Increasingly, computer science, and in particular, Artificial Intelligence are becoming incredibly ubiquitous. To the extent, Nobel prizes in basic sciences such as physics and chemistry are getting awarded to people who have used AI to further their craft. Such as this year’s Nobel prize for chemistry:
“…some of the best brains in chemistry do not only make molecules, they make computer models too.
One half of the award went to David Baker, a biochemist at the University of Washington, for his work on designing new proteins using computers. The other half was shared between John Jumper and Demis Hassabis from DeepMind, Google’s artificial-intelligence (AI) company, for their development of AI models capable of predicting three-dimensional protein structure, a long-standing grand challenge in biochemistry.
Dr Baker has long been considered a favourite to win. The choice of Drs Hassabis and Jumper, though, came as something of a surprise. But this has been AI’s year. The day before the chemistry announcement, the prize for physics was awarded for the development of the neural networks that underpin artificial intelligence models such as those DeepMind pioneered (a subject, some argued, that hardly counts as physics at all).”
Dr Baker’s work involved understanding the structure of the protein which determines its function and the challenge arose from the structure having numerous variants based on the permutations in which it could fold:
“Given the near-limitless number of configurations into which a protein can fold—by some estimates, as many as 10300 for a single complex protein—even computers had limited success. DeepMind’s AI-based AlphaFold 1 and 2 models, made public in 2018 and 2020 respectively, were the first to even get close. AlphaFold 2 now has a database of more than 200m protein structure predictions, with a prediction accuracy approaching 90%…..DeepMind says that some 2m scientists already use it in their research. AlphaFold 3, released in May, goes beyond proteins to predict the structure of a host of other biomolecules, such as DNA, as well as small molecules that might function as drugs. It can also predict how different molecules with different structures fit together, such as how a virus’s spike protein might interact with antibodies and sugars found in the body.
…By choosing, for the first time, to honour work performed with an AI model, the committee has opened the door for more such prizes in the future. That is just as well; AI has been seeping into all areas of science for some time now, as Dr Baker illustrated when he was phoned up during the committee’s press conference. He said that AlphaFold has inspired him to make generative AI models that can design new proteins. “Our new AI methods are much more powerful,” he said, sounding happy and a little tired. If recent history is anything to go by, researchers will be repeating that line for years to come.”
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