Google DeepMind's AlphaFold and Computational Protein Design Win 2024 Nobel Prize in Chemistry

Chemistry Nobel goes to developers of AlphaFold AI that predicts protein structures

For the first time -- and probably not the last -- a scientific breakthrough enabled by artificial intelligence has been recognized with a Nobel Prize. The 2024 Chemistry Nobel was awarded to John Jumper and Demis Hassabis at Google DeepMind in London, UK, for developing a game-changing AI tool for predicting protein structures called AlphaFold, and David Baker, at the University of Washington in Seattle, for his work on computational protein design, which has been revolutionized by Al in recent years. The impact of AlphaFold, which was unveiled just a few years ago, has been nothing short of transformative. The tool has made protein structures -- often, but not always, highly accurate ones -- available to researchers at the touch of a button, and enabled experiments that were unimaginable a decade ago. Biologists now talk about an era 'before AlphaFold' and one after. "It has long been a dream to learn to predict the three-dimensional structure of proteins from knowing their amino acid sequences... for several decades, this was considered impossible," said Nobel committee chair Heiner Linke, who researches nanoscience at Lund University in Sweden, during the prize announcement. This year's laureates, he adds, "have cracked the code". The winners share a prize pot of 11 million Swedish kronor (US$1 million). DeepMind debuted AlphaFold in 2018, when it won a biannual protein structure prediction contest called the Critical Assessment of Protein Structure Prediction (CASP). But it was the second iteration of the deep learning neural network, revealed in late 2020, that set off an earthquake in the life sciences. Many of AlphaFold2's predictions at CASP were so accurate as to be indistinguishable from experimentally-solved protein structures. This prompted CASP co-founder and computational biologist John Moult, at the University of Maryland in College Park, to declare in 2020 that "in some sense, the problem is solved". Hassabis, DeepMind's co-founder and CEO, and Jumper, head of the AlphaFold team, led the development of AlphaFold2. To predict protein structures, the neural network incorporates similar structures from databases of hundreds of thousands of experimentally-solved structures and millions of sequences from related proteins -- which hold information about their shapes. In 2021 DeepMind made AlphaFold2's underlying code freely available, along with the data needed to train the model. An AlphaFold database, created with the European Molecular Biology Lab's European Bioinformatics Institute in Hinxton, UK, now holds the structures of almost all the proteins from every organism represented in genetic databases, some 214 million predictions in total. This year, the company unveiled a third version of AlphaFold, which can model other molecules that interact with proteins such as drugs. The revolution that Jumper, Hassabis and their colleagues unleashed is still in its early days, and AlphaFold's full impact on science may not be known for years. Already, the tool is helping scientists make new insights. One pioneering team used the tool, along with experimental data, to map the nuclear pore complex, one of our cells' largest machines, the genome's gatekeeper called the nuclear pore complex. Last year, two teams mined the entire AlphaFold Database to uncover the darkest corners of the protein universe, identifying new families of proteins and folds and surprising connections in the machinery of life. Many researchers hope that AlphaFold, and other AI tools it has inspired, will transform medicine, but it is not yet clear how, or indeed whether, AlphaFold will transform the costly and multi-step process of developing safe new drugs. More than a decade before DeepMind started working on AlphaFold, computational biophysicist David Baker, at the University of Washington in Seattle, and his colleagues developed software tools for modeling protein structures using physical principles called Rosetta. The tool had early success designing novel proteins. Over the years, Baker's team applied Rosetta to predicting protein structure -- it has been among the top entries at numerous CASPs, prior to AlphaFold's recent hegemony -- as well as designing novel proteins such as enzymes and self-assembling protein nanoparticles. When AlphaFold2 was announced -- but not yet released -- Baker and his team, including computational chemist Minkyung Baek, now at Seoul National University in South Korea, set out to understand the software and apply some of its tricks to a previous AI-based version of Rosetta. The first version of the resulting RoseTTAFold network performed nearly as well as AlphaFold2. Since 2021, both networks have been continually improved by their developers - and other scientists due to their open-source nature - to tackle new challenges such as predicting the structure of complexes of multiple different interacting proteins. In recent years, Baker's team have been especially prolific in applying machine learning to his lab's raison d'etre: creating new proteins never seen before in nature. A tool recently developed by Baker's team that melds RoseTTAFold with image-generating diffusion neural networks has led to a step-change in researchers' capacity to design proteins. Although computational tools such as AlphaFold aren't a replacement for experimental studies, they are an accelerator, scientists say."This is going to empower a new generation of molecular biologists to ask more advanced questions," CASP judge Andrei Lupas, an evolutionary biologist at the Max Planck Institute for Developmental Biology in Tübingen, Germany, told Nature in 2020.

Google's triumph on the Nobel stage resolves a 50-year-old mystery

AlphaFold 2, an AI model developed through Google's DeepMind initiative, cracked the code for predicting complex protein structures from amino acid sequences. It resulted in the accurate prediction of nearly 200 million known proteins. AlphaFold has been used by over two million researchers globally, in areas such as antibiotic resistance and creating enzymes capable of breaking down plastics.Google DeepMind's co-founder and chief executive Demis Hassabis, along with John Jumper, a researcher at DeepMind have been awarded the Nobel Prize in Chemistry for their breakthrough in protein structure prediction using artificial intelligence (AI). The duo was accompanied by the American biochemist David Baker. DeepMind's AI tool, AlphaFold has cracked the code for predicting complex protein structures from amino acid sequences, resolving a five-decade scientific enigma. The 2024 Nobel Prize for chemistry was announced on Wednesday 9 October 2024 by the Royal Swedish Academy of Sciences. Background David Baker is the director of the Institute for Protein Design at the University of Washington School of Medicine. Baker's research group develops innovative protein design software to tackle challenges in medicine, technology, and sustainability. The experiments make use of machine learning (ML) methods to generate functional proteins. Demis Hassabis is currently the chief executive officer of Google DeepMind and Isomorphic Labs. He, along with John Jumper, unveiled AlphaFold 2 in 2020. Hassabis is an AI researcher, who graduated from Cambridge. He has been associated with roles such as lead AI programmer and also started his own venture Elixir Studios, a London-based independent games developer. Further, pursuing PhD in cognitive neuroscience from University College London, he continued experimenting with AI as the CEO and co-founder of a machine learning AI startup DeepMind. AlphaFold 2 AlphaFold 2 is an AI model developed through Google's DeepMind initiative. The experiment resulted in the accurate prediction of nearly 200 million known proteins. AlphaFold is an AI system that predicts the 3D structure of proteins from their amino acid sequences. The technology has turned a once complex and time-consuming process into a seamless experience through its predictions available freely at AlphaFold Protein Structure Database. It has been used by over two million researchers worldwide, in areas such as antibiotic resistance and creating enzymes that are capable of breaking down plastics. Computational Protein Design In the year 2003, David Baker led the design of a new protein using bespoke software laying the groundwork for designer proteins. Bespoke software refers to customised software solutions designed specifically for a particular project. The computational method to design novel proteins transforms the possibilities of protein engineering. The new proteins are formed by manipulating 20 different amino acids. Baker has introduced new functions in the newly designed proteins such as degrading plastics, which are beyond the capabilities of natural proteins. This scientific breakthrough opens up new possibilities for designing proteins with tailored functions to address critical scientific and medical challenges, developing novel therapeutic solutions. Last year, the Nobel Prize for Chemistry was jointly awarded to Moungi G. Bawendi, Louis E. Brus and Alexei I. Ekimov for the discovery and synthesis of quantum dots.

Google DeepMind wins joint Nobel Prize in Chemistry for protein prediction AI

Half the prize goes to Demis Hassabis and John M. Jumper from Google DeepMind for using AI to solve protein folding, and the other to David Baker for creating new proteins. In a second Nobel win for AI, the Royal Swedish Academy of Sciences has awarded half of the 2024 Nobel Prize in Chemistry to Demis Hassabis, the co-founder and CEO of Google DeepMind and John M. Jumper, a director at Google DeepMind, for their work on using artificial intelligence to predict the structures of proteins, and the other half to David Baker, a professor in biochemistry at the University of Washington for his work on computational protein design. The winners will share a 11 million Swedish kronor ($1 million) prize pot. The potential impact of this research is enormous. Proteins are fundamental to life, but understanding what they do involves figuring out their structure -- a very hard puzzle that once took months or years to crack for each type of protein. By cutting down the time it takes to predict a protein's structure, computational tools such as those developed by this year's award winners are helping scientists gain a greater understanding of how proteins work and opening up new avenues of research and drug development. The technology could unlock more efficient vaccines, speed up research for the cure to cancer, or lead to completely new materials.

Google DeepMind leaders share Nobel Prize in chemistry for protein prediction AI

Half the prize goes to Demis Hassabis and John M. Jumper from Google DeepMind for using AI to solve protein folding, and the other to David Baker for tools to help design new proteins. In a second Nobel win for AI, the Royal Swedish Academy of Sciences has awarded half the 2024 prize in chemistry to Demis Hassabis, the cofounder and CEO of Google DeepMind, and John M. Jumper, a director at the same company, for their work on using artificial intelligence to predict the structures of proteins. The other half goes to David Baker, a professor of biochemistry at the University of Washington, for his work on computational protein design. The winners will share a prize pot of 11 million Swedish kronor ($1 million). The potential impact of this research is enormous. Proteins are fundamental to life, but understanding what they do involves figuring out their structure -- a very hard puzzle that once took months or years to crack for each type of protein. By cutting down the time it takes to predict a protein's structure, computational tools such as those developed by this year's award winners are helping scientists gain a greater understanding of how proteins work and opening up new avenues of research and drug development. The technology could unlock more efficient vaccines, speed up research on cures for cancer, or lead to completely new materials.

Google Deepmind founder shares Nobel prize in chemistry for AI that unlocks shape of proteins

Nottingham Trent University provides funding as a member of The Conversation UK. The 2024 Nobel prize in chemistry has been awarded to three scientists for their work on describing and predicting proteins with the help of computers. One half of the prize goes to David Baker from the University of Washington in the US "for computational protein design", with the other half jointly awarded to Demis Hassabis and John M. Jumper, both from Google Deepmind, UK, "for protein structure prediction". Using computers to carry out protein design and for predicting protein structures are two sides of the same coin. They are separately very powerful - and combined, even more so. Proteins are the building blocks of life, building and powering our muscles and organs. Proteins are molecular machines: they read and copy our DNA to make new cells, and pump ions (electrically charged atoms or groups of atoms) into and out of our cells, so these always have what they need to work properly. Proteins act as sensors, detecting what's in their environment. They also activate our immune systems. The molecular building blocks of proteins are amino acids. These connect, one end to another, like letters joining to form a word. Exactly like a word, scientists give a letter to each amino acid, and these can spell out any given protein. Just having that protein sequence - the "word" - isn't enough, though. It's the three-dimensional shape of the protein that determines how it works. So, if we want to make a protein for some purpose, we need a way to determine what its three-dimensional shape will be from the amino acid sequence alone. This is protein structure prediction. Some proteins can be prepared in such a way that their structure can be determined by X-ray, but most cannot. This is why computational structure prediction is vitally important. It is still an extraordinarily difficult problem. Even a small protein, of around 100 "letters" or amino acids, has an impossibly high number of possible ways it can be arranged in three dimensions. To visualise this, imagine arranging strands of cooked spaghetti in a bowl. Read more: Nobel Prize in physics spotlights key breakthroughs in AI revolution - making machines that learn For this reason, until the last decade, computational structure prediction had very low accuracy - less than 50%, in fact. Then, in 2020, Hassabis and Jumper developed an AI tool called AlphaFold2. This can predict the three-dimensional structure of a protein, using only the sequence of letters, with over 90% accuracy. To make such a leap in accuracy, AlphaFold2 uses deep learning and neural networks. Deep learning is a computer-based approach that simulates the way the human brain makes decisions. Neural networks mimic the human brain's structure and function to process data. AlphaFold2 also makes use of massive databases of known protein structures and sequences. The neural network correlates the known three-dimensional shapes with the amino acid sequence. It can then derive rules for what shape a given sequence - the "letters" - will adopt. The opposite problem, computational protein design, can be summed up by the following question: "I want a protein with this three-dimensional shape; what is the sequence that gives me that shape?" This challenge was actually solved first. In 2003, Baker wrote a computer program called Rosetta that begins with the desired three-dimensional structure, and produces the amino acid sequence that will give that structure. It uses the idea that the three-dimensional structure of the entire protein can be built from the structures of small fragments. Read more: AI system can predict the structures of life's molecules with stunning accuracy - helping to solve one of biology's biggest problems Applying the science Computational protein design has many applications. Proteins have been designed to bind and inactivate viruses, to detect drugs like fentanyl, and even to degrade plastic in the environment. So, why has this prize been awarded for these advances now? Protein design and prediction are both inherently complex problems. There is no way to shortcut the large number of possible structures. But the rapid rise in the capabilities and use of artificial intelligence methods has given us a way to address this complexity. AI can efficiently derive correlations from millions of protein structures. The pace of development in AI approaches is highlighted by this year's Nobel prize in physics, which was awarded for the development of neural networks. The twin methods of computational protein design and computational protein structure prediction are now real tools, used by millions of scientists worldwide. Proteins to counter pandemic viruses can now be designed in a matter of weeks. It therefore wouldn't be surprising if we see many other Nobels in future being awarded for breakthroughs that use the power of artificial intelligence.

Google Deepmind founder shares Nobel prize in chemistry for AI that unlocks the shape of proteins

Nottingham Trent University provides funding as a member of The Conversation UK. The 2024 Nobel prize in chemistry has been awarded to three scientists for their work on describing and predicting proteins with the help of computers. One half of the prize goes to David Baker from the University of Washington in the US "for computational protein design", with the other half jointly awarded to Demis Hassabis and John M. Jumper, both from Google Deepmind, UK, "for protein structure prediction". Using computers to carry out protein design and for predicting protein structures are two sides of the same coin. They are separately very powerful - and combined, even more so. Proteins are the building blocks of life, building and powering our muscles and organs. Proteins are molecular machines: they read and copy our DNA to make new cells, and pump ions (electrically charged atoms or groups of atoms) into and out of our cells, so these always have what they need to work properly. Proteins act as sensors, detecting what's in their environment. They also activate our immune systems. The molecular building blocks of proteins are amino acids. These connect, one end to another, like letters joining to form a word. Exactly like a word, scientists give a letter to each amino acid, and these can spell out any given protein. Just having that protein sequence - the "word" - isn't enough, though. It's the three-dimensional shape of the protein that determines how it works. So, if we want to make a protein for some purpose, we need a way to determine what its three-dimensional shape will be from the amino acid sequence alone. This is protein structure prediction. Some proteins can be prepared in such a way that their structure can be determined by X-ray, but most cannot. This is why computational structure prediction is vitally important. It is still an extraordinarily difficult problem. Even a small protein, of around 100 "letters" or amino acids, has an impossibly high number of possible ways it can be arranged in three dimensions. To visualise this, imagine arranging strands of cooked spaghetti in a bowl. Read more: Nobel Prize in physics spotlights key breakthroughs in AI revolution - making machines that learn For this reason, until the last decade, computational structure prediction had very low accuracy - less than 50%, in fact. Then, in 2020, Hassabis and Jumper developed an AI tool called AlphaFold2. This can predict the three-dimensional structure of a protein, using only the sequence of letters, with over 90% accuracy. To make such a leap in accuracy, AlphaFold2 uses deep learning and neural networks. Deep learning is a computer-based approach that simulates the way the human brain makes decisions. Neural networks mimic the human brain's structure and function to process data. AlphaFold2 also makes use of massive databases of known protein structures and sequences. The neural network correlates the known three-dimensional shapes with the amino acid sequence. It can then derive rules for what shape a given sequence - the "letters" - will adopt. The opposite problem, computational protein design, can be summed up by the following question: "I want a protein with this three-dimensional shape; what is the sequence that gives me that shape?" This challenge was actually solved first. In 2003, Baker wrote a computer program called Rosetta that begins with the desired three-dimensional structure, and produces the amino acid sequence that will give that structure. It uses the idea that the three-dimensional structure of the entire protein can be built from the structures of small fragments. Read more: AI system can predict the structures of life's molecules with stunning accuracy - helping to solve one of biology's biggest problems Applying the science Computational protein design has many applications. Proteins have been designed to bind and inactivate viruses, to detect drugs like fentanyl, and even to degrade plastic in the environment. So, why has this prize been awarded for these advances now? Protein design and prediction are both inherently complex problems. There is no way to shortcut the large number of possible structures. But the rapid rise in the capabilities and use of artificial intelligence methods has given us a way to address this complexity. AI can efficiently derive correlations from millions of protein structures. The pace of development in AI approaches is highlighted by this year's Nobel prize in physics, which was awarded for the development of neural networks. The twin methods of computational protein design and computational protein structure prediction are now real tools, used by millions of scientists worldwide. Proteins to counter pandemic viruses can now be designed in a matter of weeks. It therefore wouldn't be surprising if we see many other Nobels in future being awarded for breakthroughs that use the power of artificial intelligence.

