Bionemo

BioNeMo

Overview

NVIDIA's software ecosystem for building, training, fine-tuning, and deploying AI models for life sciences. BioNeMo Framework provides optimized biomolecular foundation models (ESM-2, AlphaFold2, ProteinMPNN, RFdiffusion) with distributed training support, while BioNeMo NIMs offer production-ready inference microservices with REST API endpoints for scalable deployment.

Quick Pattern

Incorrect — manual protein embedding without framework:

# Raw transformer, no optimization, no pretrained weights
model = TransformerEncoder(...)
embeddings = model(tokenize(sequence))

Correct — BioNeMo ESM-2 fine-tuning with pretrained checkpoint:

from bionemo.esm2.model.finetune.finetune_regressor import ESM2FineTuneSeqConfig
from bionemo.core.data.load import load

pretrain_ckpt = load("esm2/650m:2.0")
config = ESM2FineTuneSeqConfig(initial_ckpt_path=str(pretrain_ckpt))

checkpoint, metrics, trainer = train_model(
    experiment_name="finetune_regressor",
    experiment_dir=Path(results_dir),
    config=config,
    data_module=data_module,
    n_steps_train=50,
)

Quick Command

# Pull BioNeMo container
docker pull nvcr.io/nvidia/bionemo/bionemo-framework:latest

# Run container with GPU
docker run --gpus all -it --rm \
  -v ${PWD}:/workspace \
  nvcr.io/nvidia/bionemo/bionemo-framework:latest bash

# Download ESM-2 checkpoint
python -c "from bionemo.core.data.load import load; load('esm2/650m:2.0')"

# Run ESM-2 inference
infer_esm2 --checkpoint-path /path/to/ckpt \
  --data-path data.csv \
  --results-path results/

# NIM health check
curl http://localhost:8000/v1/health/ready

# RFdiffusion via NIM
curl -X POST http://localhost:8000/biology/ipd/rfdiffusion/generate \
  -H "Content-Type: application/json" \
  -d '{"contigs": "A10-100/0 50-150"}'

Quick Reference

Supported Models

| Model | Type | Task | |-------|------|------| | ESM-2 | Protein language model | Sequence embedding, property prediction | | AlphaFold2 | Structure prediction | 3D protein folding | | AlphaFold2-Multimer | Complex prediction | Multi-chain structure | | ProteinMPNN | Inverse folding | Sequence design from structure | | RFdiffusion | Generative diffusion | De novo protein structure generation | | Evo2 | Genomic foundation model | DNA/RNA generation, variant prediction, 1B/7B/40B | | Geneformer | Single-cell transformer | Cell embeddings, type classification, GRN, 10M/106M | | AMPLIFY | Protein language model | ESM-2 variant, 120M/350M, modified layers | | DiffDock | Docking model | Protein-ligand binding | | MolMIM | Molecular model | Small molecule generation |

NIM Microservice Endpoints

| Service | Endpoint | Method | |---------|----------|--------| | AlphaFold2 | /protein-structure/alphafold2/predict-structure-from-sequence | POST | | AlphaFold2-Multimer | /protein-structure/alphafold2/multimer/predict-structure-from-sequences | POST | | RFdiffusion | /biology/ipd/rfdiffusion/generate | POST | | ProteinMPNN | /biology/ipd/proteinmpnn/predict | POST | | Health check | /v1/health/ready | GET |

Protein Binder Design Pipeline

Target PDB → AlphaFold2 (structure) → RFdiffusion (binder scaffold)
  → ProteinMPNN (sequence design) → AlphaFold2-Multimer (validation)

Key Packages

| Package | Purpose | |---------|---------| | bionemo-esm2 | ESM-2 model, training, fine-tuning, inference | | bionemo-core | Shared utilities, data loading, checkpoint management | | bionemo-geneformer | Single-cell transcriptomics | | bionemo-amplify | AMPLIFY protein language model (ESM-2 variant) | | bionemo-evo2 | Evo2 genomic foundation model | | bionemo-moco | Continuous/discrete generative modeling | | bionemo-llm | Shared LLM utilities, BioBERT, training | | bionemo-scdl | Single Cell Data Loader format |

When to Apply

Reference these guidelines when:

