Rapid new drug discovery had never been more critical in a world with an aging population and increased instances of infectious diseases. While traditional lab methods have proven reliable, the recent covid-19 pandemic has shown the need for further innovation. The global drug discovery market size was valued at US$ 74.96 billion in 2021.[1] Despite such massive investments, the number of new drugs approved by the FDA remains low. Current drug discovery methods are slow, expensive, dominated by big pharma, and require cruel animal testing procedures.

We first entered CASP13 in 2018 with our initial version of AlphaFold, which achieved the highest accuracy among participants. Afterwards, we published a paper on our CASP13 methods in Nature with associated code, which has gone on to inspire other work and community-developed open source implementations. Now, new deep learning architectures we've developed have driven changes in our methods for CASP14, enabling us to achieve unparalleled levels of accuracy. These methods draw inspiration from the fields of biology, physics, and machine learning, as well as of course the work of many scientists in the protein folding field over the past half-century. A folded protein can be thought of as a "spatial graph", where residues are the nodes and edges connect the residues in close proximity.

DeepMind has already notched up a streak of wins, showcasing AIs that have learned to play a variety of complex games with superhuman skill, from Go and StarCraft to Atari's entire back catalogue. But Demis Hassabis, DeepMind's public face and co-founder, has always stressed that these successes were just stepping stones towards a larger goal: AI that actually helps us understand the world. Today DeepMind and the organizers of the long-running Critical Assessment of protein Structure Prediction (CASP) competition announced an AI that should have the huge impact that Hassabis has been after. The latest version of DeepMind's AlphaFold, a deep-learning system that can accurately predict the structure of proteins to within the width of an atom, has cracked one of biology's grand challenges. "It's the first use of AI to solve a serious problem," says John Moult at the University of Maryland, who leads the team that runs CASP.