I was a beta tester for the Nobel prize-winning AlphaFold AI -- it's going to revolutionize health research

The deep learning machine AlphaFold, which was created by Google's AI research lab DeepMind, is already transforming our understanding of the molecular biology that underpins health and disease. One half of the 2024 Nobel prize in chemistry went to David Baker from the University of Washington in the US, with the other half jointly awarded to Demis Hassabis and John M. Jumper, both from London-based Google DeepMind. If you haven't heard of AlphaFold, it may be difficult to appreciate how important it is becoming to researchers. But as a beta tester for the software, I got to see first-hand how this technology can reveal the molecular structures of different proteins in minutes. It would take researchers months or even years to unpick these structures in laboratory experiments. This technology could pave the way for revolutionary new treatments and drugs. But first, it's important to understand what AlphaFold does. Proteins are produced by series of molecular "beads," created from a selection of the human body's 20 different amino acids. These beads form a long chain that folds up into a mechanical shape that is crucial for the protein's function. Their sequence is determined by DNA. And while DNA research means we know the order of the beads that build most proteins, it's always been a challenge to predict how the chain folds up into each "3D machine." These protein structures underpin all of biology. Scientists study them in the same way you might take a clock apart to understand how it works. Comprehend the parts and put together the whole: it's the same with the human body. Proteins are tiny, with a huge number located inside each of our 30 trillion cells. This meant for decades, the only way to find out their shape was through laborious experimental methods -- studies that could take years. Throughout my career I, along with many other scientists, have been engaged in such pursuits. Every time we solve a protein structure, we deposit it in a global database called the Protein Data Bank, which is free for anyone to use. AlphaFold was trained on these structures, the majority of which were found using X-ray crystallography. For this technique, proteins are tested under thousands of different chemical states, with variations in temperature, density and pH. Researchers use a microscope to identify the conditions under which each protein lines up in a particular formation. These are then shot with X-rays to work out the spatial arrangement of all the atoms in that protein. Having been trained on these structures, AlphaFold can now predict protein structure at speeds that were previously impossible. I started out early in my career, from the late '90s, working out protein structures using magnetic properties of their nuclei. I did this with technology called nuclear magnetic resonance (NMR) spectroscopy, which uses a huge magnet like an MRI scanner. This method had begun to fall out of favor because of certain technical limitations, but is now having a resurgence thanks to AlphaFold. NMR is one of the few techniques that can probe molecules in motion, instead of keeping them still inside a crystal or on an electron microscope grid. Addictive experience In March 2024, researchers at DeepMind approached me to beta test AlphaFold3, the latest incarnation of the software, which was close to release at the time. I've never been a gamer but I got a taste of the addictive experience as, once I got access, all I wanted to do was spend hours trying out molecular combinations. As well as lightning speed, this new version introduced the option to include bigger and more varied molecules, including DNA and metals, and the opportunity to modify amino acids to mimic chemical signaling in cells. Our lab at King's College London used X-ray crystallography to predict a structure formed by two bacterial proteins that are loosely involved in hospital superbugs when they interact. Previous incarnations of AlphaFold predicted the individual components but could never get the complex right -- yet this new version solved it at the first attempt. Understanding the moving parts and dynamics of proteins is the next frontier, now that we can predict static protein shapes with AlphaFold. Proteins come in a huge variety of shapes and sizes. They can be rigid or flexible, or made of neatly structured units connected by bendy loops. Dynamics are essential for protein function. As another Nobel laureate, Richard Feynman, said: "Everything that living things do can be understood in terms of the jiggling and wiggling of atoms." Another great feature of magnetic resonance techniques is that they can measure precise distances between atoms. So, with a few carefully designed experiments, the AlphaFold outputs can be verified in a lab. In other cases, the results are still ambiguous. It's a work in progress between experimental structural biologists, like my team, and computational scientists. The recognition that comes with a Nobel prize will only galvanize the quest to understand all molecular machinery -- and hopefully, change the game when it comes to medicines, vaccines and human health.

Machine learning cracked the protein-folding problem and won the 2024 Nobel Prize in chemistry

The 2024 Nobel Prize in chemistry recognized Demis Hassabis, John Jumper and David Baker for using machine learning to tackle one of biology's biggest challenges: predicting the 3D shape of proteins and designing them from scratch. This year's award stood out because it honored research that originated at a tech company: DeepMind, an AI research startup that was acquired by Google in 2014. Most previous chemistry Nobel Prizes have gone to researchers in academia. Many laureates went on to form startup companies to further expand and commercialize their groundbreaking work -- for instance, CRISPR gene-editing technology and quantum dots -- but the research, from start to end, wasn't done in the commercial sphere. Although the Nobel Prizes in physics and chemistry are awarded separately, there is a fascinating connection between the winning research in those fields in 2024. The physics award went to two computer scientists who laid the foundations for machine learning, while the chemistry laureates were rewarded for their use of machine learning to tackle one of biology's biggest mysteries: how proteins fold. The 2024 Nobel Prizes underscore both the importance of this kind of artificial intelligence and how science today often crosses traditional boundaries, blending different fields to achieve groundbreaking results. Proteins are the molecular machines of life. They make up a significant portion of our bodies, including muscles, enzymes, hormones, blood, hair and cartilage. Understanding proteins' structures is essential because their shapes determine their functions. Back in 1972, Christian Anfinsen won the Nobel Prize in chemistry for showing that the sequence of a protein's amino acid building blocks dictates the protein's shape, which, in turn, influences its function. If a protein folds incorrectly, it may not work properly and could lead to diseases such as Alzheimer's, cystic fibrosis or diabetes. A protein's overall shape depends on the tiny interactions, the attractions and repulsions, between all the atoms in the amino acids it's made of. Some want to be together, some don't. The protein twists and folds itself into a final shape based on many thousands of these chemical interactions. For decades, one of biology's greatest challenges was predicting a protein's shape based solely on its amino acid sequence. Although researchers can now predict the shape, we still don't understand how the proteins maneuver into their specific shapes and minimize the repulsions of all the interatomic interactions in a few microseconds. To understand how proteins work and to prevent misfolding, scientists needed a way to predict the way proteins fold, but solving this puzzle was no easy task. In 2003, University of Washington biochemist David Baker wrote Rosetta, a computer program for designing proteins. With it he showed it was possible to reverse the protein-folding problem by designing a protein shape and then predicting the amino acid sequence needed to create it. It was a phenomenal jump forward, but the shape chosen for the calculation was simple, and the calculations were complex. A major paradigm shift was required to routinely design novel proteins with desired structures. Machine learning is a type of AI where computers learn to solve problems by analyzing vast amounts of data. It's been used in various fields, from game-playing and speech recognition to autonomous vehicles and scientific research. The idea behind machine learning is to use hidden patterns in data to answer complex questions. This approach made a huge leap in 2010 when Demis Hassabis co-founded DeepMind, a company aiming to combine neuroscience with AI to solve real-world problems. Hassabis, a chess prodigy at age 4, quickly made headlines with AlphaZero, an AI that taught itself to play chess at a superhuman level. In 2017, AlphaZero thoroughly beat the world's top computer chess program, Stockfish-8. The AI's ability to learn from its own gameplay, rather than relying on preprogrammed strategies, marked a turning point in the AI world. Soon after, DeepMind applied similar techniques to Go, an ancient board game known for its immense complexity. In 2016, its AI program AlphaGo defeated one of the world's top players, Lee Sedol, in a widely watched match that stunned millions. In 2016, Hassabis shifted DeepMind's focus to a new challenge: the protein-folding problem. Under the leadership of John Jumper, a chemist with a background in protein science, the AlphaFold project began. The team used a large database of experimentally determined protein structures to train the AI, which allowed it to learn the principles of protein folding. The result was AlphaFold2, an AI that could predict the 3D structure of proteins from their amino acid sequences with remarkable accuracy. This was a significant scientific breakthrough. AlphaFold has since predicted the structures of over 200 million proteins -- essentially all the proteins that scientists have sequenced to date. This massive database of protein structures is now freely available, accelerating research in biology, medicine and drug development. Understanding how proteins fold and function is crucial for designing new drugs. Enzymes, a type of protein, act as catalysts in biochemical reactions and can speed up or regulate these processes. To treat diseases such as cancer or diabetes, researchers often target specific enzymes involved in disease pathways. By predicting the shape of a protein, scientists can figure out where small molecules -- potential drug candidates -- might bind to it, which is the first step in designing new medicines. In 2024, DeepMind launched AlphaFold3, an upgraded version of the AlphaFold program that not only predicts protein shapes but also identifies potential binding sites for small molecules. This advance makes it easier for researchers to design drugs that precisely target the right proteins. Google bought Deepmind for reportedly around half a billion dollars in 2014. Google DeepMind has now started a new venture, Isomorphic Labs, to collaborate with pharmaceutical companies on real-world drug development using these AlphaFold3 predictions. For his part, David Baker has continued to make significant contributions to protein science. His team at the University of Washington developed an AI-based method called "family-wide hallucination," which they used to design entirely new proteins from scratch. Hallucinations are new patterns -- in this case, proteins -- that are plausible, meaning they are a good fit with patterns in the AI's training data. These new proteins included a light-emitting enzyme, demonstrating that machine learning can help create novel synthetic proteins. These AI tools offer new ways to design functional enzymes and other proteins that never could have evolved naturally. The Nobel-worthy achievements of Hassabis, Jumper and Baker show that machine learning isn't just a tool for computer scientists -- it's now an essential part of the future of biology and medicine. By tackling one of the toughest problems in biology, the winners of the 2024 prize have opened up new possibilities in drug discovery, personalized medicine and even our understanding of the chemistry of life itself.

Machine learning cracked the protein-folding problem and won the 2024 Nobel Prize in chemistry

Marc Zimmer does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment. The 2024 Nobel Prize in chemistry recognized Demis Hassabis, John Jumper and David Baker for using machine learning to tackle one of biology's biggest challenges: predicting the 3D shape of proteins and designing them from scratch. This year's award stood out because it honored research that originated at a tech company: DeepMind, an AI research startup that was acquired by Google in 2014. Most previous chemistry Nobel Prizes have gone to researchers in academia. Many laureates went on to form startup companies to further expand and commercialize their groundbreaking work - for instance, CRISPR gene-editing technology and quantum dots - but the research, from start to end, wasn't done in the commercial sphere. Although the Nobel Prizes in physics and chemistry are awarded separately, there is a fascinating connection between the winning research in those fields in 2024. The physics award went to two computer scientists who laid the foundations for machine learning, while the chemistry laureates were rewarded for their use of machine learning to tackle one of biology's biggest mysteries: how proteins fold. The 2024 Nobel Prizes underscore both the importance of this kind of artificial intelligence and how science today often crosses traditional boundaries, blending different fields to achieve groundbreaking results. The challenge of protein folding Proteins are the molecular machines of life. They make up a significant portion of our bodies, including muscles, enzymes, hormones, blood, hair and cartilage. Understanding proteins' structures is essential because their shapes determine their functions. Back in 1972, Christian Anfinsen won the Nobel Prize in chemistry for showing that the sequence of a protein's amino acid building blocks dictates the protein's shape, which, in turn, influences its function. If a protein folds incorrectly, it may not work properly and could lead to diseases such as Alzheimer's, cystic fibrosis or diabetes. A protein's overall shape depends on the tiny interactions, the attractions and repulsions, between all the atoms in the amino acids its made of. Some want to be together, some don't. The protein twists and folds itself into a final shape based on many thousands of these chemical interactions. For decades, one of biology's greatest challenges was predicting a protein's shape based solely on its amino acid sequence. Although researchers can now predict the shape, we still don't understand how the proteins maneuver into their specific shapes and minimize the repulsions of all the interatomic interactions in a few microseconds. To understand how proteins work and to prevent misfolding, scientists needed a way to predict the way proteins fold, but solving this puzzle was no easy task. In 2003, University of Washington biochemist David Baker wrote Rosetta, a computer program for designing proteins. With it he showed it was possible to reverse the protein-folding problem by designing a protein shape and then predicting the amino acid sequence needed to create it. It was a phenomenal jump forward, but the shape chosen for the calculation was simple, and the calculations were complex. A major paradigm shift was required to routinely design novel proteins with desired structures. A new era of machine learning Machine learning is a type of AI where computers learn to solve problems by analyzing vast amounts of data. It's been used in various fields, from game-playing and speech recognition to autonomous vehicles and scientific research. The idea behind machine learning is to use hidden patterns in data to answer complex questions. This approach made a huge leap in 2010 when Demis Hassabis co-founded DeepMind, a company aiming to combine neuroscience with AI to solve real-world problems. Hassabis, a chess prodigy at age 4, quickly made headlines with AlphaZero, an AI that taught itself to play chess at a superhuman level. In 2017, AlphaZero thoroughly beat the world's top computer chess program, Stockfish-8. The AI's ability to learn from its own gameplay, rather than relying on preprogrammed strategies, marked a turning point in the AI world. Soon after, DeepMind applied similar techniques to Go, an ancient board game known for its immense complexity. In 2016, its AI program AlphaGo defeated one of the world's top players, Lee Sedol, in a widely watched match that stunned millions. In 2016, Hassabis shifted DeepMind's focus to a new challenge: the protein-folding problem. Under the leadership of John Jumper, a chemist with a background in protein science, the AlphaFold project began. The team used a large database of experimentally determined protein structures to train the AI, which allowed it to learn the principles of protein folding. The result was AlphaFold2, an AI that could predict the 3D structure of proteins from their amino acid sequences with remarkable accuracy. This was a significant scientific breakthrough. AlphaFold has since predicted the structures of over 200 million proteins - essentially all the proteins that scientists have sequenced to date. This massive database of protein structures is now freely available, accelerating research in biology, medicine and drug development. Designer proteins to fight disease Understanding how proteins fold and function is crucial for designing new drugs. Enzymes, a type of protein, act as catalysts in biochemical reactions and can speed up or regulate these processes. To treat diseases such as cancer or diabetes, researchers often target specific enzymes involved in disease pathways. By predicting the shape of a protein, scientists can figure out where small molecules - potential drug candidates - might bind to it, which is the first step in designing new medicines. In 2024, DeepMind launched AlphaFold3, an upgraded version of the AlphaFold program that not only predicts protein shapes but also identifies potential binding sites for small molecules. This advance makes it easier for researchers to design drugs that precisely target the right proteins. Google bought Deepmind for reportedly around half a billion dollars in 2014. Google DeepMind has now started a new venture, Isomorphic Labs, to collaborate with pharmaceutical companies on real-world drug development using these AlphaFold3 predictions. For his part, David Baker has continued to make significant contributions to protein science. His team at the University of Washington developed an AI-based method called "family-wide hallucination," which they used to design entirely new proteins from scratch. Hallucinations are new patterns - in this case, proteins - that are plausible, meaning they are a good fit with patterns in the AI's training data. These new proteins included a light-emitting enzyme, demonstrating that machine learning can help create novel synthetic proteins. These AI tools offer new ways to design functional enzymes and other proteins that never could have evolved naturally. AI will enable research's next chapter The Nobel-worthy achievements of Hassabis, Jumper and Baker show that machine learning isn't just a tool for computer scientists - it's now an essential part of the future of biology and medicine. By tackling one of the toughest problems in biology, the winners of the 2024 prize have opened up new possibilities in drug discovery, personalized medicine and even our understanding of the chemistry of life itself.

I was a beta tester for the Nobel prize-winning AlphaFold AI - it's going to revolutionise health research

King's College London provides funding as a member of The Conversation UK. The deep learning machine AlphaFold, which was created by Google's AI research lab DeepMind, is already transforming our understanding of the molecular biology that underpins health and disease. One half of the 2024 Nobel prize in chemistry went to David Baker from the University of Washington in the US, with the other half jointly awarded to Demis Hassabis and John M. Jumper, both from London-based Google DeepMind. If you haven't heard of AlphaFold, it may be difficult to appreciate how important it is becoming to researchers. But as a beta tester for the software, I got to see first-hand how this technology can reveal the molecular structures of different proteins in minutes. It would take researchers months or even years to unpick these structures in laboratory experiments. Read more: Google Deepmind founder shares Nobel prize in chemistry for AI that unlocks the shape of proteins This technology could pave the way for revolutionary new treatments and drugs. But first, it's important to understand what AlphaFold does. Proteins are produced by series of molecular "beads", created from a selection of the human body's 20 different amino acids. These beads form a long chain that folds up into a mechanical shape that is crucial for the protein's function. Their sequence is determined by DNA. And while DNA research means we know the order of the beads that build most proteins, it's always been a challenge to predict how the chain folds up into each "3D machine". These protein structures underpin all of biology. Scientists study them in the same way you might take a clock apart to understand how it works. Comprehend the parts and put together the whole: it's the same with the human body. Proteins are tiny, with a huge number located inside each of our 30 trillion cells. This meant for decades, the only way to find out their shape was through laborious experimental methods - studies that could take years. Throughout my career I, along with many other scientists, have been engaged in such pursuits. Every time we solve a protein structure, we deposit it in a global database called the Protein Data Bank, which is free for anyone to use. AlphaFold was trained on these structures, the majority of which were found using X-ray crystallography. For this technique, proteins are tested under thousands of different chemical states, with variations in temperature, density and pH. Researchers use a microscope to identify the conditions under which each protein lines up in a particular formation. These are then shot with X-rays to work out the spatial arrangement of all the atoms in that protein. Having been trained on these structures, AlphaFold can now predict protein structure at speeds that were previously impossible. I started out early in my career, from the late 90s, working out protein structures using magnetic properties of their nuclei. I did this with technology called nuclear magnetic resonance (NMR) spectroscopy, which uses a huge magnet like an MRI scanner. This method had begun to fall out of favour because of certain technical limitations, but is now having a resurgence thanks to AlphaFold. NMR is one of the few techniques that can probe molecules in motion, instead of keeping them still inside a crystal or on an electron microscope grid. Addictive experience In March 2024, researchers at DeepMind approached me to beta test AlphaFold3, the latest incarnation of the software, which was close to release at the time. I've never been a gamer but I got a taste of the addictive experience as, once I got access, all I wanted to do was spend hours trying out molecular combinations. As well as lightning speed, this new version introduced the option to include bigger and more varied molecules, including DNA and metals, and the opportunity to modify amino acids to mimic chemical signalling in cells. Our lab at King's College London used X-ray crystallography to predict a structure formed by two bacterial proteins that are loosely involved in hospital superbugs when they interact. Previous incarnations of AlphaFold predicted the individual components but could never get the complex right - yet this new version solved it at the first attempt. Understanding the moving parts and dynamics of proteins is the next frontier, now that we can predict static protein shapes with AlphaFold. Proteins come in a huge variety of shapes and sizes. They can be rigid or flexible, or made of neatly structured units connected by bendy loops. Dynamics are essential for protein function. As another Nobel laureate, Richard Feynman, said: "Everything that living things do can be understood in terms of the jiggling and wiggling of atoms." Another great feature of magnetic resonance techniques is they can measure precise distances between atoms. So, with a few carefully designed experiments, the AlphaFold outputs can be verified in a lab. In other cases, the results are still ambiguous. It's a work in progress between experimental structural biologists, like my team, and computational scientists. The recognition that comes with a Nobel prize will only galvanise the quest to understand all molecular machinery - and hopefully, change the game when it comes to medicines, vaccines and human health.

