# Chai1

Structure prediction for protein, nucleic-acid, and small-molecule complexes with the...

- Repository: https://github.com/aipoch/open-science (5414 stars)
- Category: Design
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aipoch/open-science --skill chai1 -g -a claude-code -y`
Codex: `npx skills add aipoch/open-science --skill chai1 -g -a codex -y`
Cursor: `npx skills add aipoch/open-science --skill chai1 -g -a cursor -y`
Gemini CLI: `npx skills add aipoch/open-science --skill chai1 -g -a gemini-cli -y`

## Description

Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab). Reach for this skill to predict an antibody-antigen or protein-ligand complex from a single FASTA, to re-fold designed binders as an AlphaFold-multimer alternative, or to drive co-folding from Python for batched campaigns on a GPU.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Chai-1

Chai-1 is an all-atom diffusion co-folder in the same family as Boltz-2 and
AlphaFold3: a multi-entity FASTA in, mmCIF plus pTM/ipTM/pLDDT out, with
protein, RNA, DNA, and SMILES-ligand chains all first-class. It and `boltz`
cover the same surface; running both and keeping designs that pass either is a
common consensus filter, and Chai's Python entry point makes it the easier of
the two to embed in a loop. Code and weights are Apache-2.0, commercial use
including drug discovery is explicitly permitted
(github.com/chaidiscovery/chai-lab).

## Running it

```python
from pathlib import Path
from chai_lab.chai1 import run_inference

Path("complex.fasta").write_text("""
>protein|name=target
MVTPEGNVSLVDESLLVGVTDEDRAVRS...
>protein|name=binder
AIQRTPKIQVYSRHPAENG...
>ligand|name=cofactor
CCCCCCCCCCCCCC(=O)O
""".strip())

candidates = run_inference(
    fasta_file=Path("complex.fasta"),
    output_dir=Path("out/"),
    num_trunk_recycles=3,
    num_diffn_timesteps=200,
    seed=42,
    device="cuda:0",
    use_esm_embeddings=True,
)
print([rd.aggregate_score.item() for rd in candidates.ranking_data])
```

The FASTA header is `>{entity_type}|name={id}` with `entity_type` ∈
{`protein`, `rna`, `dna`, `ligand`}; ligand records carry a SMILES string as
the sequence body, and modified residues are written inline as
`...AAK(SEP)AAG...`. From the shell the same job is `chai-lab fold
complex.fasta out/ --use-msa-server`. Without `--use-msa-server` (or
`use_msa_server=True` in Python) the model runs on ESM embeddings alone, which
is faster but typically a few ipTM points behind the MSA-backed run.

`output_dir` receives `pred.model_idx_{0..4}.cif` plus a matching
`scores.model_idx_{N}.npz` per sample with `aggregate_score`, `ptm`, `iptm`,
`per_chain_ptm`, and clash flags. Rank by `aggregate_score`; treat `iptm` >
0.5 as a soft pass for an interface. The function refuses a non-empty `output_dir`, so
clear or rotate it between calls.

The rest: https://github.com/aipoch/open-science/blob/HEAD/resources/skills/chai1/SKILL.md