Predict three-dimensional structures of complexes containing proteins, RNA, DNA, and small molecules with Chai-1. Compare candidate structures using confidence scores and clash indicators, or run repeated predictions from Python to evaluate designed binders.
中文简介正在准备中。当前显示英语简介。
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.
安装前可以查看交给 AI 代理的指令内容。
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
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.
CHAI_DOWNLOADS_DIR fails mid-run with PermissionError on a read-only imageChai 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.
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.
| 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.
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Predict structures for single proteins or protein complexes with AlphaFold2 through ColabFold. Use confidence scores and comparisons with a designed structure to assess whether a designed sequence folds as intended.
中文简介正在准备中。当前显示英语简介。
Use Boltz-2 to predict structures of complexes containing proteins, DNA, RNA, and small molecules. Obtain structure files and confidence scores to assess designed binders, with optional predictions of binding strength between a protein and a small molecule.
中文简介正在准备中。当前显示英语简介。
Predict patterns of gene expression and other genomic measurements from DNA sequences with Borzoi. Compare reference and altered sequences to estimate regulatory effects, generate predicted coverage tracks, and prioritize variants in regions that do not encode proteins.
中文简介正在准备中。当前显示英语简介。
Prepare reproducible setup and removal instructions for software environments on Open-Science SSH hosts, including Slurm clusters. Check available tools, define named activation settings, and validate the environment where jobs run after the user installs it.
中文简介正在准备中。当前显示英语简介。
Create and manage focused Specialist agents and Skills through conversation. Draft agent roles, review available Skills and Connectors, configure access, and manage changes, with Skill creation and publishing handled by the application's internal skill-creator.
中文简介正在准备中。当前显示英语简介。
Predict how a small molecule may sit within a protein using structural inputs. Generate ranked 3D poses without defining a search region, then use those poses as starting points for further evaluation.
中文简介正在准备中。当前显示英语简介。