Read about solving protein folding at deepmind.com/AlphaFold and see a timeline of our breakthrough here.

It’s been one year since we released and open sourced AlphaFold, our AI system to predict the 3D structure of a protein just from its 1D amino acid sequence, and created the AlphaFold Protein Structure Database (AlphaFold DB) to freely share this scientific knowledge with the world. Proteins are the building blocks of life, they underpin every biological process in every living thing. And, because a protein’s shape is closely linked with its function, knowing a protein’s structure unlocks a greater understanding of what it does and how it works. We hoped this groundbreaking resource would help accelerate scientific research and discovery globally, and that other teams could learn from and build on the advances we made with AlphaFold to create further breakthroughs. That hope has become a reality far quicker than we had dared to dream. Just twelve months later, AlphaFold has been accessed by more than half a million researchers and used to accelerate progress on important real-world problems ranging from plastic pollution to antibiotic resistance.

Today, I’m incredibly excited to share the next stage of this journey. In partnership with EMBL’s European Bioinformatics Institute (EMBL-EBI), we’re now releasing predicted structures for nearly all catalogued proteins known to science, which will expand the AlphaFold DB by over 200x – from nearly 1 million structures to over 200 million structures – with the potential to dramatically increase our understanding of biology.

This update includes predicted structures for plants, bacteria, animals, and other organisms, opening up many new opportunities for researchers to use AlphaFold to advance their work on important issues, including sustainability, food insecurity, and neglected diseases.

Today’s update means that most pages on the main protein database UniProt will come with a predicted structure. All 200+ million structures will also be available for bulk download via Google Cloud Public Datasets, making AlphaFold even more accessible to scientists around the world.

AlphaFold’s impact so far

Twelve months on from AlphaFold’s initial release, it’s been amazing to reflect on the incredible impact AlphaFold has already had, and our long journey to reach today’s milestone.

For our team, AlphaFold’s success was especially rewarding, both because it was the most complex AI system we’d ever built, requiring multiple critical innovations, and because it has had the most meaningful downstream impact. By demonstrating that AI could accurately predict the shape of a protein down to atomic accuracy, at scale and in minutes, AlphaFold not only provided a solution to a 50-year grand challenge, it also became the first big proof point of our founding thesis: that artificial intelligence can dramatically accelerate scientific discovery, and in turn advance humanity.

We open sourced AlphaFold’s code and published two in-depth papers in Nature [1, 2], which have already been cited more than 4000 times. We collaborated closely with the world-leading EMBL-EBI to design a tool that would best help biologists access and use AlphaFold, and together released the AlphaFold DB, a searchable database that is open and free to all. Before releasing AlphaFold, in line with our careful approach to pioneering responsibly, we sought input from more than 30 experts across biology research, security, ethics and safety to help us understand how to share the benefits of AlphaFold with the world, in a way that would maximise potential benefit and minimise potential risk.

To date, more than 500,000 researchers from 190 countries have accessed the AlphaFold DB to view over 2 million structures. Our freely available structures have also been integrated into other public datasets, such as Ensembl, UniProt, and OpenTargets, where millions of users access them as part of their everyday workflows.

We’ve been amazed by the rate at which AlphaFold has already become an essential tool for hundreds of thousands of scientists in labs and universities across the world to help them in their important work. As for our own work with AlphaFold, we prioritised applications that we felt would have the most positive social benefit, with a focus on initiatives that had been historically underfunded or overlooked. For example, we partnered with the Drugs for Neglected Diseases initiative (DNDi) to help advance their research, moving them closer to finding life-saving cures for diseases like Leishmaniasis and Chagas disease that disproportionately affect people in poorer parts of the world. We also supported World Neglected Tropical Disease Day by creating structure predictions for organisms identified by the World Health Organisation as high-priority for their research, helping to further the study of diseases like Leprosy and Schistosomiasis, which devastate the lives of more than 1 billion people globally.

It’s been so inspiring to see the myriad ways the research community has taken AlphaFold, using it for everything from understanding diseases, to protecting honey bees, to deciphering biological puzzles, to looking deeper into the origins of life itself.

Other impressive examples, chosen by members of our AlphaFold team, include:

A biological jigsaw, chosen by Kathryn Tunyasuvunakool

In a recent special issue of Science, several groups described how AlphaFold helped them piece together the nuclear pore complex, one of the most fiendish puzzles in biology. The giant structure consists of hundreds of protein parts and controls everything that goes in and comes out of the cell nucleus. Its delicate structure was finally revealed by using existing experimental methods to reveal its outline and AlphaFold predictions to complete and interpret any areas that were unclear. This powerful combination is now becoming routine in labs, unlocking new science and showing how experimental and computational techniques can work together.

A new world of bioinformatics, chosen by Richard Evans

Structural search tools like Foldseek and Dali are allowing users to very quickly search for entries similar to a given protein. This could be a first step toward mining large sequence datasets for practically useful proteins, such as those that break down plastic, and it could provide clues about protein function. The update of the database to include over 200 million predicted structures will further amplify this impact.

Direct impact on human health, chosen by John Jumper

AlphaFold is already having a significant, direct impact on human health. Meeting with researchers at the European Society of Human Genetics revealed how important AlphaFold structures are to biologists and clinicians trying to unravel the causes of rare genetic diseases. In addition, AlphaFold is accelerating drug discovery by providing a better understanding of newly identified proteins that could be drug targets, and helping scientists to more quickly find potential medicines that bind to them.

Just the beginning

AlphaFold has launched biology into an era of structural abundance, unlocking scientific exploration at digital speed. The AlphaFold DB serves as a ‘google search’ for protein structures, providing researchers with instant access to predicted models of the proteins they’re