Nobel-winning AI: DeepMind duo's breakthrough in the field of chemistry

Google DeepMind's Demis Hassabis and John Jumper just snagged this year's Nobel Prize in Chemistry, sharing the honor with David Bakker from the University of Washington. The trio's work on computational protein design has changed the game, with Hassabis and Jumper's AlphaFold2 AI model leading the charge. "This prize represents the promise of computational biology," Jumper said at a press conference, where excitement was high. AlphaFold2, their breakthrough tool, has cracked a 50-year-old problem in biology: predicting protein structures. It can now predict over 200 million of these structures -- essentially covering almost every protein known to science -- from their amino acid sequences. The implications are huge. By offering a clearer understanding of how proteins operate and interact with other molecules, AlphaFold2 is giving researchers fresh insight into diseases and drug discovery. And it's not just a lab tool -- it's accessible to scientists worldwide. Since going live, AlphaFold2 has already been adopted by over 2 million scientists in 190 countries. It's speeding up research on everything from malaria vaccines to Parkinson's therapies, and even solutions for drug-resistant bacteria. As Jumper put it, the model's impact has been immediate. "What I think will come soon through our work is that we're going to get better and better at harnessing biology and our understanding of biology to make medicines," he said. "I hope this means that, ultimately, we will be more responsive to, for example, pandemics." Hassabis, a pioneer in AI and co-founder of DeepMind, is clear about his mission: using artificial intelligence to "improve the lives of billions of people." But he also cautioned against getting swept away by the tech's rapid advancements. "We have to really think very hard as these systems and techniques get more powerful about how to enable and empower all of the benefits and good use cases, whilst mitigating the risks," he noted. AI pioneers John Hopfield and Geoffrey Hinton won Nobel Physics Prize 2024 AI's fingerprints are all over the sciences. Not just in chemistry -- this year's Nobel Prize in Physics also went to two AI researchers, John Hopfield and Geoffrey Hinton, for their pioneering work on training neural networks with physics. The future of scientific

Google DeepMind Scientists Win Nobel Prize for AlphaFold AI Project

Demis Hannabis and John Jumper are honored for their work using AI to predict the structure of proteins. Just when they believed they were passed over for this year's Nobel Prize in chemistry, two scientists from Google's DeepMind AI research team got the call -- mere minutes before they were announced as honorees. Demis Hassabis, the CEO of Google's DeepMind, and John Jumper, the project's American director, shared the prize for their work on AlphaFold2, an AI model that can predict protein structures. The two were co-honorees with David Baker, a University of Washington scientist who has been using amino acids and computational power to create new kinds of proteins. Hassabis and Jumper both said they received word from the Swedish prize organization just before the news went out; emergency phone calls and texts ultimately reached Hassabis' wife and another member of the DeepMind team. "We got the call very late. We were assuming it wasn't happening," Hassabis said in a press conference held by Google after the Wednesday announcement. "I tried to sleep in," Jumper added. "I couldn't sleep last night." The AlphaFold project was first presented in 2020 and has since predicted the structure of 200 million proteins identified by researchers. AlphaFold2, for which Hassabis and Jumper won the award, has been used by more than 2 million people in 190 countries. In the press conference, the two said that a version in the works, AlphaFold3, will be released to the scientific community for free. This year's Nobel Prize for Physics, awarded one day earlier, also recognized pioneering work in AI, which revealed "a completely new way for us to use computers." Geoffrey Hinton, of the University of Toronto, and John Hopfield, of Princeton University, shared the prize for using physics to train neural networks -- systems inspired by the workings of the human brain -- and thus enabling the machine learning that drives much of what artificial intelligence can accomplish. Hinton, known as the "godfather of AI," worked for a time at Google, but left in 2023 citing concerns about the risks that artificial intelligence poses. On Tuesday, he noted both the positive implications, such as advances in health care, and the negatives and the sheer unknowables as AI rapidly evolves. "We have no experience of what it's like to have things smarter than us," he said, as reported by The New York Times. The Nobel committee called AlphaFold2 a "stunning breakthrough." In the press conference, Hassabis and Jumper acknowledged that their work is only the beginning of AI-assisted technology that could speed up the development of medical treatments from years to months and that will help researchers understand what Hassabis called "fundamental mechanisms in biology." "I kind of see AI as potentially the ultimate tool for accelerating science and scientific knowledge," Hassabis said. Hassabis and Jumper will split the prize of 11 million Swedish kronor (about $1.06 million) with Baker. The two credited the team at Google and many other scientists who created the foundational work that their research built upon. "It's humbling," Jumper said. "Every time we train AI, every data point is years of effort from someone training to be a Ph.D. student or someone who's already gotten their Ph.D. ... Every day it's wonderful to see the work that the scientific community has done on top of AlphaFold and I can't wait to see the next breakthroughs." While AI was a significant part of AlphaFold, instrumental in identifying patterns that humans wouldn't be able to find, Hassabis pointed out that a lot of human work went into the project. "It wasn't just 'AI did this,'" he said. "It was an iterative process. We developed, we did research, we tried to find the right combinations between what the community understood about proteins and how we build those intuitions into our architecture." "AI was the toolbox in which we got to this incredible work," Hassabis said

Google DeepMind's Demis Hassabis & John Jumper awarded Nobel Prize in Chemistry

This morning, Co-founder and CEO of Google DeepMind and Isomorphic Labs Sir Demis Hassabis, and Google DeepMind Director Dr. John Jumper were co-awarded the 2024 Nobel Prize in Chemistry for their work developing AlphaFold, a groundbreaking AI system that predicts the 3D structure of proteins from their amino acid sequences. David Baker was also co-awarded for his work on computational protein design. Before AlphaFold, predicting the structure of a protein was a complex and time-consuming process. AlphaFold's predictions, made freely available through the AlphaFold Protein Structure Database, have given more than 2 million scientists and researchers from 190 countries a powerful tool for making new discoveries. The AlphaFold 2 paper, published in 2021, remains one of the most-cited publications of all time. AlphaFold's contributions to science have been widely praised, and among its recognitions are the 2023 Albert Lasker Basic Medical Research Award, the 2023 Breakthrough Prize in Life Sciences, the 2023 Canada Gairdner International Award, the 2024 Clarivate Citation Laureate award, and the 2024 Keio Medical Science Prize Award. Artificial Intelligence (AI) has long shown incredible potential for use in scientific research, and AlphaFold was proof-of-concept. As more scientists adopt AI for use in everything from building data, to simulating experiments, drug design, modelling complexity, discovering novel solutions for extant problems, and building upon existing knowledge, we will continue to see foundational scientific breakthroughs in the years ahead.

Why Demis Hassabis Truly Deserved the Nobel Prize

Does AlphaFold's monumental achievement justify Nobel recognition and set a precedent for AI-powered breakthroughs in global scientific accolades? "Winning the Nobel Prize is the honour of a lifetime and the realisation of a lifelong dream - it still hasn't really sunk in yet," said the co-founder of DeepMind, Demis Hassabis, who, along with his team, cracked the 50-year-old challenge of protein structure prediction with AlphaFold 2. "With AlphaFold 2 we cracked the grand challenge of protein structure prediction - predicting the 3D structure of a protein purely from its amino acid sequence," said Hassabis. He added that the open-access database of over 200 million protein structures has empowered more than 2 million researchers, advancing critical work in enzyme design, disease understanding, and drug discovery. As AI continues to shape the future of therapies and scientific discovery, with teams like Isomorphic Labs driving AI-led innovations, the question arises -- does AlphaFold's monumental achievement justify its Nobel recognition, and does it set a precedent for AI-powered breakthroughs in global scientific accolades? According to Heiner Linke, Chair of the Nobel Committee for Chemistry, "One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream of predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities." In December 2020, when AlphaFold 2's success in predicting protein structures was announced, there was no doubt that this breakthrough had Nobel Prize potential. At the time, experts recognised the colossal impact of solving a decades-old challenge in protein science. Following this, the 2023 Breakthrough Prize was awarded to Demis Hassabis, John Jumper, and the DeepMind team, finally leading to the Nobel Prize in 2024. It goes without saying that the Nobel Committee recognised that such a discovery not only solved age-old scientific challenges but also made the borders of AI and natural sciences porous. It redefines what's possible in biology and chemistry through the intelligent use of AI, enabling research and innovation across multiple fields. Founded by Hassabis in 2021, Isomorphic Labs -- a sister company of Google DeepMind -- is looking to revolutionise drug discovery with AI, potentially building a multi-$100 billion business by accelerating research and improving clinical trial success. "AI-designed drugs would probably be available in the next couple of years," he said. "I hope to achieve both (commercial success and societal benefits) with Isomorphic and build a multi-100 billion dollar business. I think it has that potential," said Hassabis without delving into the specific timeline. In May 2024, Google DeepMind released AlphaFold 3, a game changing protein folding model that predicts with 50% better accuracy. "Well, if you ask me the number one thing AI can do for humanity, it will be to solve hundreds of terrible diseases. I can't imagine a better use case for AI. So that's partly the motivation behind Isomorphic and AlphaFold and all the work we do in sciences," said Hassabis. He believes that "revolutionising the drug discovery process to make it ten times faster" and more efficient and increasing the likelihood of passing clinical trials through better property prediction offers plenty of commercial value. Last month, Google DeepMind, in collaboration with Isomorphic Labs, predicted over 200 million protein structures using AlphaFold. It achieved this by training the model with nearly 100,000 known proteins, driving significant breakthroughs in drug discovery by targeting previously intractable biomedical challenges. The model can predict the 3D structure of proteins with incredible accuracy, aiming to design drugs that target specific proteins, unlocking treatments for diseases that were previously untreatable. Recently, Google DeepMind also launched AlphaProteo, an AI system that generates novel proteins designed to bind to specific target molecules poised to significantly advance research in drug design, disease understanding and other health applications. DeepMind is not the only active player in the market, ESMFold, Meta's protein-folding model, has also predicted about 772 million protein structures. This is only the beginning.

Google's DeepMind Nobel Prize showcases AI's medical potential

The recent awarding of the Nobel Prize in chemistry is an incredible vote of confidence in the potential for artificial intelligence to transform the way medicines are invented by using AI to illuminate and manipulate proteins, life's most basic building blocks. The Royal Swedish Academy of Sciences honored University of Washington professor David Baker and two scientists from Google DeepMind, CEO Demis Hassabis and senior research scientist John Jumper. Hassabis and Jumper were recognized for winning a decadeslong race to use computers to predict a protein's structure based on only on its genetic code. Baker's prize nods to his use of computers to invent never-before-seen proteins.

Google DeepMind duo share Nobel chemistry prize with US biochemist

US biochemist David Baker and Google DeepMind scientists Sir Demis Hassabis and John Jumper have won the chemistry Nobel Prize jointly for their work to unlock the biological secrets of proteins that underpin life and health. Baker took one half of the SKr11mn ($1.06mn) award for his research on computational protein design and the DeepMind duo received the other for protein structure prediction, the Nobel Assembly in Stockholm said on Wednesday. The prize recognises big advances in techniques to understand how proteins function and interact to make living cells work. The methods, including DeepMind's artificial intelligence-driven AlphaFold models, have raised hopes they could be powerful tools in the development of new therapies for hard-to-treat diseases. Baker, who is director of the Institute for Protein Design at Washington university, had "succeeded with the almost impossible feat of building entirely new kinds of proteins", the Nobel organisers said. Hassabis and Jumper had "developed an AI model to solve a 50-year-old problem: predicting proteins' complex structures". "Both of these discoveries open up vast possibilities," said Heiner Linke, chair of the Nobel chemistry committee. In a call with the Nobel committee after the announcement, Baker said he was "deeply honoured" and "stood on the shoulders of giants", given the contribution of other researchers. "Our new AI methods are much more powerful than traditional scientific model methods. I'm really excited about all the ways in which protein design can now make the world a better place in health, medicine and . . . in technology and sustainability," he added. Baker has since the early 2000s harnessed computer-powered design to construct novel proteins from the 20 different building blocks, known as amino acids, of which they are mostly comprised. His teams have produced new structures for use in vaccines, nano materials and tiny sensors. In 2022, Hassabis and Jumper's teams used the AlphaFold AI model to build the most complete and accurate database yet of almost every known protein. Covering about 200mn proteins, the breakthrough is expected to reduce significantly the time required to make biological discoveries. Hassabis, a co-founder and chief executive of Google DeepMind, the Silicon Valley giant's AI research arm, described the AlphaFold innovation in March as a more efficient way "to search for the needle in a haystack". "That's what really what a lot of science boils down to . . . if you can capture a problem in that way, then these types of AI systems that we're building now can be very useful." The third iteration of AlphaFold unveiled by DeepMind in May extends beyond proteins to look at other biochemical networks sustaining life in our bodies' cells. AlphaFold 3 covers the DNA and RNA genetic codes as well as ligands, molecules that bind to others and can be important markers of disease. The Nobel committee said there had already been many applications of AlphaFold models, such as vaccine design and mining the protein database for new enzymes that could degrade plastics. DeepMind has spun off a drug discovery arm, known as Isomorphic Labs, to build on AlphaFold's scientific breakthroughs. Hassabis told the Financial Times this year that the goal was to use the model to cut the average discovery stage -- when potential drugs are identified before clinical trials -- from five years to two. "AlphaFold has given researchers the unprecedented ability to predict what proteins "look like" in three-dimensions," said Michael Dennis, chief scientific officer of CAS, a division of the American Chemical Society. "The impact of this technology on understanding disease mechanisms and developing drugs and new therapies is immense." The chemistry prize is the third of the six annual Nobels that are being announced on successive weekdays. The literature award winners will be unveiled on Thursday, followed by peace on Friday and economics on Monday.

Google DeepMind leaders Hassabis and Jumper win Nobel Prize for Chemistry

Google suddenly finds itself with two Nobel laureates on its staff. On Wednesday, the Royal Swedish Academy of Sciences announced that Demis Hassabis and John Jumper are recipients of this year's Nobel Prize in Chemistry. Hassabis is the CEO of Google's DeepMind AI unit, while Jumper is a director there. The pair received the award for their work on the AlphaFold2 AI model, which in 2020 stunned the scientific world with its ability to correctly predict the structure of almost all known proteins from their DNA sequences. Previously, computer software was much less accurate at predicting protein structures, while imaging methods to discover the shape of proteins were time-consuming and costly. This breakthrough was hugely impactful since proteins are the engines behind most biological processes, with their shape determining their function. Among other things, being able to predict the shape of a protein unlocks better understanding of disease and could enable researchers to more quickly develop new drugs. AlphaFold 2 helped solve an almost 50-year grand challenge in biology first raised by Nobel laureate chemist Christian Anfisen, who in 1973 suggested that it should be possible to determine a protein's structure from its DNA sequence alone. "Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries," the Academy said in a statement. "Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic." AlphaFold 2 has also been used to help researchers find potential treatments for neglected tropical diseases, such as Chagas Disease, which afflicts millions of people in less developed countries, as well as to explore the structure of the pores that allow material to pass to and from the nucleus of cells. Hassabis and Jumper share this year's award with David Baker, a scientist at the University of Washington, who pioneered the creation of entirely new proteins two decades ago. Baker gets half the prize money of 11 million Swedish kronor ($1.06 million), while the DeepMinders get a quarter each. "This is a monumental achievement for AI, for computational biology, and science itself," the DeepMind team posted on X. It's clearly a big year for AI at the most prestigious scientific awards. The Nobel Prize for Physics was on Tuesday given to Geoffrey Hinton and John Hopfield for their work in the development of artificial neural networks -- the foundation of today's AI systems, including AlphaFold 2. Hinton was also a leading figure in Google's AI efforts as part of its Brain unit (which developed the transformer deep-learning technology that enables today's generative AI). Shortly after Google merged the Brain unit with DeepMind in April last year, Hinton quit so he could warn the world of AI's risks. Hinton and Jumper's award was controversial among some physicists, who saw the links between their field and AI as tenuous. The framing of Wednesday's chemistry Nobel is far clearer, rewarding the development of a specific AI tool that is rapidly transforming biological research. Since its initial work on AlphaFold 2, DeepMind has continued to make advances in biology. AlphaFold 3, a sucessor model to AlpahFold 2, can predict the structures and interactions of not just proteins, but other molecules found in living things too. Last month, it unveiled AlphaProteo, a system that provide recipes for synthetic proteins that will bind with any given target molecule. This could become a key tool for drug development. It has also debuted AlphaMissense, an AI model that can predict the effect of various mutations in human DNA. Hassabis is the cofounder of DeepMInd, which has as its mission the creation of AGI, or artificial general intelligence, a single AI system that would be as smart as a human. He has been fascinated with the idea of protein folding since his days as a student at the University of Cambridge. Google acquired DeepMind in 2014, but Hassabis remained CEO of the separate unit and he continues to lead the merged Google DeepMind research division. Jumper, who initially trained as a physicist, began working on computation biology at D.E. Shaw Research in New York. He later got a Ph.D. in computational biophysics from the University of Chicago. When DeepMind began working on trying to crack Anfinsen's protein folding problem, it hired Jumper to lead the team working on what became AlphaFold. Jumper continues to lead a group within DeepMind that is researching AI methods related to proteins and other biology questions.

AI wins another Nobel, this time in Chemistry: Google DeepMinders Hassabis and Jumper awarded for AlphaFold

