BIOGRAPHY

Co-founder & CEO at Deepmind.

Demis Hassabis is a child chess prodigy from the age of 4. He designed and programmed the multi-million selling, award-winning game Theme Park at the age of 17. Following graduation from Cambridge University (1994-1997), with a Double First in Computer Science he founded the pioneering videogames company Elixir Studios (1998-2005) producing award winning games for global publishers. After a decade of experience leading successful technology startups, Demis returned to academia to complete a PhD in cognitive neuroscience at University College London (2005-2009), followed by postdocs at Massachusetts Institute of Technology – MIT and Harvard. The journal Science listed his research on imagination and memory as one of 2007’s top ten breakthroughs. Demis is a Fellow of the Royal Society, Royal Academy of Engineering and the Royal Society of Arts. In 2010, Demis co-founded Deepmind, a neuroscience-inspired AI company, bought by Google in Jan 2014 in their largest European acquisition to date. In December 2018, DeepMind’s tool AlphaFold won the 13th Critical Assessment of Techniques for Protein Structure Prediction (CASP) by successfully predicting the most accurate structure for 25 out of 43 proteins.

In 2017 he featured in the Time 100 list of most influential people, and in 2018 he was awarded a CBE for services to science and technology. CBE stands for Commander of the Order of the British Empire, the CBE is the highest-ranking Order of the British Empire award. Hassabis is now Vice President of Engineering at Google DeepMind and leads Google’s general AI efforts, including the development of AlphaGo, the first program to ever beat a professional player at the game of Go

Hassabis has won many pretigous awards including Mullard Award (2014), Nature’s 10 (2016), Golden Joystick Award, Dan David Prize (2020), Wiley Prize (2021), IRI Medal (2021), Princess of Asturias Award (2022), and Breakthrough Prize in Life Sciences (2023).

SUMMARY OF WINNING ENTRY

Proteins do most of the work in cells and are required for the structure, function, and regulation of all body’s tissues and organs. All living things on Earth are made from proteins including animals, bacteria, plants, yeast, virus and so on. They are simple strings of amino acids that fold up from a linear chain into complex, compact 3D shapes. A protein can fold in a near infinite number of ways before reaching its final structure which determines the functions of the protein. A tiny change in the structure can dramatically change the functions of a protein. Therefore, determining the folding structures of the proteins are super important to understand their functions and more importantly, generate the suggestions for drug discovery. The structure of protein was thought to be determined by its amino acid sequence. But proving that was a whole different ball game, and the protein folding problem has been a headache that has plagued and puzzled scientists for 50 years. With the team DeepMind, Jumper and Hassabis conceived and constructed a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm, that accurately and rapidly models the structure of proteins.

With AlphaFold, AI (artificial intelligence) was proved that it 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, but 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. 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. 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, enabling them to focus their effort and expedite experimental work.

SCALE OF IMPACT

A system that can accurately predict protein structure has an enormous potential on benefiting many areas. It can help increase our understanding on neglected tropical diseases, the unknown protein structure of which is a barrier of curing them. Such diseases affect millions of people every year, causing tens of thousands of deaths. Due to limited information about human involved protein structures, the process of drug discovery is slow and costly, taking about 10 years and 2,5 billion dollars. In addition, a system like AlphaFold could help finding proteins and enzymes that can break down industrial plastic waste or efficiently capturing carbon from the atmosphere. Thus, the potential of using the protein structure for breaking down industrial plastic waste is very promising on assisting the global need for reducing the global material footprint.

Just after 12 months, 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. The database of AlphaFold today has 300+ million of protein structures (nearly every known protein from across the tree of life) while the experimental protein database has only 190,000 structures. AlphaFold is free for everyone. Jumper and Hassabis decided to open source AlphaFold’s code and published two in-depth articles in Nature, which have already been cited more than 4000 times in just more than a year. To date, more than 500,000 researchers from 190 countries have accessed the AlphaFold database to view over 2 million structures.

Traditionally, research has relied on expensive and time-consuming methods to work out structures, such as X-ray crystallography and electron microscopy. It can take from a few months to several years for a biologist to crack the puzzle. “Even then, success is not guaranteed – some proteins are notoriously difficult to find structures of” says Pushmeet Kohli, head of AI for science at DeepMind. With AlphaFold, any researcher will be able to get a protein structure in mere minutes. The Universal Protein database, a collection of all the proteins that science has uncovered thus far, contains over 180 million protein sequences. These protein sequences tell us how the amino acids in a protein are ordered. But, to really understand how proteins function in the body, we need to know how that sequence determines the 3D structure of the protein – and that is a much more difficult task than simply knowing the right order of amino acids. Of those 180 million protein sequences, scientists have so far worked out the structure of just 180,000 proteins. AlphaFold provides predictions for more than double the number of known protein structures to date. Now biologists will be able to work on understanding how proteins interact and function – and beyond that, designing new proteins, enabling quicker drug discovery, deciphering disease-causing gene variations and more.