Demis Hassabis

Founder & CEO, Google DeepMind; AlphaFold Project Lead
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For developing AlphaFold, which has been heralded as an AI-based solution to the 50-year grand challenge of protein structure prediction and has culminated in the release of the most accurate and complete picture of the structure of the human proteome, with enormous potential to accelerate biological and medical research.

Demis Hassabis is the Founder and CEO of DeepMind, the world’s leading AI research company, and now a part of Alphabet. Founded in 2010, DeepMind has been at the forefront of the field ever since, producing landmark research breakthroughs such as AlphaGo, the first program to beat the world champion at the complex game of Go, and AlphaFold, which was heralded as a solution to the 50-year grand challenge of protein folding.

A chess and programming child prodigy, Demis coded the classic AI simulation game Theme Park aged 17. After graduating from Cambridge University in computer science with a double first, he founded pioneering videogames company Elixir Studios, and completed a PhD in cognitive neuroscience at UCL investigating memory and imagination processes.

His work has been cited over 100,000 times and has featured in Science’s top 10 Breakthroughs of the Year on 4 separate occasions. He is a Fellow of the Royal Society, and the Royal Academy of Engineering. In 2017 he featured in the Time 100 list of most influential people, and in 2018 he was awarded a CBE.

The Work:

Proteins are essential to life, supporting practically all its functions. They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure. Figuring out what shapes proteins fold into from their amino acid sequence is known as the ‘protein structure prediction problem’ and has stood as a grand challenge in biology for the past 50 years. With their team at DeepMind, Demis Hassabis and John Jumper have developed the artificial intelligence (AI) system AlphaFold, which today can predict the structure of a protein, at scale and in minutes, down to atomic accuracy.

Hassabis had long suspected that protein structure prediction might be the perfect problem for AI to tackle. He was the project leader on the AlphaFold project from its inception in 2016 to its conclusion, and recruited Jumper to the project in late 2017. In 2018 the team was expanded, with Jumper becoming the new research lead, with the goal to re-design the system with a completely new architecture into what would become AlphaFold2. Together they co-supervised the subsequent projects to create the most accurate and complete picture of the human proteome and predict the structures of nearly all known proteins, and released an open-access database to make all of AlphaFold’s predictions freely available to the scientific community.     

In a major scientific advance, in 2020 AlphaFold2 was recognized as a solution to the 50-year grand challenge of protein structure prediction by the organizers of the biennial Critical Assessment of Protein Structure Prediction (CASP).

The Impact:

AlphaFold has culminated in the creation of structure predictions for over 200 million proteins - nearly every protein known to science - which DeepMind has made freely available through the AlphaFold Protein Structure Database (AlphaFold DB).

Designed in partnership with European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), the AlphaFold DB serves as a ‘google search’ for protein structures, providing researchers with instant access to predicted models of the proteins they're studying, which has the potential to accelerate every field of study in biology.

Since launch, the AlphaFold DB has already been accessed by 1 million researchers and users in 190 countries. The program dramatically reduces the time scientists typically spend determining protein structure and demonstrates the impact AI can have on scientific discovery and its potential to accelerate progress in some of the most fundamental fields that explain and shape our world. Further, this research will help to better our understanding of disease, and accelerate the development of new targeted drugs.