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A trio of scientists consisting of Demis Hassabis, co-founder and CEO of Google's AI division DeepMind, as well as John Jumper, Senior Research Scientist at Google DeepMind and David Baker of the University of Washington have been awarded the 2024 Nobel Prize in Chemistry for their groundbreaking work developing AlphaFold, an AI system capable of predicting the 3D structure of proteins from their amino acid sequences. The award highlights how artificial intelligence is revolutionizing biological science -- and comes just one day after what I believe to be the first Nobel Prize awarded to an AI technology, that one for Physics to fellow Google DeepMinder Geoffrey Hinton and Princeton professor John J. Hopfield, for their work in artificial neural networks. The Royal Swedish Academy of Sciences announced the prize as it did with the Physics one, valued at 11 million Swedish kronor (around $1 million USD), split among the laureates -- half will go to Baker and the other half divided again in fourths of the total to Hassabis and Jumper. The committee emphasized the unprecedented impact of AlphaFold, describing it as a breakthrough that solved a 50-year-old problem in biology: protein structure prediction, or how to predict the three-dimensional structure of a protein from its amino acid sequence. For decades, scientists knew that a protein's function is determined by its 3D shape, but predicting how the string of amino acids folds into that shape was incredibly complex. Researchers had attempted to solve this since the 1970s, but due to the vast number of possible folding configurations (known as Levinthal's paradox), accurate predictions remained elusive. AlphaFold, developed by Google DeepMind, made a breakthrough by using AI to predict the 3D structures of proteins with near-experimental accuracy, meaning that the predictions made by AlphaFold for a protein's 3D structure are so close to the results obtained from traditional experimental methods -- like X-ray crystallography, cryo-electron microscopy, or nuclear magnetic resonance (NMR) spectroscopy -- that they are almost indistinguishable. When AlphaFold achieved "near-experimental accuracy," it was able to predict protein structures with a level of precision that rivaled these methods, typically within an error margin of around 1 Ångström (0.1 nanometers) for most proteins. This means the model's predictions closely matched the actual structures determined by experimental means, making it a transformative tool for biologists. Hassabis and Jumper's work, developed at DeepMind's London laboratory, has transformed the fields of structural biology and drug discovery, offering a powerful tool to scientists worldwide. "AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery," Hassabis said in a statement. "I hope we'll look back on AlphaFold as the first proof point of AI's incredible potential to accelerate scientific discovery." AlphaFold's Global Impact AlphaFold's predictions are freely accessible via the AlphaFold Protein Structure Database, making it one of the most significant open-access scientific tools available. Over two million researchers from 190 countries have used the tool, democratizing access to cutting-edge AI and enabling breakthroughs in fields as varied as molecular biology, drug development, and even climate science. By predicting the 3D structure of proteins in minutes -- tasks that previously took years -- AlphaFold is accelerating scientific progress. The system has been used to tackle antibiotic resistance, design enzymes that degrade plastic, and aid in vaccine development, marking its utility in both healthcare and sustainability. John Jumper, co-lead of AlphaFold's development, reflected on its significance, stating, "We are honored to be recognized for delivering on the long promise of computational biology to help us understand the protein world and to inform the incredible work of experimental biologists." He emphasized that AlphaFold is a tool for discovery, helping scientists understand diseases and develop new therapeutics at an unprecedented pace. The Origins of AlphaFold The roots of AlphaFold can be traced back to DeepMind's broader exploration of AI. Hassabis, a chess prodigy, began his career in 1994 at the age of 17, co-developing the hit video game Theme Park, which was released on June 15 that year. After studying computer science at Cambridge University and completing a PhD in cognitive neuroscience, he co-founded DeepMind in 2010, using his understanding of chess to raise funding from famed contrarian venture capitalist Peter Thiel. The company, which specializes in artificial intelligence, was acquired by Google in 2014 for around $500 million USD. As CEO of Google DeepMind, Hassabis has led breakthroughs in AI, including creating systems that excel at games like Go and chess. By 2016, DeepMind had achieved global recognition for developing AI systems that could master the ancient game of Go, beating world champions. It was this expertise in AI that DeepMind began applying to science, aiming to solve more meaningful challenges, including protein folding. The AlphaFold project formally launched in 2018, entering the Critical Assessment of protein Structure Prediction (CASP) competition -- a biannual global challenge to predict protein structures. That year, AlphaFold won the competition, outperforming other teams and heralding a new era in structural biology. But the real breakthrough came in 2020, when AlphaFold2 was unveiled, solving many of the most difficult protein folding problems with an accuracy previously thought unattainable. AlphaFold 2's success marked the culmination of years of research into neural networks and machine learning, areas in which DeepMind has become a global leader. The system is trained on vast datasets of known protein structures and amino acid sequences, allowing it to generalize predictions for proteins it has never encountered -- a feat that was previously unimaginable. Earlier this year, Google DeepMind and Isomorphic Labs unveiled AlphaFold 3, the third generation of the model, which the creators say uses an improved version of the Evoformer module, a deep learning architecture that was key to AlphaFold 2's remarkable performance. The new model also incorporates a diffusion network, similar to those used in AI image generators, which iteratively refines the predicted molecular structures from a cloud of atoms to a highly accurate final configuration. David Baker's Contribution to Protein Design While Hassabis and Jumper solved the prediction problem, David Baker's work in de novo protein design offers an equally transformative approach: the creation of entirely new proteins that do not exist in nature. Based at the University of Washington's Institute for Protein Design, Baker's lab developed Rosetta, a computational tool used to design synthetic proteins. Baker's work has led to the development of proteins that could be used to create novel therapeutics, including custom-designed enzymes and virus-like particles that may serve as vaccines. His group has even designed proteins to detect fentanyl, an opioid at the center of a global health crisis. By designing new proteins from scratch, Baker's research expands the boundaries of what proteins can do, complementing the predictive power of AlphaFold by enabling the creation of molecules tailored to specific functions. The Future of AI in Science The Nobel Prize recognition of AlphaFold and Baker's work underscores a broader trend: AI is rapidly becoming an indispensable tool in scientific research. AlphaFold's success has sparked new interest in the potential of AI to solve complex problems across various fields, including climate change, agriculture, and materials science. The Nobel Committee highlighted the transformative potential of these discoveries, emphasizing that they "open up vast possibilities" for the future of biology and chemistry. Hassabis has long been vocal about AI's potential to drive innovation, but he is also clear-eyed about the risks. "AI has the potential to accelerate scientific discovery at a rate we've never seen before, but it's crucial that we use it responsibly," he said in a recent interview. As AI systems like AlphaFold continue to evolve, their ability to simulate biological processes and predict outcomes could revolutionize healthcare, sustainability efforts, and beyond. Jumper and Hassabis' Nobel Prize is a recognition of their work's enormous impact, but it also signals the dawn of a new era in science -- one where AI plays a central role in unlocking the mysteries of life. What's next? The 2024 Nobel Prize in Chemistry recognizes the profound contributions of Demis Hassabis, John Jumper, and David Baker, whose pioneering work has reshaped the landscape of protein science. AlphaFold, now a cornerstone tool for researchers worldwide, has accelerated discovery in ways previously unimaginable. David Baker's work in computational protein design further expands the possibilities for biological innovation, offering new solutions to global challenges. Together, these advancements mark the beginning of a new era for artificial intelligence in science -- one where the possibilities are just beginning to unfold (pun intended). While he remains optimistic about AI's positive impact, Hassabis warns that the risks, including the potential for societal-scale disasters, must be taken as seriously as the climate crisis.

A pair of DeepMind researchers have won the 2024 Nobel Prize in Chemistry

A day after recognizing former Google vice president and engineering fellow Geoffrey Hinton for his contributions to the field of physics, the Royal Swedish Academy of Sciences has honored a pair of current Google employees. On Wednesday, DeepMind CEO Demis Hassabis and senior research scientist John Jumper won half of the 2024 Nobel Prize in Chemistry, with the other half going to David Baker, a professor at the University of Washington. If there's a theme to the 2024 Nobel Prize in Chemistry, it's proteins. Baker, Hassabis and Jumper all advanced our understanding of those essential building blocks of life that are responsible for functions both inside and outside the human body. The Nobel Committee cited Baker's seminal work in computational protein design. Since 2003, Baker and his research team have been using amino acids and computers to design entirely new proteins. In turn, those chemicals have contributed to the creation of pharmaceuticals, vaccines, nanomaterials and more. As for Hassabis and Jumper, their work, and that of the entire DeepMind team, on AlphaFold 2 led to a generational breakthrough. Since the 1970s, scientists have been trying to find a way to predict a protein's final, folded structure based solely on the amino acids that form its constituent parts. With AlphaFold 2, DeepMind created an AI algorithm that could do just that. Since 2020, the software has been able to successfully predict the structure of 200 million proteins, or nearly every one known to researchers. "One of the discoveries being recognized this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences," said Heiner Linke, chair of the Nobel Committee for Chemistry. "Both of these discoveries open up vast possibilities." More broadly, the 2024 Nobel Prizes highlight the growing importance of artificial intelligence in modern science. Moving forward, it's safe to say advanced algorithms will be essential to future scientific discoveries and breakthroughs.

AI is changing science: Google DeepMind duo win Nobel Prize in Chemistry

Demis Hassabis and John Jumper were awarded the prize for predicting protein structure with AI Google DeepMind scientists Demis Hassabis and John Jumper today won this year's Nobel Prize in Chemistry. The duo will share the prestigious prize -- seen as the pinnacle of scientific achievement -- with University of Washington professor David Bakker for his work on computational protein design. "This prize represents the promise of computational biology," Jumper said during a press conference on Wednesday. Hassabis co-founded DeepMind in 2014. Jumper was appointed director last year. The duo won their Nobel Prize for developed an AI model that solved a 50-year-old challenge in biology: predicting the structure of proteins. Dubbed AlphaFold2, the tool can predict over 200 million of protein structures -- nearly all known to science -- from their amino acid sequences. This enables researchers to understand how proteins work and interact with other molecules in the body, offering unprecedented insights into disease development and drug discovery. Since its launch, AlphaFold2 -- which is freely available -- has been used by more than 2 million scientists across 190 countries. It has supported multiple areas of research, from projects on malaria vaccines and Parkinson's treatments to drug-resistant bacteria. AlphaFold2's immediate impact is accelerated research, Jumper said during the press conference. "What I think will come soon through our work is that we're going to get better and better at harnessing biology and our understanding of biology to make medicines," he added. "I hope this means that, ultimately, we will be more responsive to, for example, pandemics." Hassabis said he's dedicated his life to AI because it could "improve the lives of billions of people." However, he cautioned that artificial intelligence can do both good and bad. "We have to really think very hard as these systems and techniques get more powerful about how to enable and empower all of the benefits and good use cases, whilst mitigating the risks." As artificial intelligence is changing the face of fundamental sciences, the Nobel Prize in Physics was also awarded to two AI researchers: John Hopfield and Geoffrey Hinton for their work in training neural networks using physics.

Google DeepMind scientists, biochemist share Nobel Prize in chemistry - SiliconANGLE

Google DeepMind scientists, biochemist share Nobel Prize in chemistry Google LLC scientists Demis Hassabis and John Jumper have won this year's Nobel Prize in chemistry together with biochemistry professor David Baker. The trio received the award for their contributions to protein research. In an announcement today, the Royal Swedish Academy of Sciences detailed Hassabis and Jumper jointly won half the Nobel Prize for their work on AlphaFold2. This is an artificial intelligence system that Google DeepMind, the search giant's machine learning lab, released in 2020 to advance protein research. The software solved a computational challenge that puzzled scientists for 50 years. The behavior of proteins, the building blocks of life, is heavily influenced by their structure. Until DeepMind's breakthrough, mapping out the structure of a protein required multimillion-dollar equipment and up to years of research. AlphaFold2 can perform the task automatically in a few minutes. Hassabis and Jumper led the team that created AlphaFold2. Hassabis is the CEO of the AI research group while Jumper is a senior research scientist. Last year, DeepMind introduced a successor to AlphaFold2 that is more accurate and can predict the structures of not only proteins but also other biological molecules. "The AlphaFold2 team immediately created large databases of predicted protein structures, first for the human proteome and then for the majority of sequences (> 200 million) available in the UniProt (Universal Protein Resource) database," the Nobel Committee for Chemistry stated. "This means that almost overnight, we got access to orders of magnitude more structural information." Baker, the third recipient of this year's Nobel Prize in chemistry, is a biochemistry professor at the University of Washington. He received the prize for his work on Top7, the first computationally-designed protein "entirely different to all known existing proteins." Baker and his team created Top7 in 2003 using a program called Rosetta that they developed four years earlier. The software generates protein designs based on protein structures in the Protein Data Bank, a scientific database. Since the release of Top7, Baker and his team have used Rosetta to design numerous additional proteins that can be used as pharmaceuticals, vaccines, nanomaterials and sensors. "In summary, the achievements of David Baker, Demis Hassabis and John Jumper in the fields of computational protein design and protein structure prediction are truly profound," the Nobel Committee for Chemistry stated. "Their work has opened up a new era of biochemical and biological research, where we can now predict and design protein structures in ways that had not been possible before." On Tuesday, John Hopfield and Geoffrey Hinton won the Nobel Prize in physics for their contributions to AI research. Hopfield used methods from the field of condensed matter physics to create an early neural network. Hinton, who is also a Turing Award recipient, built on Hopfield's work to develop one of the first deep learning model architectures.

2024 Nobel Prize in Chemistry goes to 3 pioneers of protein research

Three scientists, including two from Google DeepMind, have been awarded the 2024 Nobel Prize in Chemistry for revolutionary advancements in predicting and designing protein structures. Their work has opened new doors in fields ranging from medicine to nanotechnology, showcasing the immense potential of artificial intelligence (AI) and computational methods in transforming modern science. The prestigious award has been shared by Demis Hassabis, the founder of Google DeepMind, and John Jumper, a lead developer of the AlphaFold model.

DeepMind's Demis Hassabis and John Jumper scoop Nobel Prize in Chemistry for AlphaFold | TechCrunch

It has been a big week for Nobel Prizes in the world of artificial intelligence (AI). The Royal Swedish Academy of Sciences today announced the Nobel Prize in Chemistry winners for 2024, with DeepMind co-founder and CEO Demis Hassabis and DeepMind Director John Jumper sharing one-half of the prize, alongside David Baker who is head of the Institute for Protein Design at the University of Washington. The news comes the day after AI pioneers Geoff Hinton and John Hopfield won the Nobel Prize in Physics for their foundational work in machine learning and artificial intelligence. Hassabis and Jumper won the award, specifically, for "protein structure prediction," while Baker's was for "computational protein design." We've all heard that proteins are the building blocks of life, which is why DeepMind's work on AlphaFold has been so revolutionary. Though its potential had been touted for years, Google-subsidiary formally presented the AI model back in 2020, and went much of the way toward solving a problem that had stumped scientists for years by predicting the 3D structure of proteins using nothing more than their genetic sequence. The shape of a protein dictates how it works, and figuring out its shape was historically a slow, labor-intensive process that would often require years of lab experiments. With AlphaFold, DeepMind was able to accelerate this to mere hours, covering most of the 200 million proteins in existence. The ramifications of this can't be understated, as this kind of data is vital to things like drug discovery, diagnosing diseases, and bioengineering. "One of the discoveries being recognised this year concerns the construction of spectacular proteins," Heiner Linke, chair of the Nobel Committee for Chemistry, said in a statement. "The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities." It's worth noting that Hassabis was also awarded a U.K. knighthood for 'services to artificial intelligence' back in March. In addition to the global prestige of winning such an award, the Nobel Prize in Chemistry also comes with a cash prize of 11 million Swedish kronor ($1 million), with half of that going to David Baker and the other split between Hassabis and Jumper.

Google DeepMind co-founder shares Nobel Chemistry Prize

Better understanding of proteins has driven huge breakthroughs in medicine. Professor David Baker, based in the US, used amino acids to design a new protein, opening the door to the creation of new proteins used in pharmaceuticals, vaccines and other tools. Prof Baker told the committee shortly after the announcement that he was "very excited and very honoured". "I stood on the shoulders of giants," he said when asked how he had cracked the code of creating proteins. UK-based Demis Hassabis and John Jumper used artificial intelligence to predict the structures of almost all known proteins and created a tool called AlphaFold2. The committee called it a "complete revolution" in chemistry, used by 200 million people worldwide. The announcement was made by the Royal Swedish Academy of Sciences at a press conference in Stockholm, Sweden. The winners share a prize fund worth 11m Swedish kronor (£810,000).

Protein structure prediction wins the Nobel

The first human-designed protein that adopted a specific configuration. Credit: Johan Jarnestad/The Royal Swedish Academy of Science On Wednesday, the Nobel Committee announced that it had awarded the Nobel Prize in chemistry to researchers who pioneered major breakthroughs in computational chemistry. These include two researchers at Google's DeepMind in acknowledgment of their role in developing AI software that could take a raw protein sequence and use it to predict the three-dimensional structure the protein would adopt in cells. Separately, the University of Washington's David Baker was honored for developing software that could design entirely new proteins with specific structures. The award makes for a bit of a theme for this year, as yesterday's Physics prize honored AI developments. In that case, the connection to physics seemed a bit tenuous, but here, there should be little question that the developments solved major problems in biochemistry. DeepMind, represented by Demis Hassabis and John Jumper, had developed AIs that managed to master games as diverse as chess and StarCraft. But it was always working on more significant problems in parallel, and in 2020, it surprised many people by announcing that it had tackled one of the biggest computational challenges in existence: the prediction of protein structures. Chemically, proteins are a linear string of amino acids linked together, with living creatures typically having the choice of 20 different amino acids for each position along the string. Most of those 20 have distinctive chemical properties: some are acidic, others basic; some may be negatively charged, others positively charged, and still others neutral, etc. These properties allow different areas of the string to interact with each other, causing it to fold up into a complex three-dimensional structure. That structure is essential for the protein's function.

Nobel Prize for chemistry goes to ... AI, again

Let's just hope they don't give the literature award to a bot, too This year's Nobel Prizes are shaping up to be a triumph for AI. After awarding the physics prize to early AI pioneers yesterday, the chemistry prize has now gone to the creators of AI protein prediction platform AlphaFold and protein design tool Rosetta. DeepMind cofounder and CEO Demis Hassabis and director John Jumper will share half of the Nobel Prize in Chemistry for their work on AlphaFold models. The second generation can predict almost all known protein structures - more than 200 million in total. "The team trained AlphaFold2 on the vast information in the databases of all known protein structures and amino acid sequences and the new AI architecture started delivering good results," the Nobel committee said [PDF]. When it entered the 2020 Critical Assessment of Protein Structure Prediction (CASP) competition, AlphaFold2 performed almost as well as X-ray crystallography (the prior gold standard in modeling protein structures) "in most cases," the committee added. "Previously, it often took years to obtain a protein structure, if at all. Now it can be done in a few minutes." Jumper, who came to DeepMind after the Google subsidiary had already built the initial AlphaFold that improved on prior CASP results but was still only about 60 percent accurate, was essential to DeepMind 2's success, the Nobel body said. "AlphaFold2 was coloured by Jumper's knowledge of proteins," the committee explained. "The team also started to use the innovation behind the recent enormous breakthrough in AI: neural networks called transformers." So maybe some additional AI tech helped, too. Although AlphaFold has been fundamental in helping humans become better predictors of protein shapes, which play a critical role in their function, it can't develop drugs or make anything new. That's where Rosetta, designed by University of Washington biochemistry professor David Baker, comes in. Baker developed his own protein prediction software, dubbed Rosetta, in the 1990s, and when it entered the CASP competition in 1998, it did well "in comparison to other participants," the Nobel committee said. After the competition, Baker and his team got the idea to use the software in reverse: Instead of using amino acid sequences to predict the shape of a protein, they began experimenting on whether inputting the shape of a desired protein would suggest an amino acid sequence to create it. Lo and behold, it worked perfectly and led to the creation of Top7, "the first protein that was entirely different to all other known existing proteins," according to the Nobel folks. Proteins are fundamental to understanding biochemistry and are involved in the creation of biological structures like muscles, as well as chemicals like hormones and antibodies. By enabling the creation of new proteins, humans can do all sorts of things. "This can lead to new nanomaterials, targeted pharmaceuticals, more rapid development of vaccines, minimal sensors and a greener chemical industry - to name just a few applications," the committee said. The chemistry Nobel being awarded for AI development marks the second time this year. The Nobel in physics was awarded yesterday to John Hopfield for his work developing early neural networks, and to AI godfather Geoffrey Hinton for giving machines the ability to interpret information they're trained to recall. Three Nobels have been awarded so far this year; the first, for physiology and medicine, went to Victor Ambros and Gary Ruvkun for the discovery of microRNA, which regulates gene expression and protein production. Nobel prizes for literature and peace have not yet been handed out. ®

Explainer: Why have protein design and structure prediction won ...

