HannesStark/boltzgen

Health v2 · maintenance only

Adoption not part of the score

1042 stars · 253 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

BoltzGen is an open-source all-atom generative diffusion model for designing protein and peptide binders against arbitrary biomolecular targets (proteins, nucleic acids, small molecules). It unifies binder design and structure prediction in a single model, controlled via a YAML design specification language, and is installable via pip or Docker.

Use cases

• design protein binders against a target protein
• generate nanobodies or antibodies de novo
• design peptides that bind nucleic acids or small molecules
• predict structures of designed proteins
• run binder design pipelines on GPU with Docker
• rank candidate protein designs for drug discovery

When to choose

• you need de novo binder design for proteins, nucleic acids, or small-molecule targets
• you want an MIT-licensed, freely usable alternative to closed protein-design models
• you need unified design plus structure prediction in one model
• you want a flexible YAML-driven design specification workflow

When to avoid

• you lack GPU compute or cannot download ~6GB of model weights
• you need small-molecule ADMET or affinity-only prediction rather than binder generation
• you want a hosted API without local infrastructure
• your task is unrelated to biomolecular design or structure prediction

Facets

library · maturity active

machine-learning deep-learning llm-inference sdk cli bioinformatics healthcare artificial-intelligence deep-learning gpu-computing python windows cli protein-design binder-design antibody-design nanobody-design peptide-design drug-discovery generative-diffusion-model structure-prediction computational-biology structural-biology de novo protein binder generation design specification yaml docker gpu linux macos

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