striking evidence that artificial intelligence can provide profound scientific breakthroughs came with the unveiling of a program called AlphaFold by Google DeepMind. In 2016 researchers at the company had scored a big success with AlphaGo, ansystem which, having essentially taught itself the rules of Go, went on to beat the most highly rated human players of the game, sometimes by using tactics no one had ever foreseen.
Because of the importance of proteins’ three-dimensional structure there is an entire sub-discipline largely devoted to it: structural biology. It makes use of all sorts of technology to look at proteins through nuclear-magnetic-resonance techniques or by getting them to crystallise and blasting them with-rays. Before AlphaFold over half a century of structural biology had produced a couple of hundred thousand reliable protein structures through these means.
Such computational ambitions are not new. Large-scale computing, machine learning and drug design were already coming together in the 2000s, says Vijay Pande, who was a researcher at Stanford University at the time. This was in part a response to biology’s fire hose of new findings: there are now more than a million biomedical research papers published every year.
Demis Hassabis, the boss of DeepMind, points out that biology itself can be thought of as “an information processing system, albeit an extraordinarily complex and dynamic one”. In a post on Medium, Serafim Batzoglou, the chief data officer at Seer Bio, a Silicon Valley company that specialises in looking at how proteins behave, predicts the emergence of open foundation models that will integrate data spanning from genome sequences to medical histories.
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