This year's chemistry Nobel prize was awarded to three scientists working in the field of protein design and structure prediction. One half of the prize was awarded to David Baker at the University of Washington in Seattle, US, while the other half was awarded to Demis Hassabis and John Jumper, both from Google DeepMind, based in the UK. We have known for a long time that proteins are the chemical tools of life - there are many different types of protein that all have different roles in our bodies. Each protein is made of a string of amino acids that folds up into a specific 3D shape, or structure, and each protein's function is closely related to that shape. Knowing a protein's structure helps us understand how it works and for decades scientists have been working on ways to figure out protein structures, which has presented many challenges along the way. In the 1950s, the development of x-ray crystallography enabled researchers to obtain the first 3D structures of proteins. John Kendrew and Max Perutz were awarded the Nobel prize in chemistry in 1962 for that work. Other experimental methods such as NMR and cryo-EM have since been added to the toolkit and researchers have now determined the structures of around 200,000 proteins. In 1972, American biochemist Christian Anfinsen was awarded the Nobel prize in chemistry for his discovery that it is the sequence of amino acids that determines the way the polypeptide chain folds itself and that no additional genetic information is required. That means it should be possible, in theory, to predict the shape of a protein just by knowing its amino acid sequence. This finding led to 50-year-long quest to find a way to predict the 3D structure of a protein from its amino acid sequence - but the number of theoretically possible conformations of a protein is, in short, astronomical. This so-called 'prediction problem' became the great challenge of biochemistry and led to the launch of a project, turned competition, in 1994 called Critical Assessment of Protein Structure Prediction (CASP) which aimed to speed up discoveries in the field. However, it was many years before a significant breakthrough was made. The work of these three scientists is closely interlinked. Hassabis and Jumper used artificial intelligence (AI) to predict the 3D structure of a protein from its sequence alone. Meanwhile, Baker developed computational methods that could solve the inverse problem: starting from a protein with a particular structure, figuring out what sequence it would have. That enabled him to create entirely new proteins that did not previously exist. All of this work builds on the decades and decades of research - and chemistry Nobel prizes - on understanding the structure of proteins. In the 1990s, Baker began to explore how proteins fold. Using these insights he developed Rosetta: computer software for predicting protein structures. Initially Rosetta was used to convert amino acid sequences into structures, but following the 1998 CASP competition, Baker and his team decided to use the software in reverse; a technique which eventually led them to create completely novel proteins from scratch, also known as de novo design. To do this, they drew a protein with an entirely new structure and had Rosetta work out which type of amino acid sequence would result in that protein. They then introduced a gene that coded for their proposed amino acid sequence into bacteria, which produced the novel protein,Top7. Using x-ray crystallography, they were able to determine that the protein they had made had a structure very close to the one they had initially designed. The work of Baker and his colleagues was published in 2003 and the code for Rosetta was released to the global research community to enable ongoing development of the software and new applications. In 2010, Hassabis, a British computer science and AI researcher, founded DeepMind Technologies. DeepMind initially developed AI models for popular board games, and following its acquisition by Google in 2014 it achieved a machine-learning milestone when its AlphaGo program defeated the world's best Go player in 2016. The company went on to construct a computer program based on a convolutional neural network - called AlphaFold. In 2018, AlphaFold left the rest of the field behind at the 13 CASP competition, reaching 60% accuracy for its predicted protein structures. But getting to higher accuracies presented a new challenge. Enter Jumper, a researcher with creative ideas about how to improve AlphaFold. Together, Jumper and Hassabis co-led the work that led to AlphaFold2 in 2020, aided by Jumper's knowledge of proteins and the innovation behind an enormous breakthrough in AI - neural networks called transformers - which could find patterns in huge amount of data more flexibly than ever before. When an amino acid sequence with an unknown structure is fed into the programme, it searches the database for similar amino acid sequences and protein structures. The network then creates an alignment of similar sequences, sometimes from difference species, and looks for correlations between them as well as possible interactions between amino acids. From this information AlphaFold2 can then iteratively refine a distance map - which tells you how close two amino acids are to each other in space - and sequence analysis. Finally, it then converts all that information into a 3D structure. Now AlphaFold has more than 2 million users and has resulted in the prediction of 200 million protein structures. Because of these breakthroughs, most monomeric protein structures can now be predicted with high fidelity, and large databases of hundreds of millions of structures have been created as a result. Proteins are such a key component of our biology that being able to design them and predict their structures opens up potential applications in pharmaceuticals, nanomaterials and rapid development of vaccines, as well as many others. There's no doubt that the development of AI protein structure prediction tools like AlphaFold represent an important milestone in structural biology, but they are not a replacement for experimental structure determination. Experimentally determined structures are still superior to predictions, and they will also be needed to generate the training datasets for the next generations of AI tools, as well as being used to assess the performance of those tools in predicting structures. One example of the ongoing need for experimental approaches is in drug design. Although determining a protein's structure may help generate ideas about what compounds to make next, there are many other factors regarding the biological activity of proteins to consider, such as pharmacokinetics, metabolism and toxicology, that can not currently be solved using AI. It is much more likely that the future of structural biology will be in integrating high-throughput experimental studies with AI, not replacing it.

Nobel Prize Honors Breakthroughs in Protein Design and Structure Prediction - Neuroscience News

Summary: The 2024 Nobel Prize in Chemistry celebrates two groundbreaking achievements in protein science: designing novel proteins and predicting protein structures using AI. David Baker has pioneered techniques to construct entirely new proteins that could serve as pharmaceuticals, vaccines, and nanomaterials. Meanwhile, Demis Hassabis and John Jumper's AI model, AlphaFold2, solved the decades-old challenge of predicting protein structures, allowing researchers to visualize nearly all known proteins. These advancements hold transformative potential for fields like medicine, biotechnology, and environmental science. The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Chemistry 2024 with one half to David Baker, University of Washington, Seattle, WA, USA and Howard Hughes Medical Institute, USA "for computational protein design", and the other half jointly to Demis Hassabis, Google DeepMind, London, UK and John M. Jumper, Google DeepMind, London, UK "for protein structure prediction". The Nobel Prize in Chemistry 2024 is about pro­teins, life's ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins' complex structures. These discoveries hold enormous potential. The diversity of life testifies to proteins' amazing capacity as chemical tools. They control and drive all the chemi­cal reactions that together are the basis of life. Proteins also function as hormones, signal substances, antibodies and the building blocks of different tissues. "One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities," says Heiner Linke, Chair of the Nobel Committee for Chemistry. Proteins generally consist of 20 different amino acids, which can be described as life's building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors. The second discovery concerns the prediction of protein structures. In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein's function. Since the 1970s, researchers had tried to predict protein structures from amino acid sequences, but this was notoriously difficult. However, four years ago, there was a stunning breakthrough. In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic. Life could not exist without proteins. That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind.

2024 Nobel Prize in Chemistry goes to David Baker, Demis Hassabis, and John M Jumper For work on Proteins

Hassabis and Jumper developed AlphaFold2, which successfully predicts the three-dimensional structures of nearly all known proteins, approximately 200 million overcoming a challenge that has perplexed scientists for over 50 years. David Baker, an American biochemist and computational biologist, and Demis Hassabis and John M Jumper, two Google DeepMind scientists, have been awarded the Nobel Prize in Chemistry 2024 by The Royal Swedish Academy of Sciences. Baker received the award for 'computational protein design' and the other two shared jointly by Hassabis and Jumper for 'protein structure prediction.' The latter two have successfully utilised AI to predict the structure of almost all known proteins. In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, researchers have predicted the structure of virtually all the 200 million proteins identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Researchers can now better understand antibiotic resistance among many scientific applications and create images of enzymes that can decompose plastic. AlphaFold2 successfully predicts the three-dimensional structures of nearly all known proteins, approximately 200 million, overcoming a challenge that has perplexed scientists for over 50 years. Their work allows researchers to quickly access protein structures, significantly enhancing studies in areas such as antibiotic resistance and environmental sustainability. Baker's contribution lies in creating entirely new proteins through computational methods, enabling innovative applications in pharmaceuticals and green chemistry. His work allows researchers to design proteins from scratch, pushing the boundaries of biotechnology. The synergy between AI advancements and computational design has opened unprecedented possibilities in the understanding of life's building blocks, heralding a new era of biochemical research. Recently, Geoffrey E. Hinton and John J. Hopfield were also awarded the Nobel Physics Prize 2024 in Physics by the Royal Swedish Academy of Sciences for pioneering advancements that form the basis of today's ML world. Their work used principles of physics to develop neural networks.

Nobel Prize awarded for protein design and prediction research

The discoveries of David Baker and his fellow Laureates Demis Hassabis and John M Jumper 'open up vast possibilities', according to the chair of the Nobel Committee for Chemistry. Three scientists have been jointly awarded the 2024 Nobel Prize for Chemistry today (9 October) for discoveries in protein design and protein structure prediction. David Baker was honoured with one half of the award for his innovative work in computational protein design, while the other half went to Demis Hassabis and John M Jumper for their work on an AI model that can predict the structure of nearly all of 200m proteins. Baker is a biochemist and the head of the Institute for Protein Design at the University of Washington. In 2003, he succeeded in using amino acids, which form the structure of a protein, to design a new protein unlike any other. Baker and his team achieved this by using a computer program called Rosetta, which assembles short structural fragments from unrelated protein structures with similar local sequences in the Protein Data Bank (a repository of information about the 3D structures of proteins, nucleic acids and complex assemblies). Baker and his colleagues showed that the software Rosetta could be used to design a wide range of protein structures. Since then, Baker and his research group have continued to produce unique protein creations that can be applied as things such as pharmaceuticals, vaccines, nanomaterials and tiny sensors. Along with his groundbreaking work on protein structure, Baker has published more than 600 research papers, co-founded 21 companies, and has been awarded more than 100 patents. His previous accolades include a 2021 Breakthrough Prize in Life Sciences, and he was recently included in Time's 100 most influential people in health for 2024. Speaking at the announcement, Baker said he was "very excited and very honoured" to accept the award, while also crediting his colleagues Steve Mayo and Bill DeGrado by stating that he "stood on the shoulders of giants". Hassabis is the co-founder and CEO of DeepMind, an AI research company that was acquired by Google in 2014 and merged with the tech giant's other research team, Google Brain, last year to spearhead the company's AI efforts. Jumper is a senior research scientist at Google DeepMind, who, together with Hassabis, developed an AI model called AlphaFold 2 in 2020. Using AlphaFold 2, they have been able to predict the 3D structure of nearly all of the 200m proteins that the researchers have identified. The model does this by examining a protein's amino acid sequence. DeepMind previously stated that the AlphaFold model could help solve "biological mysteries", and tackle issues such as plastic pollution and antibiotic resistance. The latest iteration of the model - AlphaFold 3 - was created in May through a collaboration between DeepMind and Isomorphic Labs, which was also founded by Hassabis. The companies claimed that this latest model has improved prediction capabilities and could be used to boost drug discovery efforts. Heiner Linke, chair of the Nobel Committee for Chemistry, said that the discoveries recognised today "open up vast possibilities", and emphasised the important consequences of these discoveries. "To understand how life works, we first need to understand the shape of proteins," he said at the award's announcement. Don't miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic's digest of need-to-know sci-tech news.

Google DeepMind Nobel Showcases AI's Medical Potential

Today's Nobel Prize in chemistry is an incredible vote of confidence in the potential for artificial intelligence to transform the way medicines are invented by using AI to illuminate and manipulate proteins, life's most basic building blocks. The Royal Swedish Academy of Sciences honored University of Washington professor David Baker and two scientists from Google DeepMind, CEO Demis Hassabis and senior research scientist John Jumper.

Nobel Winners in Chemistry Used AI to Innovate New Protein Designs

Three scientists who discovered powerful techniques to decode and even design novel proteins -- the building blocks of life -- were awarded the Nobel Prize in chemistry Wednesday. Their work used advanced technologies, including artificial intelligence, and holds the potential to transform how new drugs and other materials are made. The prize was awarded to David Baker, a biochemist at the University of Washington in Seattle, and to Demis Hassabis and John Jumper, computer scientists at Google DeepMind, a British-American artificial intelligence research laboratory based in London. Heiner Linke, chair of the Nobel Committee for Chemistry, said the award honored research that unraveled "a grand challenge in chemistry, and in particular in biochemistry, for decades." "It's that breakthrough that gets awarded today," he said.

Moungi Bawendi, Louis Brus, and Alexei Ekimov Win Nobel Chemistry Prize 2024

Hassabis and Jumper developed AlphaFold2, which successfully predicts the three-dimensional structures of nearly all known proteins, approximately 200 million overcoming a challenge that has perplexed scientists for over 50 years. Moungi Bawendi, Louis Brus, and Alexei Ekimov were awarded the Nobel Prize in Chemistry by The Royal Swedish Academy of Sciences with one half to David Baker 'for computational protein design' and the other half jointly to Demis Hassabis and John M. Jumper 'for protein structure prediction.' Nobel laureates Demis Hassabis and John Jumper have successfully utilised AI to predict the structure of almost all known proteins. In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic. Hassabis and Jumper developed AlphaFold2, which successfully predicts the three-dimensional structures of nearly all known proteins, approximately 200 million overcoming a challenge that has perplexed scientists for over 50 years. Their groundbreaking work allows researchers to quickly access protein structures, significantly enhancing studies in areas such as antibiotic resistance and environmental sustainability. David Baker's contribution lies in the creation of entirely new proteins through computational methods, enabling innovative applications in pharmaceuticals and green chemistry. His work allows researchers to design proteins from scratch, pushing the boundaries of biotechnology. The synergy between AI advancements and computational design has opened unprecedented possibilities in the understanding of life's building blocks, heralding a new era of biochemical research. Recently, Geoffrey E Hinton and John J Hopfield were also awarded the Nobel Physics Prize 2024 in Physics by The Royal Swedish Academy of Sciences for pioneering advancements that form the basis of today's ML world. Their work used principles of physics to develop neural networks.

2024 Nobel Prize in chemistry awarded to scientists who revealed a 'completely new world of protein structures'

A panel announces the winners of the 2024 Nobel Prize in Chemistry on October 9th. (Image credit: JONATHAN NACKSTRAND via Getty Images) The 2024 Nobel Prize in chemistry has been awarded to three scientists who work in two closely intertwined fields of protein science. David Baker, a professor of biochemistry at the University of Washington, received half of the 11 million Swedish krona ($1.06 million) prize for his work on computational protein design -- a tool that enables researchers to design and create completely novel protein structures with properties unlike any found in nature. The second half of the prize was shared between Demis Hassabis and John Jumper, respectively the CEO and director of Google DeepMind, for their work on protein structure prediction. The AI-powered program AlphaFold2, released in 2021, can predict the three-dimensional structure of any protein from the amino acid sequence encoded in DNA, revolutionizing our understanding of how proteins and molecules in living systems interact with each other. "Proteins are the molecules which enable life," Heiner Linke, chair of the Nobel Committee for Chemistry, said during the announcement ceremony in Sweden this morning (Oct. 9). A protein has tens of thousands of individual atoms, and its specific function is determined by the precise positions of these atoms, with links and folds between the different parts of the molecule creating a unique 3D shape. "To understand how life works, we first need to understand the shape of proteins," Linke said. Protein molecules are formed from many individual units called amino acids, which are encoded by three "letter" DNA sequences. It should therefore be possible to predict the 3D structure of a particular protein from this sequence of amino acids. But this problem has been frustrating scientists for decades because there are many possible ways for proteins to fold. In 2020, Hassabis and Jumper finally cracked this code by developing a program called AlphaFold2, which boosted the accuracy of structure predictions from 40% to 90%. The AI program was trained on a database of protein sequences and protein structures and looks for correlations between the positions of amino acids across thousands of examples. The system then iteratively refines these results down to a single predicted 3D structure. Sign up for the Live Science daily newsletter now Get the world's most fascinating discoveries delivered straight to your inbox. Contact me with news and offers from other Future brandsReceive email from us on behalf of our trusted partners or sponsorsBy submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. In the years since it was released, this tool has dramatically improved our understanding of thousands of protein-mediated processes, including antibiotic resistance, and it is now possible to mine these databases for proteins with previously unknown functions, such as plastic-degrading enzymes. RELATED STORIES -- 2 scientists snag Nobel in medicine for discovering 'microRNAs' -- 'It will be comparable with the industrial revolution': Two legendary AI scientists win Nobel Prize in physics for work on neural networks -- Nobel Prize in chemistry: 1901-present Protein design approaches this same problem from the opposite direction, enabling researchers to visualize the ideal 3D protein structure for a particular function and work backward to calculate the amino acid sequence needed to synthesize it. In 2003, Baker developed a computer program called Rosetta that combines shorter amino acid fragments from an existing database, successively tweaking and optimizing the sequence to match the required 3D shape. "David Baker opened up a completely new world of protein structures," Johan Åqvist, a member of the Nobel Committee for Chemistry, said during the announcement. "It's only your imagination which sets the limit for what you can do here." Rosetta has since helped to design hundreds of new proteins with diverse applications, ranging from inhibiting the COVID spike protein to acting as biological sensors for opioids in the environment. Speaking to The Royal Swedish Academy of Sciences Secretary General Hans Ellegren following the prize announcement, Baker said he felt "very excited and very honored" and had been "really deeply inspired by others in the field and people I've worked with."

Opinion | When AI looked at biology, the result was astounding

The Nobel Prize in chemistry honored a real-world example of how AI is helping humans. One of this year's Nobel Prize winners in physics, Geoffrey Hinton, who pioneered work on the neural networks that undergird artificial intelligence, has warned that machines might someday get smarter than humans. Perhaps. But this year's Nobel Prize in chemistry honored a real-world example of how AI is helping humans today with astounding discoveries in protein structure that have far-reaching applications. This is a development worth savoring. Proteins are biology's lead actors. As the Nobel committee pointed out, proteins "control and drive all the chemi­cal reactions that together are the basis of life. Proteins also function as hormones, signal substances, antibodies and the building blocks of different tissues." In the human body, they are necessary for the structure, function and regulation of tissues and organs. All proteins begin with a chain of up to 20 kinds of amino acids, strung together in a sequence encoded in DNA. Each chain folds into a unique structure, and those shapes determine how proteins interact with other molecules. Looking like a tangled ball of twine, proteins have a complex and precise design of moving parts that are linked to chemical events and bind to other molecules. Antibodies are proteins produced by the immune system that bind to foreign molecules, including those on the surface of an invading virus, such as the spikes on the coronavirus that causes covid-19. At the end of the 1950s, University of Cambridge researchers John Kendrew and Max Perutz successfully used a method called X-ray crystallography to produce the first 3D models of proteins. In recognition, they were awarded the 1962 Nobel Prize in chemistry. In the ensuing half-century, the quest to document protein structures remained laborious and slow. A single protein structure might require a doctoral student four or five years to figure out. Before AI, the field's central repository contained some 185,000 experimentally solved protein structures. Follow Editorial Board Follow This year's Nobel Prize in chemistry went to three scientists who revolutionized the field. David Baker of the University of Washington built entirely new kinds of proteins. Demis Hassabis and John Jumper of DeepMind, a Britain-based firm that is part of Alphabet, Google's parent company, developed an AI and machine learning model that can predict the structure of proteins, decoding the amino acids that make up each protein. The model, AlphaFold, can do in minutes what once took years. AlphaFold takes advantage of neural networks that can locate patterns in enormous amounts of data. The system was trained on the vast information in the databases of all known protein structures and amino acid sequences. AlphaFold has predicted more than 200 million protein structures, or nearly all catalogued proteins known to science, including those in humans, plants, bacteria, animals and other organisms. The AlphaFold Protein Structure Database makes this data freely available. To design new drugs and vaccines, scientists need to know how a protein looks or behaves. The AlphaFold result is a prediction -- a visual representation of a protein's expected structure -- but such predictions can accelerate biomedical research. The AlphaFold blog recounts the story of scientists searching for a better vaccine against malaria, a disease that afflicts 250 million people a year and causes more than 600,000 deaths. Because malaria is caused by a shapeshifting parasite, vaccine researchers had long struggled to characterize the structure of one surface protein they needed to target to interrupt the infection. Then AlphaFold's prediction of the right structure snapped it into focus. Matthew Higgins at the University of Oxford said the breakthrough helped his team decide which bits of the protein to put in the vaccine, which trains the body's immune system to detect it and act. This helped advance his research from "a fundamental science stage to the preclinical and clinical development stage." Anyone who has used ChatGPT knows that AI is not always correct -- and the malaria scientists found that some of the 3D visualizations of proteins were inexact. But AI will only get better over time. Already, the AlphaFold effort is expanding to create accurate visualizations of how proteins interact with other biomedical structures, such as nucleic acids. In the years ahead, AI dangers must be confronted and safeguards considered. Without a doubt, there are risks when powerful technology falls into the hands of malign actors. But, for now, AlphaFold shows that AI can supercharge existing knowledge to benefit mankind. The Nobel committee noted that, thanks to these advances, "researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic." And there will be more to come.

