# AlphaFold

> software by DeepMind

**Wikidata**: [Q60827595](https://www.wikidata.org/wiki/Q60827595)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/AlphaFold)  
**Source**: https://4ort.xyz/entity/alphafold

## Summary
AlphaFold is a software developed by Google DeepMind that uses artificial intelligence to predict the 3D structures of proteins from their amino acid sequences. It is built using TensorFlow and PyTorch, and has been instrumental in advancing protein structure prediction, with versions released as far back as 2018 and as recent as 2023.

## Key Facts
- Developed by Google DeepMind, an artificial intelligence company owned by Google.
- Built using TensorFlow and PyTorch, two open-source machine learning libraries.
- Primarily used for protein structure prediction, a critical task in biochemistry and drug discovery.
- Released under the Apache Software License 2.0.
- Available in multiple versions, with the latest stable version being 2.3.2 as of 2023.
- Written in Python, a general-purpose programming language.
- First version released in 2018.
- Hosted on GitHub at [https://github.com/google-deepmind/alphafold](https://github.com/google-deepmind/alphafold).
- Accessible via the AlphaFold Protein Structure Database at [https://alphafold.ebi.ac.uk/](https://alphafold.ebi.ac.uk/).
- Has a Wikipedia page in multiple languages, including English, German, French, and Spanish.
- Includes a commons category for related media files.
- Features a video description by Lex Fridman on YouTube.

## FAQs
- **What is AlphaFold used for?** AlphaFold is used to predict the 3D structures of proteins from their amino acid sequences, which is essential for understanding protein function and designing drugs.
- **Who developed AlphaFold?** AlphaFold was developed by Google DeepMind, an artificial intelligence company.
- **What programming languages is AlphaFold written in?** AlphaFold is written in Python.
- **What are the key versions of AlphaFold?** Key versions include 2.0.0 (2021), 2.1.2 (2022), 2.2.4 (2022), and 2.3.2 (2023).
- **What license does AlphaFold use?** AlphaFold is released under the Apache Software License 2.0.
- **Where can I find the source code for AlphaFold?** The source code is available on GitHub at [https://github.com/google-deepmind/alphafold](https://github.com/google-deepmind/alphafold).
- **How does AlphaFold compare to other protein structure prediction tools?** AlphaFold is notable for its high accuracy and use of deep learning, distinguishing it from traditional computational methods.

## Why It Matters
AlphaFold revolutionized protein structure prediction by leveraging artificial intelligence to achieve unprecedented accuracy. Its ability to predict protein structures from amino acid sequences has accelerated drug discovery, biotechnology, and our understanding of biological systems. By providing atomic-resolution models, AlphaFold has enabled researchers to study protein functions, design novel enzymes, and develop targeted therapies. Its impact spans academia, industry, and healthcare, making it a cornerstone of modern computational biology.

## Notable For
- Achieved groundbreaking accuracy in protein structure prediction, often outperforming traditional methods.
- Released multiple versions with incremental improvements, reflecting ongoing advancements in AI.
- Open-sourced under the Apache License, promoting widespread adoption and collaboration.
- Integrated with major machine learning frameworks like TensorFlow and PyTorch.
- Featured in academic publications and media, highlighting its scientific and technological significance.

## Body
### Overview
AlphaFold is a software developed by Google DeepMind to predict the 3D structures of proteins from their amino acid sequences. It represents a major advancement in computational biology, leveraging artificial intelligence to solve a long-standing challenge in biochemistry.

### Development
AlphaFold was created by Google DeepMind, an AI research lab owned by Google. The project was initiated in 2018 and has since undergone multiple iterations, with significant updates released in 2021, 2022, and 2023. The software is built using TensorFlow and PyTorch, two prominent machine learning libraries.

### Functionality
AlphaFold specializes in protein structure prediction, a process that constructs atomic-resolution models of proteins. This capability is crucial for understanding protein function, designing drugs, and advancing biotechnology. The software uses deep learning algorithms to analyze amino acid sequences and generate precise 3D structures.

### Versions
AlphaFold has been released in several versions, including 2.0.0 (2021), 2.1.2 (2022), 2.2.4 (2022), and 2.3.2 (2023). Each version includes improvements and bug fixes, reflecting ongoing development and refinement.

### Licensing
AlphaFold is open-sourced under the Apache Software License 2.0, which allows for broad use and modification. This licensing model encourages collaboration and innovation within the scientific community.

### Accessibility
The software is accessible via the AlphaFold Protein Structure Database, hosted at [https://alphafold.ebi.ac.uk/](https://alphafold.ebi.ac.uk/). Users can download and use the software for their research, with support provided through the official GitHub repository.

### Media and Documentation
AlphaFold is documented on Wikipedia in multiple languages, including English, German, French, and Spanish. It also features a commons category for related media files and a video description by Lex Fridman on YouTube.

### Impact
AlphaFold has had a profound impact on the field of computational biology, accelerating drug discovery and biotechnological research. Its ability to predict protein structures with high accuracy has transformed how scientists approach protein analysis and design.

## References

1. [Release 2.0.0. 2021](https://github.com/google-deepmind/alphafold/releases/tag/v2.0.0)
2. [Release 2.0.1. 2021](https://github.com/google-deepmind/alphafold/releases/tag/v2.0.1)
3. [Release 2.1.0. 2021](https://github.com/google-deepmind/alphafold/releases/tag/v2.1.0)
4. [Release 2.1.1. 2021](https://github.com/google-deepmind/alphafold/releases/tag/v2.1.1)
5. [Release 2.1.2. 2022](https://github.com/google-deepmind/alphafold/releases/tag/v2.1.2)
6. [Release 2.2.0. 2022](https://github.com/google-deepmind/alphafold/releases/tag/v2.2.0)
7. [Release 2.2.1. 2022](https://github.com/google-deepmind/alphafold/releases/tag/v2.2.1)
8. [Release 2.2.2. 2022](https://github.com/google-deepmind/alphafold/releases/tag/v2.2.2)
9. [Release 2.2.3. 2022](https://github.com/google-deepmind/alphafold/releases/tag/v2.2.3)
10. [Release 2.2.4. 2022](https://github.com/google-deepmind/alphafold/releases/tag/v2.2.4)
11. [Release 2.3.0. 2022](https://github.com/google-deepmind/alphafold/releases/tag/v2.3.0)
12. [Release 2.3.1. 2023](https://github.com/google-deepmind/alphafold/releases/tag/v2.3.1)
13. [Release 2.3.2. 2023](https://github.com/google-deepmind/alphafold/releases/tag/v2.3.2)
14. [AlphaFold Protein Structure Database](https://alphafold.ebi.ac.uk/)
15. [Source](https://golden.com/wiki/AlphaFold-5KPBZA4)