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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.
Passed 20 of 23 test cases with the skill, and 6 of 23 without it.
and 18 more in the full report
15 fixed5 passed either way2 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am writing a script to call `run_inference` in Chai-1. I want to specify the standard diffusion refinement depth and trunk iterations matching default benchmarks. Which keyword arguments set the trunk iteration count and diffusion steps?
| Checked | Without skill | With skill |
|---|---|---|
| Sets trunk recycling iterations using num_trunk_recycles | Passed | Passed |
| Configures diffusion refinement timesteps using num_diffn_timesteps | Missed | Passed |
The instructions your model reads when the skill loads.
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).
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
test cases passed, vs 6 / 23 without
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