AlphaFold
DeepMind (Google) / ebi.ac.uk
DeepMind's AI system that predicts protein 3D structures from amino acid sequences with near-experimental accuracy, solving a 50-year grand challenge in biology and transforming drug discovery and structural biology.
Pricing
Free
Free plan
Yes
Category
Education
Platforms
3
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
3
What is AlphaFold?
AlphaFold is arguably the most impactful single AI system ever created in terms of scientific and human benefit. Developed by DeepMind (a Google subsidiary), it predicts the 3D structure of proteins from their amino acid sequences with near-experimental accuracy — solving the protein folding problem that structural biologists had worked on for over 50 years.
Prior to AlphaFold, determining a protein's 3D structure required years of experimental work using X-ray crystallography, cryo-electron microscopy, or NMR spectroscopy. AlphaFold predictions take minutes to hours on a computer and achieve accuracy competitive with experimental methods for many proteins.
The AlphaFold Protein Structure Database, developed with EMBL's European Bioinformatics Institute, contains predicted structures for over 200 million proteins spanning virtually every organism with a sequenced genome — essentially the entire known protein universe. This database is freely available for researchers globally.
AlphaFold's impact on drug discovery is significant: understanding a target protein's 3D structure helps researchers design molecules that fit into specific binding sites. Pharmaceutical companies now use AlphaFold structures as starting points for drug discovery programmes that would previously have required years of crystallography work.
Demis Hassabis and John Jumper of DeepMind received the 2024 Nobel Prize in Chemistry for AlphaFold, the first Nobel Prize awarded for AI-driven science.
How AlphaFold works
AlphaFold runs as deep learning model software built around data workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on web, api, and python, with API access for teams that want to embed it into their own products.
Watch AlphaFold in action
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What makes it worth shortlisting
The capabilities that matter most for teams evaluating AlphaFold.
Protein structure prediction
Predicts 3D protein structures from amino acid sequences with near-experimental accuracy in minutes to hours rather than years of experimental work.
200M+ protein database
Freely available predicted structures for virtually every known protein across all sequenced organisms, openly accessible to researchers globally.
Confidence scoring
Per-residue confidence scores (pLDDT) and Predicted Aligned Error (PAE) for identifying reliable versus uncertain regions in predicted structures.
Best use cases
Who should use it
Pros
- Solved a 50-year scientific grand challenge — arguably the most impactful AI application in scientific history
- 200M+ protein structure database is freely available to all researchers globally
- 2024 Nobel Prize in Chemistry validates scientific importance and accuracy
- AlphaFold 3 extends to protein-small molecule and protein-DNA complex prediction
Cons
- Non-commercial use only for AlphaFold Server — commercial drug discovery applications require separate licensing
- Structure predictions have confidence scores and should be validated for critical applications
- Computational drug discovery still requires significant additional work beyond structure prediction alone
Is it worth the price?
AlphaFold Protein Structure Database is free. AlphaFold Server for new structure predictions is free for non-commercial use. Commercial use of AlphaFold in drug discovery is licensed separately.
Model
Free
Starting price
Free
Free trial
No
Tools like AlphaFold
RoseTTAFold is an open source alternative protein structure predictor from the Baker Lab. ESMFold (Meta AI) provides fast structure prediction. Schrödinger provides commercial protein modelling with more analysis tools.
AlphaFold vs Carnegie Mellon AI
A side-by-side look at the closest alternative in this category.
AlphaFold
DeepMind (Google)
Carnegie Mellon AI
Carnegie Mellon University
AlphaFold Protein Structure Database is free. AlphaFold Server for new structure predictions is free for non-commercial use. Commercial use of AlphaFold in drug discovery is licensed separately.
Educational resources and research tools largely free. Specific courses and professional programmes vary. Most resources publicly available.
Data processed under EMBL-EBI terms. Open source code under Apache 2.0. Commercial use licensing available through Isomorphic Labs. AlphaFold Server for non-commercial research use.
Standard university data handling. Open educational resources under creative commons. Research data subject to individual project terms.
Review EMBL-EBI data handling terms. AlphaFold Server use is for non-commercial research. Sequences submitted to AlphaFold Server are processed on DeepMind's infrastructure.
CMU handles student and research data under standard US university privacy frameworks. Open course materials and research publications are publicly available without personal data requirements.
- Solved a 50-year scientific grand challenge — arguably the most impactful AI application in scientific history
- 200M+ protein structure database is freely available to all researchers globally
- 2024 Nobel Prize in Chemistry validates scientific importance and accuracy
- AlphaFold 3 extends to protein-small molecule and protein-DNA complex prediction
- Top-ranked AI/CS university globally with decades of foundational research contributions
- Open course materials provide free access to world-class AI education content
- Research publications cover cutting-edge AI across all subfields
- Alumni who founded or lead major AI companies including Duolingo, DeepMind talent, and many others
- Non-commercial use only for AlphaFold Server — commercial drug discovery applications require separate licensing
- Structure predictions have confidence scores and should be validated for critical applications
- Computational drug discovery still requires significant additional work beyond structure prediction alone
- Not a product — CMU provides education and research resources rather than deployable AI tools
- Competitive admission for professional degree programmes limits accessibility
- Research papers require technical background to fully utilise
Technical & deployment info
Key facts about model providers, platforms, and team support.
Model Provider
DeepMind
Models
AlphaFold 3
Platforms
Web, API, Python
Deployment
SaaS, Open Source
Integrations
PyMOL, ChimeraX, GROMACS, PDB, EMBL-EBI, API
Team Collaboration
No
Launch Year
2021
Security & privacy
Compliance signals and data-handling notes as reported by the vendor.
Data processed under EMBL-EBI terms. Open source code under Apache 2.0. Commercial use licensing available through Isomorphic Labs. AlphaFold Server for non-commercial research use.
Review EMBL-EBI data handling terms. AlphaFold Server use is for non-commercial research. Sequences submitted to AlphaFold Server are processed on DeepMind's infrastructure.
What users are saying
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Common questions about AlphaFold
Editorial Verdict
Should you use AlphaFold?
AlphaFold is the most important AI tool in the life sciences, essential for any structural biology or drug discovery research programme. Every researcher working with proteins should be using the AlphaFold database.
Last verified July 24, 2026.