AlphaFold is a revolutionary artificial intelligence system developed by Google DeepMind in partnership with EMBL-EBI that predicts a protein's 3D structure from its amino acid sequence with accuracy competitive with experimental methods. The AlphaFold Database provides free access to over 200 million structure predictions covering a significant portion of known proteins across 48+ species. This breakthrough technology has transformed structural biology by making protein structure prediction accessible to researchers worldwide, accelerating drug discovery, disease research, and fundamental biological understanding. The system uses deep learning trained on existing protein structures to generate highly accurate predictions, complete with confidence metrics for each residue.
OVERVIEW
AI-powered protein structure prediction system that accurately predicts 3D protein structures from amino acid sequences
RATING & STATS
User Rating
4.9/5.0
8500 reviews
Customers
1,000,000+ researchers worldwide
Founded
2018
KEY FEATURES
- ✓Predicts 3D protein structures from amino acid sequences with experimental-level accuracy
- ✓Access to 200+ million pre-computed structure predictions via AlphaFold Database
- ✓AlphaMissense integration for proteome-wide missense variant effect prediction
- ✓Multimer prediction capabilities through open-source code
- ✓Downloadable multiple sequence alignments (MSAs) for each prediction
- ✓Confidence metrics (pLDDT scores) for prediction quality assessment
- ✓RESTful API for programmatic access to structure predictions
- ✓Regular database updates synchronized with UniProt releases
PRICING
Model: Free
Starting at: USD 0.00
Completely free for both academic and commercial use. All data available under CC-BY-4.0 license. Open-source code available on GitHub under Apache 2.0 license.
FREE TRIALFREE TIER
TECHNICAL DETAILS
Deployment: Cloud-based Database, On-premise via Docker, Local Installation (Linux)
Platforms: Linux, Web Browser (for database access), Docker (GPU required), Singularity (community support)
🔌 API Available⚡ Open Source
USE CASES
Drug discovery and rational drug design by predicting target protein structuresUnderstanding disease mechanisms through protein structure analysisProtein engineering and synthetic biology designEvolutionary biology research across species proteomesStructural genomics and proteomics studiesAgricultural research for crop improvement and disease resistance
INTEGRATIONS
UniProtProtein Data Bank (PDB)EMBL-EBI ServicesGitHubPython scientific stack (JAX, NumPy)Docker/Singularity containersNVIDIA GPU infrastructure
COMPLIANCE & SECURITY
Compliance:
Open Source Initiative Approved License (Apache 2.0)Creative Commons BY 4.0 for data
Security Features:
- 🔒Open-source code for security auditing
- 🔒Apache 2.0 license ensures transparency
- 🔒No data collection from users (database is publicly accessible)
- 🔒Self-hosted deployment option for sensitive data
SUPPORT & IMPLEMENTATION
Support: GitHub Issues, Community Forums, Scientific Publications, Documentation, AlphaFold Database Help Pages
Implementation Time: 1-2 weeks (includes database download and setup; immediate for database-only use)
Target Company Size: Academic Institutions, Research Labs, Pharmaceutical Companies, Biotechnology Startups, Enterprise Biotech, Government Research Agencies
TRAINING AVAILABLE
PROS & CONS
✓ Pros:
- +Completely free for academic and commercial use with open-source code
- +Revolutionary accuracy competitive with experimental methods (CASP14 winner)
- +Massive database with 200+ million pre-computed predictions saves computation time
- +Strong scientific validation with thousands of citations and Nature publications
- +Active development by Google DeepMind with continuous improvements
- +Comprehensive API and programmatic access for integration
- +No vendor lock-in due to open-source nature
✗ Cons:
- -Requires significant computational resources (NVIDIA GPU, 8+ GB RAM, 600GB+ storage)
- -Linux-only support limits accessibility for Windows/Mac users without Docker
- -Long setup time due to 2.62TB database download for full functionality
- -Steep learning curve for running local installations
- -Limited support for certain protein types (membrane proteins, disordered regions)
- -Predictions are computational models, not experimental structures
TRY IT OUT
ABOUT GOOGLE DEEPMIND
Website: https://www.deepmind.com