4 added, 4 removed. Audit A to A.
---
name: chai1
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.
license: Apache-2.0
category: biomodels
requirements: [gpu]
metadata:
display-name: Chai-1
# github.com/chaidiscovery/chai-lab/blob/main/LICENSE: Apache-2.0
# (relicensed at v0.4.0). verified 2026-06-30
third_party:
- kind: weights
name: Chai-1
provider: Chai Discovery
license: Apache-2.0
terms_url: https://github.com/chaidiscovery/chai-lab/blob/main/LICENSE
# SKILL.md — `--use-msa-server` / `use_msa_server=True` sends the sequence
# to the public ColabFold MMseqs2 server. No published ToS — wiki is the
# closest data-use reference.
- kind: service
name: ColabFold MSA server (api.colabfold.com)
provider: Steinegger Lab
info_url: https://github.com/sokrypton/ColabFold/wiki
---
# 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.
## Unset `CHAI_DOWNLOADS_DIR` fails mid-run with PermissionError on a read-only image
Chai downloads ~5 GB on the first inference call (not at install time),
including its own traced ESM2-3B for the embedding path. If
`CHAI_DOWNLOADS_DIR` is unset, the default is inside `site-packages`: on a
read-only image that fails with a confusing `PermissionError` mid-run, and on
a writable one it silently re-downloads ~5 GB into the container on every cold
start. Export the variable to a persisted volume so the download happens once.
## No-MSA mode still loads a 3 B-parameter ESM — same VRAM, not less
`use_esm_embeddings=True` without an MSA still loads a 3-billion-parameter
language model into GPU memory alongside the trunk; it removes the MSA-server
round-trip, not the VRAM cost. If you OOM, drop `num_diffn_timesteps` or fold
fewer chains per call rather than expecting the no-MSA mode to fit a smaller
card.
## Errors worth recognizing
- | You see | It means / do this |
- |---|---|
- | `PermissionError` under `site-packages/chai_lab/...` | `CHAI_DOWNLOADS_DIR` not set on a read-only image — export it to a writable path or the pre-populated mount. |
- | `RuntimeError: CUDA out of memory` during ESM embedding | The traced ESM2-3B is loading alongside the trunk — use an 80 GB tier or split chains across calls. |
+ | You see | It means / do this |
+ | ------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ |
+ | `PermissionError` under `site-packages/chai_lab/...` | `CHAI_DOWNLOADS_DIR` not set on a read-only image — export it to a writable path or the pre-populated mount. |
+ | `RuntimeError: CUDA out of memory` during ESM embedding | The traced ESM2-3B is loading alongside the trunk — use an 80 GB tier or split chains across calls. |
---
**Next:** filter survivors on confidence/clash metrics or feed them back to
`proteinmpnn` for the next design round.