Without Code for DeepMind's Protein AI, One Lab Wrote Its Own

For biologists who study the structure of proteins, the recent history of their field is divided into two epochs: before CASP14, the 14th biennial round of the Critical Assessment of Protein Structure conference, and after. In the decades before, scientists had spent years slowly chipping away at the problem of how to predict the structure of a protein from the sequence of amino acids that it comprises. After CASP14, which took place in December 2020, the problem had effectively been solved, by researchers at the Google subsidiary DeepMind. A research company focused on a branch of artificial intelligence known as "deep learning," DeepMind had previously made headlines by building an AI system that beat the Go world champion. But their success at protein structure prediction, which they achieved using a neural network they call AlphaFold2, represented the first time they had built a model that could solve a problem of real scientific relevance.