| 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 |
| origin | openai4s |
| category | biomodels |
| requirements | ["gpu"] |
| capabilities | {"network":{"mode":"raw_required","domains":[]}} |
| metadata | {"display-name":"Chai-1","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"},{"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
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 ; treat >
0.5 as a soft pass for an interface. The function refuses a non-empty , so
clear or rotate it between calls.
