open sourceother

AlphaFold

Revolutionizing protein structure prediction with deep learning.

Developed by DeepMind

Official Site
1.5BParams
YesAPI Available
stableStability
1.0Version
Apache 2.0License
JAXFramework
NoRuns Locally
Real-World Applications
  • Drug discoveryOptimized Capability
  • Protein engineeringOptimized Capability
  • Genomics researchOptimized Capability
  • Biomedical applicationsOptimized Capability
Implementation Example
Example Prompt
Predict the 3D structure of the given protein sequence: 'AKKLVSKLSVRGNLVFDK'.
Model Output
"The predicted 3D structure visualization of 'AKKLVSKLSVRGNLVFDK' is now available for analysis."
Advantages
  • ✓ Achieves higher accuracy in protein structure prediction compared to previous models.
  • ✓ Utilizes a robust architecture allowing rapid training and inference.
  • ✓ Open-sourced, enabling widespread accessibility and community-driven enhancements.
Limitations
  • ✗ Requires substantial computational resources for training and inference.
  • ✗ Accuracy may diminish for proteins outside of known structural families.
  • ✗ Limited to static predictions without dynamics of protein folding considered.
Model Intelligence & Architecture

Technical Documentation

AlphaFold is an innovative AI model developed by DeepMind that predicts 3D protein structures with remarkable accuracy. It has revolutionized the field of structural biology by enabling scientists and developers to understand protein folding mechanics using artificial intelligence.

Technical Overview

AlphaFold leverages advanced machine learning techniques to predict the spatial arrangement of amino acids in protein molecules. This model uses a complex pipeline integrating evolutionary data, physical and geometric constraints to produce highly accurate protein structures that match experimental results.

Framework & Architecture

  • Framework: JAX
  • Architecture: Deep neural networks tailored for protein folding prediction
  • Version: 1.0
  • Parameters: Detailed proprietary architecture combining multiple neural network components

AlphaFold's architecture is engineered to interpret biological sequences and incorporate structural biology principles within its network design, powered by JAX for fast and efficient computation.

Key Features / Capabilities

  • High-precision prediction of 3D protein structures
  • Integration of evolutionary, physical, and geometric information
  • Supports a broad range of proteins and complex structures
  • Open-source availability enabling community collaboration
  • Robustness validated by independent experiments and competitions

Use Cases

  • Drug discovery: Accelerate identification of therapeutic targets
  • Protein engineering: Design novel proteins with specific functions
  • Genomics research: Decode protein functions from genetic sequences
  • Biomedical applications: Support research on diseases linked to protein misfolding

Access & Licensing

AlphaFold is open-source under the Apache 2.0 license, allowing free usage for commercial and research applications. Developers can access the full source code on GitHub. Official information and updates are available from DeepMind's website. The model is implemented using JAX framework ensuring high-performance execution for large-scale protein folding tasks.

Technical Specification Sheet

FAQs

Technical Details
Architecture
Attention-based Neural Network
Stability
stable
Framework
JAX
Signup Required
No
API Available
Yes
Runs Locally
No
Release Date
2021-07-15

Best For

Biological research and computational biology applications.

Alternatives

RoseTTAFold, Folding@home

Pricing Summary

AlphaFold is open-source and freely available for use.

Compare With

AlphaFold vs RoseTTAFoldAlphaFold vs Folding@homeAlphaFold vs CAlphaAlphaFold vs PyMOL

Explore Tags

#protein structure prediction

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