chai1

Chai-1: Biomolecular Structure Prediction

Overview

Chai-1 is a multimodal structure prediction model that handles proteins, small molecules, RNA, DNA, and modifications in a single unified framework. It is a direct competitor to AlphaFold 3 and Boltz-2.

Key differentiators:

• Apache 2.0 license (commercial use explicitly permitted, including drug discovery)
• Simple pip install + FASTA-like input format
• Returns PAE, PDE, pLDDT, pTM, ipTM confidence metrics
• Optional MSA server integration (ColabFold MMseqs2)
• Supports templates, restraints, and covalent bonds

Supported entity types:

• protein — amino acid sequences
• ligand — SMILES-encoded small molecules
• rna — RNA sequences
• dna — DNA sequences
• Modified residues (e.g., phosphoserine: AAA(SEP)AAA)

Installation

pip install chai_lab==0.6.1

Requirements: Python ≥ 3.10, Linux, CUDA GPU with bfloat16 support.

Recommended GPUs: A100 (80GB), H100 (80GB), L40S (48GB). Also works on A10, A30, RTX 4090 for smaller complexes.

Model weights download automatically on first run to ~/.chai/ (or $CHAI_DOWNLOADS_DIR).

Input Format

Chai-1 uses a FASTA-like format with entity type headers:

>protein|name=receptor
AGSHSMRYFSTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAA...

>protein|name=peptide
GAAL

>ligand|name=inhibitor
CC(=O)Nc1ccc(O)cc1

>rna|name=guide_rna
AUGCUAGCUAGC

>dna|name=template
ATGCTAGCTAG

• Each entity needs a unique name= identifier
• All entities in one file form a complex
• Ligands use SMILES notation
• Modified residues use parenthetical notation: AAA(SEP)AAA (phosphoserine at position 4)

Core Usage

Python API

from pathlib import Path
from chai_lab.chai1 import run_inference

candidates = run_inference(
    fasta_file=Path("input.fasta"),
    output_dir=Path("output/"),
    num_trunk_recycles=3,
    num_diffn_timesteps=200,
    num_diffn_samples=5,
    seed=42,
)

# Access results
for i, (cif_path, ranking) in enumerate(zip(candidates.cif_paths, candidates.ranking_data)):
    score = ranking.aggregate_score.item()
    print(f"Sample {i}: {cif_path}  aggregate_score={score:.3f}")

# Confidence tensors
plddt = candidates.plddt   # (num_samples, num_tokens)
pae   = candidates.pae     # (num_samples, num_tokens, num_tokens)
pde   = candidates.pde     # (num_samples, num_tokens, num_tokens)