This week, AI has made a significant impact on the Nobel Prizes. John Hopfield and Geoffrey Hinton won the Nobel Prize in Physics for their groundbreaking work in machine learning, which laid the foundation for today’s AI. Just yesterday, Demis Hassabis and John Jumper of Google DeepMind, along with David Baker from The University of Washington, were awarded the Nobel Prize in Chemistry. They discovered methods to predict and design new proteins, which could revolutionize how therapeutic drugs are made.

Heiner Linke, the chair of the Nobel Committee for Chemistry, called this achievement a 50-year dream come true. Back in 1973, chemist Christian Anfinsen had postulated that it would be possible to predict the shape of a protein based on its DNA sequence. This dream is now a reality thanks to the work of these researchers.

David Baker started making progress in 2003 by designing new proteins using the 20 different amino acids found in proteins. Then, Hassabis and Jumper made a major breakthrough in 2020 with their machine learning model AlphaFold2. This model can predict the structure of virtually all known proteins.

The award is a full-circle moment for Hassabis, who started his AI journey by teaching computers to master games like Go but always aimed higher. His vision for “AI Scientists” includes machines that can generate and test hypotheses about diseases.

DeepMind began working on protein folding in 2016 and has since made significant advancements. The latest version, AlphaFold 3, can predict protein interactions with DNA, RNA, and other molecules with high accuracy.

AI’s impact on scientific discovery is already being felt. For instance, an AI model from Cambridge scientists showed 82% accuracy in predicting Alzheimer’s disease progression. Several AI-discovered drugs have also moved into clinical trials.

While AI in scientific research is promising, it’s not without challenges. Issues like bias need to be addressed. However, the early accomplishments are worth celebrating.

AI’s Big Wins: Nobel Prizes Reflect AI’s Growing Power in Science

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