Output Format

Output Directory Structure

AlphaFold 3 creates an organized directory structure for each job. The directory name is the sanitized version of your job name.

For job name “My first fold (TEST)”, outputs are written to My_first_fold_TEST. If the directory exists, a timestamp is appended unless --force_output_dir is used.

Example Directory Layout

For a job named “Hello Fold” with 1 seed and 5 samples:

hello_fold/
├── seed-1234_distogram/                        # If --save_distogram=true
│   └── hello_fold_seed-1234_distogram.npz
├── seed-1234_embeddings/                       # If --save_embeddings=true
│   └── hello_fold_seed-1234_embeddings.npz
├── seed-1234_sample-0/
│   ├── hello_fold_seed-1234_sample-0_confidences.json
│   ├── hello_fold_seed-1234_sample-0_model.cif
│   └── hello_fold_seed-1234_sample-0_summary_confidences.json
├── seed-1234_sample-1/
│   ├── hello_fold_seed-1234_sample-1_confidences.json
│   ├── hello_fold_seed-1234_sample-1_model.cif
│   └── hello_fold_seed-1234_sample-1_summary_confidences.json
├── seed-1234_sample-2/
│   ├── hello_fold_seed-1234_sample-2_confidences.json
│   ├── hello_fold_seed-1234_sample-2_model.cif
│   └── hello_fold_seed-1234_sample-2_summary_confidences.json
├── seed-1234_sample-3/
│   ├── hello_fold_seed-1234_sample-3_confidences.json
│   ├── hello_fold_seed-1234_sample-3_model.cif
│   └── hello_fold_seed-1234_sample-3_summary_confidences.json
├── seed-1234_sample-4/
│   ├── hello_fold_seed-1234_sample-4_confidences.json
│   ├── hello_fold_seed-1234_sample-4_model.cif
│   └── hello_fold_seed-1234_sample-4_summary_confidences.json
├── TERMS_OF_USE.md
├── hello_fold_confidences.json
├── hello_fold_data.json
├── hello_fold_model.cif
├── hello_fold_ranking_scores.csv
└── hello_fold_summary_confidences.json

Output Files

Top-Level Files

model.cif

Top-ranked prediction structuremmCIF format compatible with structural biology tools. No PDB format provided (convert CIF if needed).

confidences.json

Detailed confidence metricsFull 1D/2D arrays of pLDDT, PAE, and contact probabilities for top prediction.

summary_confidences.json

Summary confidence scoresScalar metrics like pTM, ipTM, ranking scores for top prediction.

data.json

Input with MSA/templatesOriginal input JSON augmented with MSA and template data from pipeline.

ranking_scores.csv

file

CSV file ranking all predictions. Highest ranking prediction is included in root directory.

TERMS_OF_USE.md

file

License and usage terms for AlphaFold 3 outputs

Per-Sample Subdirectories

For each seed and sample combination (seed-<seed>_sample-<n>), three files are generated:

Structure File

<job>_seed-<seed>_sample-<n>_model.cif - Predicted structure in mmCIF format

Detailed Confidences

<job>_seed-<seed>_sample-<n>_confidences.json - Full confidence arrays

Summary Confidences

<job>_seed-<seed>_sample-<n>_summary_confidences.json - Scalar metrics

Optional Output Files

Distogram
Embeddings

Enabled with: --save_distogram=trueLocation: seed-<seed>_distogram/distogram.npzNumPy zip file containing distance predictions:

Key: distogram
Shape: (num_tokens, num_tokens, 64)
Dtype: np.float16
Size: ~3 GiB for 5,000 tokens

Enabled with: --save_embeddings=trueLocation: seed-<seed>_embeddings/embeddings.npzNumPy zip file with two keys:

single_embeddings: (num_tokens, 384), np.float16
pair_embeddings: (num_tokens, num_tokens, 128), np.float16
Size: ~6 GiB for 5,000 tokens

import numpy as np

with open('embeddings.npz', 'rb') as f:
    embeddings = np.load(f)
    single = embeddings['single_embeddings']
    pair = embeddings['pair_embeddings']

Multi-Seed and Multi-Sample Results

By default, AlphaFold 3 generates 5 samples per seed. The top-ranked prediction across all samples and seeds is placed in the root directory.

Ranking Predictions

For ranking the full complex, use the ranking_score (higher is better):

ranking_score = 0.8 × ipTM + 0.2 × pTM + 0.5 × disorder - 100 × has_clash

This score includes:

Structure confidence (pTM and ipTM)
Disorder penalty for spurious helices
Clash penalty for atomic conflicts

The ranking_score is designed for ranking only. Use pTM, ipTM, PAE, or pLDDT for quality assessment.

