AlphaFold — Google DeepMind

Accelerating breakthroughs in biology with AI
Powered by AlphaFold 3 — AlphaFold Server predicts how proteins will interact with other molecules throughout cells.
View over 200 million protein structure predictions to support your research.
Access the AlphaFold 3 model code and weights for academic use.
AlphaFold has revealed millions of intricate 3D protein structures, and is helping scientists understand how life’s molecules interact.
Proteins underpin every biological process, in every living thing.
Made from long chains of amino acids, each has a unique complex 3D structure. But figuring out just one of these can take several years, and hundreds of thousands of dollars. In 2020, AlphaFold solved this problem, with the ability to predict protein structures in minutes, to a remarkable degree of accuracy.
That’s helping researchers understand what individual proteins do and how they interact with other molecules. So valuable time and resources can be redirected into advancing research that could help solve society’s biggest medical and environmental challenges.
In November 2020, AlphaFold was recognised as a solution to the 50-year “protein-folding problem”.
DeepMind’s AlphaGo program defeats legendary Go player Lee Sae Dol in a challenge match in Seoul. This watershed moment demonstrated that DeepMind’s AI techniques were potentially advanced enough to be applied to scientific challenges. A small team is established to start working on protein structure prediction.
AlphaFold’s performance is benchmarked in the 13th Critical Assessment of Protein Structure Prediction (CASP13), placing first in the rankings (under entry A7D). The methods are subsequently published in the scientific journal Nature. The team is expanded, and work begins on an innovative new system.
AlphaFold2 wins CASP14 by a huge margin and is recognised as a solution to the 50-year-old “protein folding problem” by the organisers of CASP after predicting structures down to atomic accuracy with a median error (RMSD_95) of less than 1 Angstrom - 3 times more accurate than the next best system and comparable to experimental methods.
Nature publishes AlphaFold’s detailed methodology in the paper “Highly accurate protein structure prediction with AlphaFold” and DeepMind open sources the code along with 60 pages of supplemental information. To-date, the paper has been cited over 40,000 times in scientific journals.
A week later, Nature publishes a second DeepMind paper containing the structure predictions of the entire human proteome. In close collaboration with the European Bioinformatics Institute at the European Molecular Biology Laboratory (EMBL-EBI), DeepMind launches the AlphaFold Protein Structure Database (AFDB) to give the scientific community free of charge and open access to the human proteome along with another 20 model organisms - over 350,000 structures in total.
In partnership with EMBL’s European Bioinformatics Institute (EMBL-EBI), DeepMind releases the predicted structures for nearly all catalogued proteins known to science, expanding the AFDB to over 200 million structures.
Google DeepMind and Isomorphic Labs introduce AlphaFold 3, which predicts the structure and interactions of all of life’s molecules. At the same time, Google DeepMind launches AlphaFold Server, a platform that provides scientists free access to AF3 structure prediction capabilities for non-commercial research.
Demis Hassabis and John Jumper are co-awarded the Nobel Prize in Chemistry for their work on AlphaFold, alongside David Baker for his work on computational protein design.
AlphaFold is being used by over 3 million researchers from over 190 countries around the world, tackling problems such as antimicrobial resistance, crop resilience and heart disease.
What took us months and years to do, AlphaFold was able to do in a weekend.
Over 2 million researchers in over 190 countries are using the AlphaFold Protein Structure Database to inform their research.
91% of all plastic ever produced has never been recycled. AlphaFold could help us face up to the challenge of cleaning up our world.
40% of the world’s crops are lost to disease each year. AlphaFold could unlock insights that help keep food on tables.
AlphaFold has already made a significant impact. We hope it will eventually help to transform our understanding of the biological world.
So far, AlphaFold has predicted over 200 million protein structures – nearly all catalogued proteins known to science. The AlphaFold Protein Structure Database makes this data freely available. So far, it has over three million users from over 190 countries. That means it has already potentially saved millions of dollars and hundreds of millions of years in research time.
Meanwhile, AlphaFold Server predicts how proteins will interact with a broad spectrum of biomolecules, accelerating new research.
Research years saved by AlphaFold database structures.
Users in low and middle income countries.
Of papers citing AlphaFold are related to the study of disease.
Scientists are using AlphaFold to accelerate research in nearly every field of biology.
AlphaFold has allowed us to take our project to the next level, from a fundamental science stage to the preclinical and clinical development stage.
A powerful suite of tools, helping the scientific community advance research.
View over 200 million protein structure predictions to support your research.
AlphaFold Server predicts how proteins will interact with other molecules throughout cells.
Access the AlphaFold 3 model code and weights for academic use.
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