AI Pioneers and Protein Designer Win Nobel Prize in Chemistry | PYMNTS.com

The Royal Swedish Academy of Sciences on Wednesday (Oct. 9) awarded the Nobel Prize in Chemistry to three scientists whose work in computational protein design and structure prediction could lead to breakthroughs in drug development, materials science and biotechnology. David Baker of the University of Washington will receive half of the 11 million Swedish kronor ($1 million) prize "for computational protein design." Demis Hassabis and John M. Jumper of Google DeepMind will share the other half "for protein structure prediction." "One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences," Heiner Linke, chair of the Nobel Committee for Chemistry, said in a news release. "Both of these discoveries open up vast possibilities." Baker, 62, pioneered techniques to design entirely new proteins not found in nature. His work has led to the creation of novel proteins with potential applications in pharmaceuticals, vaccines and nanomaterials. "In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein," the Nobel committee noted. "Since then, his research group has produced one imaginative protein creation after another." Hassabis, 48, and Jumper, 39, were recognized for developing AlphaFold2, an artificial intelligence system that can accurately predict the three-dimensional structure of proteins from their amino acid sequences. This achievement solved a long-standing challenge in molecular biology. "In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified," the committee said. The ability to predict and design protein structures has far-reaching implications across multiple scientific disciplines. Researchers can now better understand complex biological processes, design more effective drugs and develop new materials with specific properties. "Life could not exist without proteins," the Nobel committee said. "That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind." Baker's work has already yielded practical applications. His lab has designed proteins that can be used as sensors, catalysts, and potential therapeutic agents. "Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins," the committee said. The impact of AlphaFold2 has been equally profound. Since its release, the AI model "has been used by more than two million people from 190 countries," according to the Nobel committee. Applications range from studying antibiotic resistance to developing enzymes that can break down plastic. Hassabis, CEO of Google DeepMind, and Jumper, a senior research scientist at the company, emphasized the collaborative nature of their work. In a joint statement, they said, "This recognition reflects the efforts of the entire AlphaFold team and our partners in the scientific community." The chemistry prize is the third Nobel awarded this week, following the prizes in medicine and physics. The literature prize will be announced on Thursday, followed by the peace prize on Friday and the economics prize on Monday.

AI just won a Nobel Prize for its ability to predict protein structures

Artificial intelligence systems have now become so sophisticated they are being awarded Nobel prizes for their academic achievements, and now AI has gained its second Nobel prize, but this time for protein prediction. Geoffrey Hinton, a computer scientist whose work on deep learning is the foundation of all AI models currently used today, was awarded a Nobel prize, along with Princeton University professor John Hopfield. Both researchers were awarded the Nobel Prize in physics for their contributions to deep learning technologies, which have become the underpinning technology we now broadly call AI. Now, AI has done it again, with a Nobel Prize being given to Demis Hassabis, the cofounder and CEO of Google DeepMind, and John M. Jumper, a director at DeepMind, for the creation of an AI capable of accurately predicting the structures of protein. Half of the Nobel Prize is awarded to Hassabis and Jumper, and the other half is awarded to David Baker, a professor of biochemistry at the University of Washington, who was recognized for his work on computational protein design. Each of the prize winners shares a $1 million pot. Why is this creation important? Being able to accurately predict protein structures has many big implications as it will mean researchers are able to develop a deeper understanding of human health, the emergence of life, and the creation of lifesaving drugs like the cure for cancer - all through understanding how protein structures work.

Nobel Prize in chemistry awarded for work on proteins

The Nobel Prize in chemistry was awarded Wednesday to David Baker, Demis Hassabis and John Jumper for their work with proteins. Baker works at the University of Washington in Seattle, while Hassabis and Jumper both work at Google Deepmind in London. Hans Ellegren, secretary general of the Royal Swedish Academy of Sciences that decides on the winner, announced the prize. In 2003, Baker designed a new protein and since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors, the Nobel committee said. Hassabis and Jumper created an artificial intelligence model that has been able to predict the structure of virtually all the 200 million proteins that researchers have identified, the committee added. Last year, the chemistry award went to three scientists for their work on quantum dots -- tiny particles just a few nanometers in diameter that can release very bright colored light and whose applications in everyday life include electronics and medical imaging. Six days of Nobel announcements opened Monday with Americans Victor Ambros and Gary Ruvkun winning the medicine prize. Two founding fathers of machine learning -- John Hopfield and Geoffrey Hinton -- won the physics prize. The awards continue with the literature prize on Thursday. The Nobel Peace Prize will be announced Friday and the economics award on Oct. 14. The prize carries a cash award of 11 million Swedish kronor ($1 million) from a bequest left by the award's creator, Swedish inventor Alfred Nobel. The laureates are invited to receive their awards at ceremonies on Dec. 10, the anniversary of Nobel's death. Nobel committee announcement: The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Chemistry 2024 with one half to David Baker, University of Washington, Seattle, WA, U.S. They cracked the code for proteins' amazing structures The Nobel Prize in Chemistry 2024 is about proteins, life's ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins' complex structures. These discoveries hold enormous potential. The diversity of life testifies to proteins' amazing capacity as chemical tools. They control and drive all the chemical reactions that together are the basis of life. Proteins also function as hormones, signal substances, antibodies and the building blocks of different tissues. "One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities," says Heiner Linke, Chair of the Nobel Committee for Chemistry. Proteins generally consist of 20 different amino acids, which can be described as life's building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors. The second discovery concerns the prediction of protein structures. In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein's function. Since the 1970s, researchers had tried to predict protein structures from amino acid sequences, but this was notoriously difficult. However, four years ago, there was a stunning breakthrough. In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic. Life could not exist without proteins. That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind.

Discover who won the 2024 Nobel Prize in Chemistry!

Breakthroughs have vast implications for science and medicine The Royal Swedish Academy of Sciences has announced the Nobel Prize in Chemistry for 2024, recognising the significant contributions of three remarkable scientists. David Baker from the University of Washington and Howard Hughes Medical Institute has been awarded one half of the prize for his pioneering work in computational protein design. The other half is jointly awarded to Demis Hassabis and John M. Jumper from Google DeepMind for their groundbreaking AI model that predicts protein structures. Proteins are vital to life, acting as catalysts for chemical reactions and forming the structural foundation for cells and tissues. Baker's innovative research has led to the creation of entirely new proteins, which could revolutionise pharmaceuticals, vaccines, and nanotechnology. His approach utilises the 20 amino acids that compose proteins, leading to unique protein structures with diverse applications. The challenge of predicting protein structures has existed for over 50 years. Since the 1970s, researchers have struggled to develop reliable methods for predicting how amino acid sequences fold into three-dimensional structures. In 2020, the introduction of the AlphaFold2 AI model by Demis Hassabis and John M. Jumper transformed this field. The model can accurately predict the structures of nearly all known proteins, facilitating advancements in various scientific domains, including antibiotic research and environmental science. Heiner Linke, Chair of the Nobel Committee for Chemistry, highlighted the impact of these discoveries, noting their potential to transform our understanding of life at the molecular level. The ability to design new proteins and predict their structures holds vast possibilities for humanity, paving the way for new therapeutic interventions and biotechnological innovations.

Nobel Prize for chemistry goes to American 2 Google scientists in Britain

Oct. 9 (UPI) -- The Nobel Prize for chemistry was awarded to American David Baker along with Brits Demis Hassabis and John M. Jumper for separate work in unraveling the mystery of the complex structures of protein, the Nobel committee announced on Wednesday. Baker, of the University of Washington and the Howard Hughes Medical Institute, was awarded for the computation of protein design. Hasabis and Jumper, both of Google DeepMind, were able to create an artificial intelligence model to help predict the structure of protein sequences. The committee said Baker for the first time designed new proteins from amino acids that today are being used in pharmaceuticals, vaccines, nanomaterials and tiny sensors. "One of the discoveries being recognized this year concerns the construction of spectacular proteins," the Nobel Committee for Chemistry said in a statement. "The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities." The committee said the predicting of protein structures long proved problematic for researchers. "In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein's function," the committee said. "Since the 1970s, researchers had tried to predict structures from amino acid sequences but this was notoriously difficult. However, four years ago, there was a stunning breakthrough." That's when Hassabis and Jumper developed their AlphaFold2 AI model that led to the prediction of the protein structures for all 200 million proteins that researchers have identified. Since then, the model has been used by two million people from 190 countries.

Google DeepMind scientists win Nobel Prize for chemistry

The scientific applications for AlphaFold2 include helping researchers to understand antibiotic resistance and develop plastic degrading enzymes, according to the Nobel Committee. "Work that once took years now takes just a few minutes thanks to this year's chemistry laureates," the Nobel committee said on X. The development of artificial intelligence technology has played a notable role in this year's lineup, with the Nobel Prize for Physics being awarded yesterday to two scientists credited for building the "foundation" for AI. That announcement has already attracted criticism from some physicists who feel the award was miscategorized -- a sentiment echoed by Geoffrey Hinton, one of the Physics awardees who is known as a "Godfather of AI."

Chemistry Nobel

Three scientists won the Nobel Prize in Chemistry for discovering how to predict the shape of proteins, crucial to understanding their function, and for creating entirely novel proteins that can clean the environment, block viruses, and more If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. The 2024 Nobel Prize in Chemistry was awarded Wednesday to three scientists for discovering how proteins -- the building blocks of life and the dynamos that let cells function -- do their jobs. Proteins build muscles and brains, help hearts beat on time, and filter out poisons. Half of the Nobel went jointly to researchers Demis Hassabis and John Jumper, both of Google DeepMind in London, for developing an AI program -- AlphaFold2 -- that can predict a protein's shape and structure from its chemical building blocks, called amino acids. Since protein shape determines its function, these predictions are incredibly important. The other half of the prize went to structural biologist David Baker of the University of Washington, for figuring out ways to design entirely new proteins -- molecules never seen in nature. Some of these artificial proteins can serve as miniscule sensors, while others may block the coronavirus that causes COVID. Baker will get 50 percent of the prize money, 11 million Swedish kronor, or nearly $1 million. Hassabil and Jumper will get the other 50 percent.

Nobel Prize in chemistry goes to three scientists for work on proteins

The three scientists contributed to science by creating AI protein models and innovative designs including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors, the Nobel committee said. The Nobel Prize in chemistry was awarded to a trio of scientists on Wednesday for their work on proteins. Google DeepMind co-founder Demis Hassabis and DeepMind researcher John Jumper were awarded one part of the prize for developing an artificial intelligence (AI) model to predict the structures of almost all known proteins, a feat that has taken 50 years to solve. They created a tool called AlphaFold2, which the Nobel Prize committee said is a "complete revolution" in chemistry, and the tool is now used by 200 million people worldwide. The committee also said the tool meant that researchers could now better understand antibiotic resistance and create images of enzymes that can decompose plastic. The second part of the prize was awarded to US-based Professor David Baker. In 2003, Baker designed a new protein and since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors, the Nobel committee said. Baker said he was "very excited and very honoured," after he found out he had won. Hans Ellegren, secretary general of the Royal Swedish Academy of Sciences which decides on the winner, announced the prize. Last year, the award went to three scientists for their work on quantum dots, tiny particles just a few nanometres in diameter that can release very bright coloured light and whose applications in everyday life include electronics and medical imaging. Six days of Nobel announcements opened Monday with Americans Victor Ambros and Gary Ruvkun winning the medicine prize. The physics prize, meanwhile went to two pioneers in machine learning, John Hopfield and Geoffrey Hinton. The awards continue with the literature prize on Thursday. The Nobel Peace Prize will be announced on Friday and the economics will be awarded next Monday. The prize carries a cash award of 11 million Swedish kronor (nearly €1 million) from a bequest left by the award's creator, Swedish inventor Alfred Nobel. The laureates are invited to receive their awards at ceremonies on 10 December, the anniversary of Nobel's death.

Trio wins chemistry Nobel for protein design, prediction

Stockholm (AFP) - Americans David Baker and John Jumper, together with Briton Demis Hassabis, shared the Nobel Prize in Chemistry on Wednesday for work revealing the secrets of proteins through computing and artificial intelligence. The three were honoured for cracking the code of the structure of proteins, the building blocks of life, with the jury hailing their work as holding "enormous potential" in a range of fields. Biochemist Baker, 62, was given half the award "for computational protein design", while Hassabis and Jumper shared the other half "for protein structure prediction," the Nobel committee said. "David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins," it said in a statement. The committee added that his work has led to the creation of proteins that "can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors." Hassabis and Jumper developed an AI model "to solve a 50-year-old problem: predicting proteins' complex structures," the jury said of the duo who head up AI research lab Google DeepMind. 'Monumental achievement' Hassabis, 48, and Jumper, who was born in 1985, were among those speculated to be contenders for this year's Nobel for their work on the AI-model Alphafold. They received the prestigious Lasker Award in 2023. The AI tool is used to predict the three-dimensional structure of proteins based on their amino acid sequence, and the Alphafold database now contains the predicted structure of over 200 million proteins. In a post to X, Google DeepMind congratulated the duo. "This is a monumental achievement for AI, for computational biology, and science itself," the research lab said. Heiner Linke, chair of the Nobel Committee for Chemistry, told a press conference that "proteins are the molecules that enable life. Proteins are building blocks that form bones, skin, hair and tissue." The shape of proteins is key as it determines their function. "To understand how life works, we first need to understand the shape of proteins," Linke said and added that being able to predict their structure from their amino acid building blocks had "long been a dream." Baker meanwhile told reporters Wednesday was turning out to be "quite a unique, special day" for him. "I was sleeping when the phone rang, and I answered the phone and I heard the announcement," Baker said via phone link after the prize was announced in Stockholm. 'More powerful' The researcher said he was really excited about "all the ways in which protein design can now make the world a better place," while listing areas such as health, medicine as well as technology and sustainability. "Our new AI methods are much more powerful than our previous traditional scientific model methods," he said. Tuesday's physics prize honoured key breakthroughs in artificial intelligence (AI), going to American John Hopfield and British-Canadian Geoffrey Hinton, known as the Godfather of AI. Last year, the chemistry prize went to French-born Moungi Bawendi, Louis Brus of the United States and Russian-born Alexei Ekimov for developing tiny "quantum dots" used to illuminate TVs and lamps. Awarded since 1901, the Nobel Prizes honour those who have, in the words of prize creator and scientist Alfred Nobel, "conferred the greatest benefit on humankind". On Monday, the Medicine Prize was awarded to American scientists Victor Ambros and Gary Ruvkun for their discovery of microRNA and its role in how genes are regulated. Wednesday's chemistry prize will be followed by the highly watched literature and peace prizes to be announced on Thursday and Friday respectively. The economics prize wraps up the 2024 Nobel season on October 14. The winners will receive their prize, consisting of a diploma, a gold medal and a $1 million cheque, from King Carl XVI Gustaf at a formal ceremony in Stockholm on December 10, the anniversary of the 1896 death of scientist Alfred Nobel who created the prizes in his will.

What are proteins again? Nobel-winning chemistry explained

The Nobel Prize in Chemistry was awarded on Wednesday to three scientists who have help unravel some of the enduring secrets of proteins, the building blocks of life. While Demis Hassabis and John Jumper of Google's DeepMind lab used artificial intelligence techniques to predict the structure of proteins, biochemist David Baker managed to design totally new ones never seen in nature. These breakthroughs are hoped to lead towards numerous advances, from discovering new drugs to enzymes that decompose pollutants. Here is an explainer about the science behind the Nobel win. What are proteins? Proteins are molecules that serve as "the factories of everything that happens in our body," Davide Calebiro, a protein researcher at the UK's University of Birmingham, told AFP. DNA provides the blueprint for every cell. Proteins then use this information to do the work of turning that cell into something specific -- such as a brain cell or a muscle cell. Proteins are made up of 20 different kinds of amino acid. The sequence that these acids start out in determines what 3D structure they will twist and fold into. American Chemical Society president Mary Carroll compared how this works to an old-fashioned telephone cord. "So you could stretch out that telephone cord, and then you would just have a one-dimensional structure," she told AFP. "Then it would spring back" into the 3D shape, she added. So if chemists wanted to master proteins, they needed to understand how the 2D sequences turned into these 3D structures. "Nature already provides tens of thousands of different proteins, but sometimes we want them to do something they do not yet know how to do," said French biochemist Sophie Sacquin-Mora. What did AI do? The work of previous Nobel winners had demonstrated that chemists should be able to look at amino acid sequences and predict the structure they would become. But it was not so easy. Chemists struggled for 50 years -- there was even a biannual competition called the "Protein Olympics" where many failed the prediction test. Enter Hassabis and Jumper. They trained their artificial intelligence model AlphaFold on all the known amino acid sequences and corresponding structures. When given an unknown sequence, AlphaFold compares it with previous ones, gradually reconstructing the puzzle in three dimensions. After the newer generation AlphaFold2 crushed the 2020 Protein Olympics, the organizers deemed the problem solved. The model has now predicted the structure of almost all of the 200 million proteins known on Earth. What about the new proteins? US biochemist Baker started at the opposite end of the process. First, he designed an entirely new protein structure never seen in nature. Then, using a computer program called Rosetta that he had developed, he was able to work out the amino acid sequence that it started out as. To achieve this, Rosetta trawled through all the known protein structures, searching for short protein fragments similar to the structure it wanted to build. Rosetta then tweaked them and proposed a sequence that could end up as the structure. What is all this for? Mastering such fundamental and important little machines as proteins could have a vast number of potential uses in the future. "It allows us to better understand how life functions, including why some diseases develop, how antibiotic resistance occurs or why some microbes can decompose plastic," the Nobel website said. Making all-new proteins could lead to new nanomaterials, targeted drugs and vaccines, or more climate-friendly chemicals, it added. Asked to pick a favorite protein, Baker pointed to one he "designed during the pandemic that protects against the coronavirus". "I've been very excited about the idea of a nasal spray of little design proteins that would protect against all possible pandemic viruses," he told the Nobel ceremony via videolink. Calebiro emphasized how "transformative" this research would be. "I think this is just the beginning of a completely new era."

