Google DeepMind wins joint Nobel Prize in Chemistry for protein prediction AI
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.
Hassabis and Jumper created AlphaFold, an AI tool that in 2020 solved a problem scientists have been wrestling with for decades: predicting the three-dimensional structure of a protein from a sequence of amino acids. The tool has since been used to predict the shapes of all proteins known to science. Their latest model, AlphaFold 3, can predict the structures of DNA, RNA, and molecules like ligands, which are essential to drug discovery. DeepMind has also released the source code and database of its results to scientists for free.
Baker has created several AI tools for designing and predicting the structure of proteins, such as a family of tools called Rosetta. In 2022, his lab created an open-source AI tool called ProteinMPNN that could help researchers discover previously unknown proteins and design entirely new ones. It helps researchers who have an exact protein structure in mind find amino acid sequences that fold into that shape. Most recently in late September, Baker’s lab unveiled that they had developed custom molecules that allow scientists to precisely target and eliminate proteins that are associated with diseases in living cells.
“[Proteins] evolved over the course of evolution to solve the problems that organisms faced during evolution. But we face new problems today, like covid. If we could design proteins that were as good at solving new problems as the ones that evolved during evolution are at solving old problems, it would be really, really powerful,” Baker told MIT Technology Review in 2022.
