The Nobel Prize in Chemistry Goes to the Team Whose AI Predicts the Shape of Proteins

Demis Hassabis and John Jumper of Google DeepMind share the prize with David Baker of the University of Washington. AlphaFold, released four years ago, has now predicted the structures of 200 million proteins and been used by two million scientists.

A day after the physics prize went to the founders of machine learning, the Nobel Prize in Chemistry went this morning to people who used it. Demis Hassabis and John Jumper of Google DeepMind share half the prize for AlphaFold, the program that in 2020 solved the fifty-year problem of predicting a protein's shape from its chemical sequence. David Baker of the University of Washington receives the other half for a related feat: designing entirely new proteins that don't exist in nature.

The academy's citation was plain about the scale. AlphaFold has now predicted the structures of essentially all 200 million known proteins, and its database has been used by more than two million researchers in 190 countries — to understand antibiotic resistance, to design enzymes that break down plastic, to study diseases whose proteins had never been seen. Work that once took a doctoral student's entire degree now takes minutes.

Hassabis, a former chess prodigy and video-game designer who founded DeepMind in 2010 with the goal of solving intelligence and then using it to solve everything else, said the prize was "the honor of a lifetime" and that AlphaFold is what he had always hoped AI would be for. Jumper, at 39, is among the youngest chemistry laureates in decades.

Two Nobels in two days for AI is not a coincidence so much as a verdict: the committees are saying the technology has done science, not just promised to. It is worth remembering that both prizes honor work whose value is not in dispute, at a moment when much of the rest of the field's is.