> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/google-deepmind/alphafold3/llms.txt
> Use this file to discover all available pages before exploring further.
# AlphaFold 3 - Accurate Biomolecular Structure Prediction
> Implementation of the AlphaFold 3 inference pipeline for predicting protein, RNA, DNA, and ligand structures with state-of-the-art accuracy
# AlphaFold 3
This package provides an implementation of the inference pipeline of AlphaFold 3 - a revolutionary AI system for predicting the structure and interactions of biomolecules with unprecedented accuracy.
Run your first structure prediction in minutes
Complete setup guide with Docker and dependencies
Get access to AlphaFold 3 model weights
Use AlphaFold 3 online for non-commercial research
## Overview
AlphaFold 3 extends the capabilities of previous versions to predict structures of:
* **Proteins** with post-translational modifications
* **RNA and DNA** with modified nucleotides
* **Ligands** using SMILES or CCD codes
* **Protein-ligand complexes** with covalent bonds
* **Biomolecular interactions** across all molecule types
Any publication using this source code, model parameters, or outputs should cite the [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://doi.org/10.1038/s41586-024-07487-w) paper published in Nature.
## Key Features
Predict structures for proteins, RNA, DNA, ligands, and their complexes in a single model
Support for CCD codes, SMILES strings, and user-defined chemical components
Model covalent bonds between entities and post-translational modifications
Per-atom pLDDT, PAE, pTM and ipTM confidence scores
Provide your own multiple sequence alignments and structural templates
Efficient inference on NVIDIA A100 and H100 GPUs
## System Requirements
AlphaFold 3 requires Linux and does not support other operating systems. Full installation requires up to 1 TB of disk space for genetic databases.
### Minimum Hardware
* **OS**: Linux (Ubuntu 22.04 LTS recommended)
* **GPU**: NVIDIA GPU with Compute Capability 8.0+ (A100 80GB or H100 80GB officially supported)
* **RAM**: 64 GB minimum (more for deep MSAs)
* **Storage**: 1 TB SSD recommended for genetic databases
* **CUDA**: Version 12.6+
### Supported Input Sizes
* Up to **5,120 tokens** on NVIDIA A100 80 GB
* Up to **5,120 tokens** on NVIDIA H100 80 GB
* Smaller inputs supported on GPUs with less memory
## Input Format
AlphaFold 3 uses a flexible JSON input format that allows you to specify:
```json Simple Protein theme={null}
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
```
```json Protein-Ligand Complex theme={null}
{
"name": "protein_with_atp",
"sequences": [
{
"protein": {
"id": "A",
"sequence": "MKVLWAALLVTFLAGCQAKV..."
}
},
{
"ligand": {
"id": "B",
"ccdCodes": ["ATP"]
}
}
],
"modelSeeds": [1, 2, 3],
"dialect": "alphafold3",
"version": 1
}
```
```json RNA Structure theme={null}
{
"name": "rna_prediction",
"sequences": [
{
"rna": {
"id": "R",
"sequence": "AGCUAGCUAGCU",
"modifications": [
{"modificationType": "2MG", "basePosition": 1}
]
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
```
## Output Structure
AlphaFold 3 produces comprehensive outputs including:
* **Predicted structures** in mmCIF format
* **Confidence metrics** including pLDDT, PAE, pTM, and ipTM scores
* **Per-atom confidence** estimates
* **Ranking scores** across multiple seeds and samples
* **Optional embeddings** for downstream analysis
## Terms of Use
You may only use AlphaFold 3 model parameters if received directly from Google. Use is subject to the [AlphaFold 3 Model Parameters Terms of Use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md).
### Important Disclaimers
* AlphaFold 3 and its outputs are for **theoretical modeling only**
* **Not intended, validated, or approved for clinical use**
* Do not use for clinical purposes or rely on for medical advice
* Predictions have varying confidence levels - interpret carefully
* Source code is licensed under **CC-BY-NC-SA 4.0** (non-commercial use)
## Get Started
Set up AlphaFold 3 with Docker on Linux
Run your first prediction
Get model parameters from Google DeepMind
## Get in Touch
If you have any questions not covered in this documentation, please contact the AlphaFold team at [alphafold@google.com](mailto:alphafold@google.com).
We would love to hear your feedback and understand how AlphaFold 3 has been useful in your research.
***
This is not an officially supported Google product.
Copyright 2024 DeepMind Technologies Limited.