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.

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.