• Working with protein language models (ESM-2 embeddings, fine-tuning)
• Predicting protein 3D structures (AlphaFold2)
• Designing novel proteins or binders (RFdiffusion, ProteinMPNN)
• Building drug discovery pipelines
• Deploying biology AI models as microservices (NIM)
• Processing biological sequence data
• Analyzing genomic sequences (DNA/RNA variant effects, generation) with Evo2
• Processing single-cell RNA-seq data (cell type classification, GRN) with Geneformer
• Setting up distributed training infrastructure (NeMo2, Megatron, TransformerEngine)
• Running multi-model biology workflows

Priority-Ordered Guidelines

| Priority | Category | Impact | Prefix | |----------|----------|--------|--------| | 1 | ESM-2 Protein Language | CRITICAL | esm2-* | | 2 | Structure Prediction | CRITICAL | structure-* | | 3 | Protein Design | HIGH | protein-* | | 4 | NIM Microservices | HIGH | nim-* | | 5 | Evo2 Genomic Model | HIGH | evo2-* | | 6 | Geneformer Single-Cell | HIGH | geneformer-* | | 7 | Training Infrastructure | HIGH | training-* | | 8 | Drug Discovery | MEDIUM | drug-* | | 9 | Data Preparation | MEDIUM | data-* |

References

Full documentation with code examples in [references/](references/):

| File | Impact | Description | |------|--------|-------------| | [esm2-protein-language.md][esm2-protein-language] | CRITICAL | ESM-2 training, fine-tuning, inference, embeddings | | [structure-prediction.md][structure-prediction] | CRITICAL | AlphaFold2 folding, MSA, multimer prediction | | [protein-design.md][protein-design] | HIGH | RFdiffusion generation, ProteinMPNN inverse folding | | [nim-microservices.md][nim-microservices] | HIGH | NIM API endpoints, deployment, health checks | | [drug-discovery.md][drug-discovery] | MEDIUM | Multi-model pipelines, binder design blueprint | | [evo2-genomic-model.md][evo2-genomic-model] | HIGH | Evo2 DNA/RNA model, variant prediction, generation | | [geneformer-single-cell.md][geneformer-single-cell] | HIGH | Geneformer single-cell embeddings, cell type classification | | [training-infrastructure.md][training-infrastructure] | HIGH | Hardware, containers, distributed training, recipes | | [data-preparation.md][data-preparation] | MEDIUM | Biological data preprocessing, tokenization, datasets |

Problem -> Skill Mapping

| Problem | Start With | |---------|------------| | Get protein embeddings | [esm2-protein-language.md][esm2-protein-language] | | Predict protein structure | [structure-prediction.md][structure-prediction] | | Design new proteins | [protein-design.md][protein-design] | | Deploy model as API | [nim-microservices.md][nim-microservices] | | Build drug discovery pipeline | [drug-discovery.md][drug-discovery] | | Prepare biological data | [data-preparation.md][data-preparation] | | Fine-tune ESM-2 | [esm2-protein-language.md][esm2-protein-language] | | Validate protein binder | [drug-discovery.md][drug-discovery] | | Predict variant effects on DNA/RNA | [evo2-genomic-model.md][evo2-genomic-model] | | Generate DNA/RNA sequences | [evo2-genomic-model.md][evo2-genomic-model] | | Classify cell types from scRNA-seq | [geneformer-single-cell.md][geneformer-single-cell] | | Build gene regulatory networks | [geneformer-single-cell.md][geneformer-single-cell] | | Set up distributed training | [training-infrastructure.md][training-infrastructure] | | Pre-train ESM-2 from scratch | [esm2-protein-language.md][esm2-protein-language] -> [training-infrastructure.md][training-infrastructure] | | Convert h5ad to SCDL | [data-preparation.md][data-preparation] | | Run AlphaFold2 inference | [structure-prediction.md][structure-prediction] -> [nim-microservices.md][nim-microservices] |

[esm2-protein-language]: references/esm2-protein-language.md [structure-prediction]: references/structure-prediction.md [protein-design]: references/protein-design.md [nim-microservices]: references/nim-microservices.md [drug-discovery]: references/drug-discovery.md [evo2-genomic-model]: references/evo2-genomic-model.md [geneformer-single-cell]: references/geneformer-single-cell.md [training-infrastructure]: references/training-infrastructure.md [data-preparation]: references/data-preparation.md

Attribution

Based on NVIDIA BioNeMo Framework documentation, bionemo-framework repository, and generative-protein-binder-design blueprint.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: dtunai
• Source: dtunai/agent-skills-for-compute
• License: MIT

Install and usage instructions live in the source repository linked above.