chaidiscovery/chai-lab

Health v2 · maintenance only

Adoption not part of the score

1986 stars · 286 forks observed · 2026-08-28

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

Chai-1 is a state-of-the-art multi-modal foundation model for biomolecular structure prediction, handling proteins, small molecules, DNA, RNA, and glycosylations in unified complexes. It is distributed as a Python package (chai_lab) with both a CLI and Python API for running folding inference on GPU hardware.

Use cases

• predict 3D structure of a protein complex from FASTA sequences
• fold protein-ligand complexes including small molecules and nucleic acids
• run antibody structure prediction for drug discovery
• predict biomolecular interactions with MSAs and templates
• generate PDB structure predictions via Python API

When to choose

• you need SOTA accuracy for multi-component biomolecular complexes
• you have access to a CUDA GPU with bfloat16 support (A100/H100/L40S class)
• you want both CLI and programmatic Python inference
• you work in drug discovery or computational biology

When to avoid

• you only have CPU-only or non-Linux environments
• you need lightweight or fast structure prediction without heavy GPU resources
• you need a web service rather than a local inference package
• your focus is general-purpose protein modeling beyond structure prediction

Facets

library · maturity active

machine-learning deep-learning llm-inference cli bioinformatics machine-learning artificial-intelligence healthcare python cli structure-prediction protein-folding drug-discovery molecular-modeling alphafold-alternative cuda linux gpu

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markdown · JSON · MCP: product_card(name="chaidiscovery/chai-lab")