA-BioNeMomolmim-nim
Generate and optimize small-molecule structures via NVIDIA BioNeMo NIM microservices.
NVIDIA-BioNeMokermt-add-cmim-pretrain
Convert grover_base checkpoints into hybrid cMIM-enabled models for continued pretraining.
NVIDIA-BioNeMorfdiffusion-nim
Generate protein backbone structures with RFDiffusion NIM via hosted or local Docker inference.
NVIDIA-BioNeMokermt-infer
Execute KERMT model inference workflows on SMILES chemical datasets.
NVIDIA-BioNeMocomplexa-sweep
Execute cartesian-product parameter sweeps over Proteina-Complexa design pipelines with Hydra-based configuration management.
NVIDIA-BioNeMoevo2-nim
Generate DNA sequences and extract model representations via Evo 2 NIM.
NVIDIA-BioNeMocomplexa-design
Orchestrate end-to-end protein and ligand design pipelines with AF2, RF3, and MPNN backends.
NVIDIA-BioNeMokermt-finetune
Finetune pretrained KERMT encoder models on labeled CSV datasets.
NVIDIA-BioNeMokermt-monitor
Monitors detached KERMIT scientific workflows by parsing logs and querying Docker container statuses.
Frequently Asked Questions About NVIDIA BioNeMo
FAQPage SchemaWhat specific scientific tasks are enabled by these capabilities?▼
These capabilities enable high-throughput protein structure prediction, de novo binder design, small-molecule generation, and genomics sequence analysis. Users can perform molecular docking, evaluate binding affinities, and execute large-scale parameter sweeps for protein design pipelines using specialized GPU-accelerated microservices.
Which technical personas are the primary users of these resources?▼
The primary users are computational biologists, bioinformatics engineers, and drug discovery researchers. These professionals utilize the provided microservices and configuration frameworks to integrate structural biology models into their research pipelines for molecular design and genomic data processing.
What are the hardware and software prerequisites for deployment?▼
Deployment requires a Linux environment with Docker, the NVIDIA Container Toolkit, and CUDA-capable hardware. Users must configure runtime environments using specific YAML or .env files and ensure access to the required model checkpoints and container images for the specific microservice being executed.