Nobel winners hope protein work will spur 'incredible' breakthroughs

The winners of the Nobel Prize in Chemistry for work revealing the secrets of proteins through artificial intelligence said Wednesday they hoped their research would "open the door to many incredible scientific breakthroughs". But they also warned of the dangers of AI, saying that while it had "extraordinary potential for good", it could also have negative effects if allowed to develop unchecked. Americans David Baker and John Jumper, together with Briton Demis Hassabis, were honored for cracking the code behind the structure of proteins, the building blocks of life. The jury hailed their work as holding "enormous potential" in a range of fields. At a press conference in London following the announcement, Jumper said the prize "represents the promise of computational biology". "We want to make the world a better place, and we have these incredibly powerful tools to do it. We're ultimately going to make people healthy because of the work we do with AI," he added. "I hope this is just a sign that we have opened the door to many incredible scientific breakthroughs." Their work could have particular importance in the field of drug discovery, noted Hassabis. "We think there's a huge potential there to revolutionize the way drug discovery is done, and try and shorten it down from almost a decade or more of work to... maybe months," he said. AI particularly lends itself to molecular biology because it is able to identify "patterns that we never see," said Jumper. "Medicine is hard because we don't understand how the body works in its extraordinary complexity," he added, calling their work "a step towards doing that". Late call Both Jumper and Hassabis said they had given up on getting the call, as the minutes ticked down to the announcement. "I don't think they had either of our numbers, funnily enough," said Hassabis. "So we got the call very late. We just thought 'it's not happening', or at least I did." Jumper said that he was still waiting with only 30 minutes to go. "I turned to my wife, and I said, 'Well, I guess it's not this year'. "And then 30 seconds later, I got this phone call from Sweden, and I absolutely could not believe it." Hassabis, 48, said his gaming background was the foundation of his computational expertise, and encouraged children to follow a similar path. "I would actually encourage kids to play games, but not just to play them, but the most important thing is to try and make them. "It's just a really fun way to get into the guts of how computers work," he added. However, Hassabis sounded a note of caution about AI, which he called "one of the most transformative technologies in human history". It has "the extraordinary potential for good... but also it can be used for harm," he said, warning "we have to really think very hard as these systems and techniques get more powerful". "I'm a big believer in human ingenuity," Hassabis added, arguing "given enough time and enough resources and enough smart people", humanity can solve many of its most vexing problems.

Nobel winners hope protein work will spur 'incredible' breakthroughs

London (AFP) - The winners of the Nobel Prize in Chemistry for work revealing the secrets of proteins through artificial intelligence said Wednesday they hoped their research would "open the door to many incredible scientific breakthroughs". But they also warned of the dangers of AI, saying that while it had "extraordinary potential for good", it could also have negative effects if allowed to develop unchecked. Americans David Baker and John Jumper, together with Briton Demis Hassabis, were honoured for cracking the code behind the structure of proteins, the building blocks of life. The jury hailed their work as holding "enormous potential" in a range of fields. At a press conference in London following the announcement, Jumper said the prize "represents the promise of computational biology". "We want to make the world a better place, and we have these incredibly powerful tools to do it. We're ultimately going to make people healthy because of the work we do with AI," he added. "I hope this is just a sign that we have opened the door to many incredible scientific breakthroughs." Their work could have particular importance in the field of drug discovery, noted Hassabis. "We think there's a huge potential there to revolutionise the way drug discovery is done, and try and shorten it down from almost a decade or more of work to... maybe months," he said. AI particularly lends itself to molecular biology because it is able to identify "patterns that we never see," said Jumper. "Medicine is hard because we don't understand how the body works in its extraordinary complexity," he added, calling their work "a step towards doing that". Late call Both Jumper and Hassabis said they had given up on getting the call, as the minutes ticked down to the announcement. "I don't think they had either of our numbers, funnily enough," said Hassabis. "So we got the call very late. We just thought 'it's not happening', or at least I did." Jumper said that he was still waiting with only 30 minutes to go. "I turned to my wife, and I said: 'Well, I guess it's not this year'. "And then 30 seconds later, I got this phone call from Sweden, and I absolutely could not believe it." Hassabis, 48, said his gaming background was the foundation of his computational expertise, and encouraged children to follow a similar path. "I would actually encourage kids to play games, but not just to play them, but the most important thing is to try and make them. "It's just a really fun way to get into the guts of how computers work," he added. However, Hassabis sounded a note of caution about AI, which he called "one of the most transformative technologies in human history". It has "the extraordinary potential for good... but also it can be used for harm," he said, warning "we have to really think very hard as these systems and techniques get more powerful". "I'm a big believer in human ingenuity," Hassabis added, arguing "given enough time and enough resources and enough smart people", humanity can solve many of its most vexing problems.

Baker, Hassabis, Jumper win 2024 Nobel chemistry prize for work on proteins

US nationals David Baker and John Jumper, together with Briton Demis Hassabis, shared the Nobel Prize in Chemistry on Wednesday for work revealing proteins' secrets through computing and artificial intelligence. The Nobel Prize in chemistry was awarded Wednesday to David Baker, Demis Hassabis and John Jumper for their work with proteins, the building blocks of life. Baker works at the University of Washington in Seattle, while Hassabis and Jumper both work at Google Deepmind in London. Baker designed a new protein in 2003 and his research group has since produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors, the Nobel committee said. Hassabis and Jumper created an artificial intelligence model that has been able to predict the structure of virtually all the 200 million proteins that researchers have identified, the committee added. Heiner Linke, Chair of the Nobel Committee for Chemistry, said scientists had long dreamt of predicting the three-dimensional structure of proteins. "Four years ago in 2020, Demis Hassabis and John Jumper managed to crack the code with skillful use of artificial intelligence. They made it possible to predict the complex structure of essentially any known protein in nature," Linke said. "Another dream of scientists has been to build new proteins to learn how to use nature's multi-tool for our own purposes. This is the problem that David Baker solved," he added. "He developed computational tools that now enable scientists to design spectacular new proteins with entirely novel shapes and functions, opening endless possibilities for the greatest benefit to humankind." Last year, the chemistry award went to three scientists for their work on quantum dots -- tiny particles just a few nanometers in diameter that can release very bright colored light and whose applications in everyday life include electronics and medical imaging. Six days of Nobel announcements opened Monday with Americans Victor Ambros and Gary Ruvkun winning the medicine prize. Two founding fathers of machine learning -- John Hopfield and Geoffrey Hinton -- won the physics prize. The awards continue with the literature prize on Thursday. The Nobel Peace Prize will be announced Friday and the economics award on Oct. 14. The prize carries a cash award of 11 million Swedish kronor ($1 million) from a bequest left by the award's creator, Swedish inventor Alfred Nobel. The laureates are invited to receive their awards at ceremonies on Dec. 10, the anniversary of Nobel's death.

Work on protein structure and design wins the 2024 chemistry Nobel

Proteins drive much of the chemistry underlying all life on Earth The 2024 Nobel Prize in chemistry has been awarded to David Baker "for computational protein design" and to Demis Hassabis and John Jumper "for protein structure prediction." Proteins are one of the foundational molecules of life. Baker, of the University of Washington in Seattle, "succeeded with the almost impossible feat of building entirely new kinds of proteins," the Royal Swedish Academy of Sciences in Stockholm said this morning in a press release. Hassabis and Jumper, both at Google DeepMind, "developed an AI model to solve a 50-year-old problem: predicting proteins' complex structures." The trio will split the prize of 11 million Swedish kroner, or about $1 million.

What are proteins again? Nobel-winning chemistry explained

Paris (AFP) - The Nobel Prize in Chemistry was awarded on Wednesday to three scientists who have help unravel some of the enduring secrets of proteins, the building blocks of life. While Demis Hassabis and John Jumper of Google's DeepMind lab used artificial intelligence techniques to predict the structure of proteins, biochemist David Baker managed to design totally new ones never seen in nature. These breakthroughs are hoped to lead towards numerous advances, from discovering new drugs to enzymes that decompose pollutants. Here is an explainer about the science behind the Nobel win. What are proteins? Proteins are molecules that serve as "the factories of everything that happens in our body," Davide Calebiro, a protein researcher at the UK's University of Birmingham, told AFP. DNA provides the blueprint for every cell. Proteins then use this information to do the work of turning that cell into something specific -- such as a brain cell or a muscle cell. Proteins are made up of 20 different kinds of amino acid. The sequence that these acids start out in determines what 3D structure they will twist and fold into. American Chemical Society president Mary Carroll compared how this works to an old-fashioned telephone cord. "So you could stretch out that telephone cord, and then you would just have a one-dimensional structure," she told AFP. "Then it would spring back" into the 3D shape, she added. So if chemists wanted to master proteins, they needed to understand how the 2D sequences turned into these 3D structures. "Nature already provides tens of thousands of different proteins, but sometimes we want them to do something they do not yet know how to do," said French biochemist Sophie Sacquin-Mora. What did AI do? The work of previous Nobel winners had demonstrated that chemists should be able to look at amino acid sequences and predict the structure they would become. But it was not so easy. Chemists struggled for 50 years -- there was even a biannual competition called the "Protein Olympics" where many failed the prediction test. Enter Hassabis and Jumper. They trained their artificial intelligence model AlphaFold on all the known amino acid sequences and corresponding structures. When given an unknown sequence, AlphaFold compares it with previous ones, gradually reconstructing the puzzle in three dimensions. After the newer generation AlphaFold2 crushed the 2020 Protein Olympics, the organisers deemed the problem solved. The model has now predicted the structure of almost all of the 200 million proteins known on Earth. What about the new proteins? US biochemist Baker started at the opposite end of the process. First, he designed an entirely new protein structure never seen in nature. Then, using a computer programme called Rosetta that he had developed, he was able to work out the amino acid sequence that it started out as. To achieve this, Rosetta trawled through all the known protein structures, searching for short protein fragments similar to the structure it wanted to build. Rosetta then tweaked them and proposed a sequence that could end up as the structure. What is all this for? Mastering such fundamental and important little machines as proteins could have a vast number of potential uses in the future. "It allows us to better understand how life functions, including why some diseases develop, how antibiotic resistance occurs or why some microbes can decompose plastic," the Nobel website said. Making all-new proteins could lead to new nanomaterials, targeted drugs and vaccines, or more climate-friendly chemicals, it added. Asked to pick a favourite protein, Baker pointed to one he "designed during the pandemic that protects against the coronavirus". Calebiro emphasised how "transformative" this research would be. "I think this is just the beginning of a completely new era."

Trio of scientists win chemistry Nobel for work on the structure of proteins

Scientists David Baker, Demis Hassabis and John M. Jumper won the 2024 Nobel Prize in chemistry for cracking the code for proteins' structures, the award-giving body said Wednesday. One half of the award going to Baker "for computational protein design" and the other half jointly to Hassabis and Jumper "for protein structure prediction," the body said in a statement. "Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins," the statement said, with co-laureates Hassabis and Jumper praised for having developed "an AI model to solve a 50-year-old problem: predicting proteins' complex structures." "These discoveries hold enormous potential," the statement said. "Life could not exist without proteins," it added. "That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind." The 2023 prize was awarded to three U.S.-based scientists for the discovery and synthesis of "quantum dots" -- tiny clusters of atoms that are used to create color in a range of devices such as flat-screen TVs. On Tuesday, a pair of artificial intelligence researchers won the physics prize for their work enabling machine learning with artificial neural networks, while on Monday, two U.S. scientists won the medical Nobel for their discovery of microRNA and its role in post-transcriptional gene regulation. Nobel announcements continue this week with the literature award on Thursday and the peace prize on Friday. The Nobel for economic sciences will be announced on Monday, closing out this year's awards.

Innovation

Trio of 'protein pioneers' use AI to win 2024 Nobel chemistry prize U.S. scientists David Baker and John Jumper, and Briton Demis Hassabis won the 2024 Nobel Prize in Chemistry on Wednesday for work on decoding the structure of proteins and creating new ones, yielding advances in areas such as drug development.

Nobel Prize in chemistry awarded to David Baker, Demis Hassabis and John Jumper for work on proteins

The laureates of the 2024 Nobel Prize in Chemistry are seen on a screen at the Royal Swedish Academy of Sciences in Stockholm, Sweden, Wednesday. From left are David Baker, Demis Hassabis and John Jumper. AFP-Yonhap The Nobel Prize in chemistry was awarded Wednesday to David Baker, Demis Hassabis and John Jumper for their work with proteins, the building blocks of life. Baker works at the University of Washington in Seattle, while Hassabis and Jumper both work at Google Deepmind in London. Baker designed a new protein in 2003 and his research group has since produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors, the Nobel committee said. Hassabis and Jumper created an artificial intelligence model that has been able to predict the structure of virtually all the 200 million proteins that researchers have identified, the committee added. Heiner Linke, Chair of the Nobel Committee for Chemistry, said scientists had long dreamt of predicting the three-dimensional structure of proteins. "Four years ago in 2020, Demis Hassabis and John Jumper managed to crack the code with skillful use of artificial intelligence. They made it possible to predict the complex structure of essentially any known protein in nature," Linke said. "Another dream of scientists has been to build new proteins to learn how to use nature's multi-tool for our own purposes. This is the problem that David Baker solved," he added. "He developed computational tools that now enable scientists to design spectacular new proteins with entirely novel shapes and functions, opening endless possibilities for the greatest benefit to humankind." Last year, the chemistry award went to three scientists for their work on quantum dots -- tiny particles just a few nanometers in diameter that can release very bright colored light and whose applications in everyday life include electronics and medical imaging. Six days of Nobel announcements opened Monday with Americans Victor Ambros and Gary Ruvkun winning the medicine prize. Two founding fathers of machine learning -- John Hopfield and Geoffrey Hinton -- won the physics prize . The awards continue with the literature prize on Thursday. The Nobel Peace Prize will be announced Friday and the economics award on Oct. 14. The prize carries a cash award of 11 million Swedish kronor ($1 million) from a bequest left by the award's creator, Swedish inventor Alfred Nobel. The laureates are invited to receive their awards at ceremonies on Dec. 10, the anniversary of Nobel's death. (AP)

Nobel Prize in chemistry awarded to three scientists for work on proteins

The prize was awarded Wednesday for research that cracked the code of proteins. Winner The Nobel Prize in Chemistry was awarded to David Baker at the University of Washington and Demis Hassabis and John M. Jumper of Google DeepMind. Discovery The prize was awarded to scientists who cracked the code of proteins. Hassabis and Jumper used artificial intelligence to predict the structure of proteins, one of the toughest problems in biology. Baker created computational tools to design novel proteins with shapes and functions that can be used in drugs, vaccines and sensors. Quote "One of the discoveries being recognized this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities," said Heiner Linke, chair of the Nobel Committee for Chemistry. "It's only your imagination that sets the limit for what you can do here," said Johan Åqvist, professor of cell and molecular biology at Uppsala University. Skip to end of carousel Tracking the 2024 Nobel Prizes A view of the gold Nobel Prize medal. (Fernando Vergara/AP) The Nobel Prizes shine a spotlight on some of the world's most prominent thinkers and innovators. We're tracking the 2024 Nobel Prize winners here. End of carousel Last year's winner Last year's prize honored three scientists who discovered and developed tiny, submicroscopic nanoparticles called quantum dots, used to bring light to TV screens and LED lamps. The prize was shared by Moungi Bawendi of the Massachusetts Institute of Technology, Louis Brus of Columbia University and Alexei Ekimov of Nanocrystals Technology in New York. Next Nobel prize to be awarded The chemistry Nobel is the last science Nobel of 2024. On Thursday, the Nobel Prize in literature will be announced. This year's winners For a full list of who has won the Nobel prizes this year, The Washington Post is tracking the winners here.

Nobel Prize in Chemistry 2024

The 2024 Nobel Prize in Chemistry has been awarded to David Baker "for computational protein design" and to Demis Hassabis and John M. Jumper "for protein structure prediction". Proteins are life's essential building blocks, nature's most ingenious molecular machines and the basis of all living organisms. Hassabis and Jumper have developed a series of artificial intelligence models to address the decades-long structural biology problem of how to predict the complex 3D structures of proteins solely from their linear amino acid sequences, while Baker has dedicated his scientific career to designing and constructing proteins that are not, and even can not, be found in nature. In recognition of this award, Nature Portfolio presents a collection of research, review and opinion articles that celebrates both contributions by the awardees and the advances they have inspired.

Nobel Prize in chemistry awarded to David Baker, Demis Hassabis and John Jumper for work on proteins

STOCKHOLM -- The Nobel Prize in chemistry was awarded Wednesday to scientists David Baker, Demis Hassabis and John Jumper for their work with proteins. Baker works at the University of Washington in Seattle, while Hassabis and Jumper both work at Google Deepmind in London. Hans Ellegren, secretary general of the Royal Swedish Academy of Sciences that decides on the winner, announced the prize. Baker designed a new protein in 2003 and his research group has since produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors, the Nobel committee said. Hassabis and Jumper created an artificial intelligence model that has been able to predict the structure of virtually all the 200 million proteins that researchers have identified, the committee added. Last year, the chemistry award went to three scientists for their work on quantum dots -- tiny particles just a few nanometers in diameter that can release very bright colored light and whose applications in everyday life include electronics and medical imaging. Six days of Nobel announcements opened Monday with Americans Victor Ambros and Gary Ruvkun winning the medicine prize. Two founding fathers of machine learning -- John Hopfield and Geoffrey Hinton -- won the physics prize on Tuesday. The awards continue with the literature prize on Thursday. The Nobel Peace Prize will be announced Friday and the economics award on Oct. 14. The prize carries a cash award of 11 million Swedish kronor ($1 million) from a bequest left by the award's creator, Swedish inventor Alfred Nobel. The laureates are invited to receive their awards at ceremonies on Dec. 10, the anniversary of Nobel's death.

