Chai1 Agent Skill

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

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.

Wisp execution

Use python only for bounded interactive checks. For a long or GPU-backed

workload, require a selected and probed ssh:<alias> context and load

remote-compute-ssh. Put the documented invocation in a self-contained project

script, activate the remote environment explicitly, stage only small files with

input_paths, and make the command write to a known absolute remote result

path. Submit it with run_in_context and register that exact ssh:// path in

output_specs. Call monitor_run once when waiting is needed, get_run once

for a snapshot, or cancel_run to stop. Do not send a scheduler submission

through the SSH-direct runner.

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. |

Next: filter survivors on confidence/clash metrics or feed them back to

proteinmpnn for the next design round.