If you want to understand the universe, you can start by reading the greats: Feynman, Weinberg, Curie, Hofstadter, Kant, Spinoza, Turing, and all the brilliant scientists and philosophers who advanced the frontiers of human knowledge and on whose shoulders modern civilization stands.
But in the course of that journey you will also discover that, despite all this incredible progress, there are surprising limits to the things we know. We are still nowhere near answering some of the biggest questions, like the nature of time, consciousness, or the very fabric of reality.
To make progress towards answering these profound questions, new tools and approaches will almost certainly be needed. Artificial intelligence (AI) is one such tool, and we’ve always believed that it could, in fact, be the ultimate tool to help accelerate scientific discovery.
We’ve been working toward this goal for more than 20 years. DeepMind (now Google DeepMind) was founded with the mission of responsibly building Artificial General Intelligence (AGI), a system that can perform almost any cognitive task at a human level. The immense promise of such systems is that they could then be used to advance our understanding of the world around us, and help us solve some of society’s greatest challenges.
In 2016, after we’d developed AlphaGo, the first AI system to beat a world champion at the complex game of Go, and witnessed its famously creative Move 37 in Game 2, we felt the techniques and methods were in place to start using AI to tackle important open problems in science.
At the top of that list was the 50-year-old grand challenge of protein folding. Proteins are the building blocks of life. They underpin every biological process in every living thing, from the fibers in your muscles to the neurons firing in your brain. Each protein is specified by its amino acid sequence (roughly its genetic sequence) and spontaneously folds into a three-dimensional structure. The shape of a protein is important because it tells you a lot about what the protein does—information that’s critical for things like understanding diseases, and drug discovery.
Predicting the 3D shape of a protein directly from its 1D amino acid sequence is known as the “protein folding problem.” It’s incredibly challenging because there are estimated to be more potential ways that an average protein can fold than there are atoms in the universe.
Finding a protein’s structure…
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