chai1

# 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. | --- **Next:** filter survivors on confidence/clash metrics or feed them back to `proteinmpnn` for the next design round.