chai

name	chai
description	Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.
license	MIT
category	design-tools
tags	["structure-prediction","validation","foundation-model"]
source	https://github.com/adaptyvbio/protein-design-skills

Chai-1 Structure Prediction

Prerequisites

Requirement	Minimum	Recommended
Python	3.10+	3.11
CUDA	12.0+	12.1+
GPU VRAM	24GB	40GB (A100)
RAM	32GB	64GB

How to run

Option 1: Modal

cd biomodals
modal run modal_chai1.py \
  --input-faa complex.fasta \
  --out-dir predictions/

Option 2: Chai API (recommended)

pip install chai_lab
python -c "
from chai_lab.chai1 import run_inference
run_inference(fasta_file='complex.fasta', output_dir='predictions/', num_trunk_recycles=3)
"

FASTA Format

>binder
MKTAYIAKQRQISFVKSHFSRQLE...
>target
MVLSPADKTNVKAAWGKVGAHAGE...

Protein + ligand

>protein
MKTAYIAKQRQISFVKSHFSRQLE...
>ligand|smiles
CCO

Key parameters

Parameter	Default	Description
num_trunk_recycles	3	Recycles (more = better)
num_diffn_timesteps	200	Diffusion steps

Output format

predictions/
├── pred.model_idx_0.cif    # Best model
├── scores.json             # pTM, ipTM, ranking_score
├── pae.npy                 # PAE matrix
└── plddt.npy               # pLDDT values

Chai vs AF2

Aspect	Chai-1	AlphaFold2
MSA required	No	Yes
Small molecules	Yes	No
Speed	Faster	Slower
Accuracy	Comparable	Reference

Typical performance

Campaign	Time (A100)	Cost (Modal)
100 complexes	30-60 min	~$10
500 complexes	2-4h	~$45

Troubleshooting

Error	Cause	Fix
CUDA out of memory	Complex too large	Use A100-80GB
KeyError: 'iptm'	Single chain	Ensure FASTA has 2+ chains
ValueError: invalid SMILES	Malformed ligand	Validate SMILES with RDKit

Next: protein-qc for filtering and ranking.