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
Rating
4.9 (15000)
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
3
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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.
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
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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 MagicSchool AI
A side-by-side look at the closest alternative in this category.
AlphaFold
DeepMind (Google)
MagicSchool AI
MagicSchool AI
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.
Free plan for individual teachers with access to 80+ tools. Individual Plus $3/month. School and District plans at custom pricing.
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.
FERPA and COPPA compliant. Student data not used to train AI models. SOC 2 compliant. School plans include data processing agreements.
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.
MagicSchool is FERPA and COPPA compliant, designed with student privacy in mind. Student data is not used to train AI models. Review school plan data handling agreements before district deployment.
- 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
- Specifically designed for teacher workflows with 80+ purpose-built tools
- Very affordable at $3/month for individual teachers
- FERPA and COPPA compliant for school use
- Lesson plan and differentiation tools address the highest-value teacher use cases
- 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
- Designed for K-12 education, less relevant outside that context
- Individual teacher free plan has usage limits
- School deployment requires district procurement process
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
Verified reviews from signed-in users, stored in the backend and averaged into this tool's rating.
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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.