British Nobel Prize winners on moment they were told they had won scientific prize

Please use Chrome browser for a more accessible video player "I don't think the committee had our phone numbers," said Sir Demis Hassabis. He found out he'd won the Nobel Prize for chemistry - but the Swedish awards committee had a hard job letting him know. They ended up phoning Sir Hassabis' wife on Microsoft Teams, who was working and repeatedly ignored them. "Eventually about the third or fourth call, she decided to answer it," he said. Google DeepMind boss Sir Hassabis and his colleague Dr John Jumper, as well as the US' Dr David Baker, have just won the Nobel Prize for chemistry for their work in artificial intelligence and biology. Sir Hassabis and Dr Jumper, both based in London, won for their groundbreaking work in predicting protein structures. The AI model they developed, AlphaFold, can accurately predict the structure of millions of proteins, which are found in every living thing around us. Their work could have a "truly huge" impact in developing medicines, vaccines and improving human health, according to the Nobel committee. "An experiment that takes about a year for a PhD student to do, AlphaFold will predict the answer in a few minutes," said Dr Jumper, talking to Sky News after a whirlwind day. He'd expected to spend Wednesday just "writing a bit of code", instead he was in back-to-back interviews with the world's media and just like Sir Hassabis, Dr Jumper was taken aback when he found out he'd just won the Nobel Prize. "I knew that the call [to say you'd won] went about an hour before the press conference," he said. "It had got to 30 minutes before the press conference and I said, 'Okay, not this year'." Dr Jumper is 39 years old, making him the youngest chemistry laureate in 70 years. "After I told my wife, 'Well, not this year', I got a phone call from Sweden and it was... exceptional and unbelievable. "The look on my wife's face was my favourite part... Other than getting the Nobel Prize." Sir Hassabis and Dr Jumper announced AlphaFold2 in 2020 and have now been able to predict the structure of virtually all the 200 million proteins that researchers have identified, according to the Royal Swedish Academy of Sciences who award the prizes. Read more from Sky News: Boki the bear has pioneering brain surgery Man hunted over rape of 18-year-old Because of their work, scientists now better understand things like antibiotic resistance and have even created images of enzymes that can decompose plastic. The potential for their AI tool to change the world is not lost on Dr Jumper. "As excited as I've been to receive the Nobel [Prize], I'll be just as excited when the first Nobel is given for discoveries that used AlphaFold - when it's the basis of other people's Nobel worthy work," he said. However, there are some people concerned about the risks of technology like AlphaFold, the worry is that this kind of technology could be used to create things like bioweapons or to enhance viruses. This year, a group of scientists, including Dr Baker, called for safeguards to be built into AI technology working with proteins. "We just need to be cautiously optimistic about what we're doing," said Sir Hassabis. "Being bold with applying it to the good use cases, but also trying to mitigate where we can the risks." The winning trio will now share a prize of 11 million Swedish kroner (around £810,000).

Nobel Prize in chemistry awarded to scientists for work on proteins

STOCKHOLM -- The Nobel Prize in chemistry was awarded Wednesday to David Baker, Demis Hassabis and John Jumper for their work with proteins. Baker works at the University of Washington in Seattle, while Hassabis and Jumper both work at Google Deepmind in London. Hans Ellegren, secretary general of the Royal Swedish Academy of Sciences that decides on the winner, announced the prize. In 2003, Baker designed a new protein and since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors, the Nobel committee said. Hassabis and Jumper created an artificial intelligence model that has been able to predict the structure of virtually all the 200 million proteins that researchers have identified, the committee added. Last year, the chemistry award went to three scientists for their work on quantum dots -- tiny particles just a few nanometers in diameter that can release very bright colored light and whose applications in everyday life include electronics and medical imaging. Six days of Nobel announcements opened Monday with Americans Victor Ambros and Gary Ruvkun winning the medicine prize. Two founding fathers of machine learning -- John Hopfield and Geoffrey Hinton -- won the physics prize. The awards continue with the literature prize on Thursday. The Nobel Peace Prize will be announced Friday and the economics award on Oct. 14. The prize carries a cash award of 11 million Swedish kronor ($1 million) from a bequest left by the award's creator, Swedish inventor Alfred Nobel. The laureates are invited to receive their awards at ceremonies on Dec. 10, the anniversary of Nobel's death.

2024 Nobel Prize in Chemistry: Who are David Baker, Demis Hassabis and John Jumper?

2024 Nobel Prize in Chemistry Winner: The 2024 Nobel Prize in Chemistry honored David Baker for computational protein design and Demis Hassabis alongside John M. Jumper for predicting protein structures using AI. Their groundbreaking work on proteins promises significant advancements in pharmaceuticals, vaccines, and understanding biological mechanisms.The 2024 Nobel prize in Chemistry has been awarded to David Baker "for computational protein design" and Demis Hassabis and John M. Jumper "for protein structure prediction." This year's Nobel prize laureates in chemistry cracked the code for proteins' amazing structures. The Nobel Prize in Chemistry 2024 is about proteins, life's ingenious chemical tools. Chemistry laureate David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. His co-laureates Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins' complex structures. These discoveries hold enormous potential. David Baker was born in 1962 in Seattle. He completed his PhD in 1989 from University of California, USA. He is a professor at University of Washington, Seattle. Baker is a member of the United States National Academy of Sciences and the director of the University of Washington's Institute for Protein Design. He has co-founded more than a dozen biotechnology companies and was included in Time magazine's inaugural list of the 100 Most Influential People in health in 2024. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors. Demis Hassabis was born in 1976 in London. Hassabis did his PhD in 2009 from University College London, UK. He is the chief executive officer and co-founder of DeepMind and Isomorphic Labs, and a UK Government AI Advisor. ALSO READ: David Baker, Demis Hassabis and John M. Jumper awarded Chemistry Nobel Prize He has won many prestigious awards for his work on AlphaFold including the Breakthrough Prize, the Canada Gairdner International Award, and the Lasker Award. In 2017 he was appointed a CBE and listed in the Time 100 most influential people list. In 2024 he was knighted for services to AI. Born 1985 in Little Rock, USA, John Jumper completed his PhD from University of Chicago. He is a senior research scientist at Google DeepMind, London, UK. Jumper and his colleagues created AlphaFold, an artificial intelligence (AI) model to predict protein structures from their amino acid sequence with high accuracy. He won the 2024 Nobel Prize in Chemistry. Jumper has stated that the AlphaFold team plans to release 100 million protein structures. In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic. The scientific journal Nature included Jumper as one of the ten "people who mattered" in science in their annual listing of Nature's 10 in 2021. The Nobel Prize in Chemistry 2023 was awarded to Moungi G. Bawendi, Louis E. Brus and Aleksey Yekimov "for the discovery and synthesis of quantum dots".

University of Chicago grad, 2 other scientists awarded Nobel Prize in chemistry for work on proteins, building blocks of life

CHICAGO (CBS/AP) -- A University of Chicago alumnus was awarded a share of the Nobel Prize in chemistry on Wednesday, along with two other scientists, for their work to discover powerful techniques to predict and even design novel proteins -- the building blocks of life. John Jumper, who received his master's degree in 2012 and his Ph.D. from the University of Chicago in 2017, shared the prize with Dennis Hassabis -- who works with Jumper at Google DeepMind, a British-American artificial intelligence research laboratory based in London -- and David Baker, who works at the University of Washington in Seattle, for their work on "protein structure prediction." Their work used advanced technologies, including machine learning, and holds the potential to transform how new drugs are made. "It's absolutely extraordinary," Jumper said in a statement on the University of Chicago website. "I've been a computational biologist a long time, and I like to say in talks: we need this to work. We need computation to solve the problems of biology, and I just love that it's starting to work." Heiner Linke, chair of the Nobel Committee for Chemistry, said the award honored research that unraveled long-standing scientific mysteries. "That was actually called a grand challenge in chemistry, and in particular in biochemistry, for decades. So, it's that breakthrough that gets awarded today," he said. Jumper and Hassabis co-invented the AlphaFord System at Google DeepMind. The Nobel committee wrote they "have utilised artificial intelligence to successfully solve a problem that chemists wrestled with for over 50 years: predicting the three-dimensional structure of a protein from a sequence of amino acids." Jumper is the 19th person affiliated with the University of Chicago to receive a Nobel Prize for chemistry, and the 100th scholar associated with the university to receive a Nobel Prize overall. Proteins are complex molecules with thousands of atoms that twist, turn, loop and spiral in a countless array of shapes. The shape of a protein determines its biological function. For decades, scientists have dreamed of being able to efficiently design and build new proteins. Baker, whose work has received funding from the National Institutes of Health since the 1990s, created a computer program called Rosetta that helped analyze information about existing proteins in comprehensive databases in order to build new proteins that don't exist in nature. "It seems that you can almost construct any type of protein now with this technology," said Johan Åqvist of the Nobel committee. Hassabis and Jumper created an artificial intelligence model that has been able to predict the structure of virtually all the 200 million proteins that researchers have identified, the committee added. The duo "managed to crack the code. With skillful use of artificial intelligence, they made it possible to predict the complex structure of essentially any known protein in nature," Linke said. The ability to custom design new proteins -- and better understand existing proteins -- could enable researchers to create new kinds of medicines and vaccines. It could also allow scientists to design new enzymes to break down plastics or other waste materials, and to design fine-tuned sensors for hazardous materials. "I think there's fantastic prospects for making better medicines -- medicines that are smarter, that only work in the right time and place in the body," Baker told The Associated Press. One example is a potential nasal spray that could slow or stop the rapid spread of specific viruses, such as COVID-19, he said. Another is a medicine to disrupt the cascade of symptoms known as cytokine storm. "That was always the holy grail. If you could figure out how protein sequences folded into their particular structures, then it might be possible to design protein sequences to fold into previously never seen structures that might be useful for us," said Jon Lorsh of the NIH. Baker said Hassabis and Jumper's artificial intelligence work gave his team a huge boost. "The breakthroughs made by Demis and John on protein structure prediction really highlighted to us the power that AI could have. And that led us to apply these AI methods to protein design and that has greatly increased the power and accuracy," he said. Baker told the AP he found out he won the Nobel during the early hours of the morning alongside his wife, who immediately started screaming. "So it was a little deafening, too," he said. Hassabis said in a statement that "receiving the Nobel Prize is the honor of a lifetime." One of Britain's leading tech figures, he co-founded the AI research lab DeepMind in 2010, which was later acquired by Google. DeepMind's breakthroughs include developing an AI system that mastered the Chinese game of Go and was able to defeat the game's human world champion much faster than expected. Jumper said in the same statement that it was an honor to be "recognized for delivering on the long promise of computational biology to help us understand the protein world and to inform the incredible work of experimental biologists." "It is a key demonstration that AI will make science faster and ultimately help to understand disease and develop therapeutics," Jumper said. Baker gets half of the prize money of 11 million Swedish Kronor ($1 million) while Hassabis and Jumper share the other half. It's the second Nobel prize that has gone to someone with links to Google. Physics prize winner Geoffrey Hinton also previously worked at the tech company, but later quit so he could speak more freely about the potential dangers of artificial intelligence. Last year, the chemistry award went to three scientists for their work on quantum dots -- tiny particles just a few nanometers in diameter that can release very bright colored light and whose applications in everyday life include electronics and medical imaging. Six days of Nobel announcements opened Monday with Americans Victor Ambros and Gary Ruvkun winning the medicine prize. Two founding fathers of machine learning -- Hinton and John Hopfield -- won the physics prize. The awards continue with the literature prize on Thursday. The Nobel Peace Prize will be announced Friday and the economics award on Oct. 14. The prize money comes from a bequest left by the award's creator, Swedish inventor Alfred Nobel. The laureates are invited to receive their awards at ceremonies on Dec. 10, the anniversary of Nobel's death.

Nobel Prize in chemistry goes to 3 scientists for predicting, creating proteins

By Claire Moses, Cade Metz and Teddy Rosenbluth NYT News Service/Syndicate Stories The Nobel Prize in chemistry was awarded Wednesday to three scientists for discoveries that show the potential of advanced technology, including artificial intelligence, to predict the shape of proteins, life's chemical tools, and to invent new ones. The laureates are: Demis Hassabis and John Jumper of Google DeepMind, who used AI to predict the structure of millions of proteins; and David Baker of the University of Washington, who used computer software to invent a new protein. The impact of the work of this year's laureates is "truly huge," Johan Aqvist, a member of the Nobel Committee for Chemistry, said Wednesday. "In order to understand how proteins work, you need to know what they look like, and that's what this year's laureates have done." That task once took months, or even decades. But AI models like AlphaFold make it possible to do that in a few hours or even minutes. That speed has real-world applications. AlphaFold has been cited in scientific studies more than 20,000 times, and biochemists have used the technology to accelerate the discovery of medicines. "We can draw a straight line from what we do to people being healthy," Jumper said. It could also lead to new biological tools such as enzymes that efficiently break down plastic bottles and convert them into materials that are easily reused and recycled. Wednesday's prize was the second this week to involve AI, highlighting the technology's growing significance in scientific research. This year's Nobel Prize in chemistry also offered a reminder of how AI could be co-opted by bad actors. "Of course, it's a dual-purpose technology," Hassabis said at a news conference. "It has extraordinary potential for good, but also it can be used for harm." Some worry that this technology may be used to create new viruses or toxic substances that could be used in biological attacks. Baker was one of more than 90 scientists who signed an agreement this year that sought to regulate the equipment needed to manufacture new bioweapons, an effort to ensure that their AI research will not cause harm. Cracking the code Proteins and enzymes are the microscopic mechanisms that drive the behavior of viruses, bacteria, the human body and all other living things. They begin as strings of chemical compounds, before twisting and folding into three-dimensional shapes that define what they can and cannot do. Pinpointing the precise shape of individual proteins was a laborious task for many years, and scientists had struggled for over 50 years to solve what was called "the protein folding problem." Hassabis was born in London, where his parents -- one a Greek Cypriot, the other a Singaporean -- ran a toy store. As a teenager, he was the second-highest-ranked chess player under 14 in the world, and he began designing video games professionally before attending college. After completing a computer science degree at the University of Cambridge, he founded a video game company, then returned to academia for a doctorate in neuroscience. He and a fellow academic, Shane Legg, and a childhood friend, Mustafa Suleyman, founded an AI startup called DeepMind in 2010. About four years later, Google acquired it for $650 million. DeepMind's stated goal was to build artificial general intelligence, a machine that can do anything the human brain can do. It also explored other technologies that could help reach that goal and solve particular scientific problems. One of those technologies was AlphaFold. AlphaFold is built using a mathematical system called a neural network. With neural networks, computers can analyze vast amounts of data to learn to perform many tasks that were once beyond their capacity. Such systems drive facial and voice recognition, as well as online chatbots. They can also be used to predict the shape of a protein in the human body, which can determine how other molecules will bind or physically attach to it. This is one-way drugs are developed: A drug binds to particular proteins in the body and alters their behavior. Jumper, the youngest chemistry laureate in over 70 years, was born in the United States. After finishing an undergraduate degree at Vanderbilt University and a master's degree at the University of Cambridge, he earned a doctorate in theoretical chemistry at the University of Chicago. He joined DeepMind as a researcher in 2017 after Google had acquired the lab. Alongside Hassabis and others, he soon began work on what would become AlphaFold. In 2018, a DeepMind team led by Jumper entered a global competition called the Critical Assessment of Structure Prediction, a 25-year effort to solve the protein-folding problem. Their technology outperformed all other competitors. Many scientists had assumed that a protein-folding breakthrough was still years away. Then in 2020, when the Google researchers unveiled an update of the technology, AlphaFold2, at the next contest, they showed that it had fully cracked the problem, predicting shapes with an accuracy level that rivaled physical experiments. "We just woke up that day and knew: This is a different biology," said Mohammed AlQuraishi, a researcher at Columbia University who studies AI and protein folding. With AlphaFold2, the Google team was able to calculate the structure of all human proteins, the Nobel committee said, before eventually predicting "the structure of virtually all the 200 million proteins that researchers have so far discovered when mapping Earth's organisms." 'I love all proteins' Baker's work preceded the emergence of the latest AI models and centered on protein creation. A Seattle native, Baker earned his undergraduate degree from Harvard University in 1984 and a doctorate in biochemistry doctorate from the University of California, Berkeley, in 1989. He now serves as the director of the Institute for Protein Design and a professor of biochemistry at the University of Washington. In 2003, Baker and his colleagues created the first entirely new protein: a molecule called Top7 that was useless but symbolic. "Until then, really the only proteins that were known were the ones that came down through millions or billions of years of evolution," he said in an interview with The New York Times. The researchers started with their desired protein shape and used a computer model called Rosetta, which searches databases of existing proteins to find a sequence of amino acids that might create such a structure. He remembered the "amazing moment" when the protein he had created with bacteria from the proposed amino acid sequence showed almost the exact same structure as the one from his model. This work "opened up a completely new world of protein structures that we had never seen before," Aqvist of the Nobel committee said. Baker realized that if he could create a novel protein structure, he should also be able to create more sophisticated proteins "that actually do things," like break up the amyloid fibrils that are thought to be involved in Alzheimer's disease. In recent years, his work has dovetailed with the kind of research explored by Hassabis and Jumper at DeepMind, as his lab uses neural networks to not just predict the shapes of proteins but also generate blueprints for new proteins. It is another form of what researchers and tech companies call generative AI. His lab's proteins -- created with a more advanced iteration of Rosetta -- have already been the basis of several potential medical treatments, such as an antiviral nasal spray for COVID-19 and a medication for celiac disease. A COVID vaccine, SKYCovione, based on his one of his lab's proteins, was approved for use in South Korea in 2022. Baker is also a co-founder of more than 20 biotechnology companies. When asked by a journalist after the ceremony if he had a favorite protein, he said: "I love all proteins. I don't want to pick favorites." Screams and missed calls When the committee informed the laureates Wednesday, Baker was sleeping. "I answered the phone and I heard the announcement, and my wife began screaming very loudly so I couldn't really hear very well," he told reporters. He said "he turned down 100 calls" while he was on the phone with the Nobel committee. Jumper had heard that the Nobel committee would typically call laureates an hour before the news conference. With just 30 minutes until the announcement, he turned to his wife and said, "I guess it's not this year." After the call finally came, Jumper recorded a video of himself sharing the news with colleagues over a video call. They hugged and cheered, appearing in little squares on his computer screen. "Glad you guys are all caught up now," he said. In an interview shortly after the prize was announced, Hassabis said the news hadn't "really sunk in." "I had a whole day of normal work ahead of me, but I guess those plans will have to change now," he said. Who received the 2023 Nobel Prize in chemistry? The prize went to Moungi G. Bawendi, Louis E. Brus and Alexei I. Ekimov for discovering and developing quantum dots, semiconductors made of tightly squeezed particles that are expected to lead to advances in electronics, solar cells and encrypted quantum information. Who else has received a Nobel Prize in the sciences this year? ▪ On Monday, the prize in physiology or medicine went to Victor Ambros and Gary Ruvkun for their discovery of microRNA, which helps determine how cells develop and function. ▪ On Tuesday, the prize in physics was awarded to John J. Hopfield and Geoffrey E. Hinton for discoveries that helped computers learn more in the way that the human brain does, providing the building blocks for developments in artificial intelligence. When will the other Nobel Prizes be announced? ▪ The Nobel Prize in literature will be awarded Thursday by the Swedish Academy in Stockholm. Last year, Jon Fosse of Norway was honored for plays and prose that gave "voice to the unsayable." ▪ The Nobel Peace Prize will be awarded Friday by the Norwegian Nobel Institute in Oslo. Last year, Narges Mohammadi, an activist in Iran was recognized "for her fight against the oppression of women in Iran and her fight to promote human rights and freedom for all." Mohammadi is serving a 10-year sentence in an Iranian prison where her attorneys have raised concerns about her well-being. ▪ The Nobel Memorial Prize in Economic Sciences will be awarded Monday by the Royal Swedish Academy of Sciences in Stockholm. Last year, Claudia Goldin was awarded for her research uncovering the reasons for gender gaps in labor force participation and earnings. All of the prize announcements are streamed live by the Nobel Prize organization.