Chain-Specific Ranking

If interested in specific entities or interactions, rank by:

chain_ptm: Confidence in individual chain structure
chain_iptm: Confidence in chain interfaces with all other chains
chain_pair_iptm: Confidence in specific two-chain interfaces
chain_pair_pae_min: Minimum PAE between chain pairs (correlates with binding)

Confidence JSON Files

Two JSON files provide confidence metrics for each prediction:

Summary Confidences JSON

Scalar and per-chain/per-chain-pair metrics:

ptm

number

Predicted TM-score for full structure (0-1). Values >0.5 indicate correct overall fold.

iptm

number

Interface predicted TM-score (0-1). Values >0.8 = high quality, <0.6 = likely failed, 0.6-0.8 = uncertain.

fraction_disordered

number

Fraction of structure that is disordered (0-1), measured by accessible surface area.

has_clash

boolean

True if >50% of a chain has clashes, or >100 clashing atoms in any chain.

ranking_score

number

Composite score for ranking predictions (-100 to 1.5).

chain_ptm

array<number>

Per-chain pTM scores. Element i is pTM restricted to chain i.

chain_iptm

array<number>

Per-chain interface confidence. Average ipTM between each chain and all others.

chain_pair_iptm

array<array<number>>

[num_chains, num_chains] matrix. Off-diagonal (i,j) = ipTM for chains i-j interface. Diagonal (i,i) = pTM for chain i.

chain_pair_pae_min

array<array<number>>

[num_chains, num_chains] matrix. Element (i,j) = minimum PAE from chain i to chain j. Correlates with binding interactions.

Full Confidences JSON

Detailed per-atom and per-token arrays:

atom_plddts

array<number>

[num_atoms] array of per-atom predicted lDDT scores (0-100). Higher = more confident.

pae

array<array<number>>

[num_tokens, num_tokens] matrix. Element (i,j) = predicted error in position of token j when aligned using token i’s frame.

contact_probs

array<array<number>>

[num_tokens, num_tokens] matrix. Element (i,j) = probability tokens i and j are within 8Å.

token_chain_ids

array<string>

[num_tokens] array mapping tokens to chain IDs.

atom_chain_ids

array<string>

[num_atoms] array mapping atoms to chain IDs.

mmCIF Structure Files

The .cif files contain predicted 3D coordinates in the standard mmCIF format.

AlphaFold 3 does not output PDB format. Use standard tools to convert mmCIF to PDB if needed:

# Using PyMOL
pymol -c -d "load model.cif; save model.pdb"

# Using gemmi
gemmi convert model.cif model.pdb

Viewing Structures

Compatible with most structural biology tools:

PyMOL: pymol model.cif
ChimeraX: chimerax model.cif
VMD: vmd model.cif
Mol*: Web-based viewer at https://molstar.org

Data JSON File

The <job>_data.json file contains your original input augmented with:

MSAs generated by genetic search
Structural templates found by template search
Other data pipeline outputs

This file can be reused as input with --norun_data_pipeline to skip expensive genetic searches.

Chirality Checks

For ligand predictions, chirality errors can be assessed using the provided utility:

from alphafold3.model.scoring.chirality import compare_chirality

# Compare predicted structure chirality against CCD reference
result = compare_chirality(predicted_mol, reference_mol)

In the AlphaFold 3 paper, a chirality penalty was applied to ranking scores for ligands. Running multiple seeds with chiral-aware ranking greatly reduces error rates.

File Sizes

Typical file sizes for a 5,000-token prediction:

File Type	Approximate Size
mmCIF structure	5-20 MB
Confidence JSON	200-500 MB
Summary JSON	1-10 KB
Distogram (optional)	~3 GB
Embeddings (optional)	~6 GB

Getting Started

Core Concepts

User Guides

Advanced Usage

Resources

Output Directory Structure

Example Directory Layout

Output Files

Top-Level Files

model.cif

confidences.json

summary_confidences.json

data.json

Per-Sample Subdirectories

Optional Output Files

Multi-Seed and Multi-Sample Results

Ranking Predictions

Chain-Specific Ranking

Confidence JSON Files

Summary Confidences JSON

Full Confidences JSON

mmCIF Structure Files

Viewing Structures

Data JSON File

Chirality Checks

File Sizes

Next Steps

Confidence Metrics

Input Format

Build docs developers (and LLMs) love

Getting Started

Core Concepts

User Guides

Advanced Usage

Resources

​Output Directory Structure

​Example Directory Layout

​Output Files

​Top-Level Files

model.cif

confidences.json

summary_confidences.json

data.json

​Per-Sample Subdirectories

​Optional Output Files

​Multi-Seed and Multi-Sample Results

​Ranking Predictions

​Chain-Specific Ranking

​Confidence JSON Files

​Summary Confidences JSON

​Full Confidences JSON

​mmCIF Structure Files

​Viewing Structures

​Data JSON File

​Chirality Checks

​File Sizes

​Next Steps

Confidence Metrics

Input Format

Build docs developers (and LLMs) love

Output Directory Structure

Example Directory Layout

Output Files

Top-Level Files

Per-Sample Subdirectories

Optional Output Files

Multi-Seed and Multi-Sample Results

Ranking Predictions

Chain-Specific Ranking

Confidence JSON Files

Summary Confidences JSON

Full Confidences JSON

mmCIF Structure Files

Viewing Structures

Data JSON File

Chirality Checks

File Sizes

Next Steps