Machine-learning technology has beaten humans at games of chess and Go to worldwide fanfare. A demonstration of its eerily lifelike prowess in making phone calls to unsuspecting people went viral.
But a less-noticed win for DeepMind, the artificial-intelligence arm of Google’s parent Alphabet Inc., at a biennial biology conference could upend how drugmakers find and develop new medicines. It could also dial up pressure on the world’s largest pharmaceutical companies to prepare for a technological arms race. Already, a new breed of upstarts are jumping into the fray.
In December, at the CASP13 meeting in Riviera Maya, Mexico, DeepMind beat seasoned biologists at predicting the shapes of proteins, the basic building blocks of disease. The seemingly esoteric pursuit has serious implications: A tool that can accurately model protein structures could speed up the development of new drugs.
“Absolutely stunning,” tweeted one scientist after the raw results were posted online. “It was a total surprise,” said conference founder John Moult, a University of Maryland computational biologist. “Compared to the history of what we had been able to do, it was pretty spectacular.”
Sorting out the structure of proteins in order to find ways for medicines to attack disease is an enormously complex problem. Researchers still don’t fully understand the rules for how proteins are built. And then there’s the math: There are more possible protein shapes than there are atoms in the universe, making prediction a herculean undertaking of computation. For a quarter century, computational biologists have laboured to devise software equal to the task.
Enter DeepMind. With limited experience in protein folding — the physical process by which a protein acquires its three-dimensional shape — but armed with the latest neural-network algorithms, Deep-Mind did more than what 50 top labs from around the world could accomplish.
Excitement rippled around the resort where the meeting was held. Two DeepMind presenters were peppered with questions from scientists about how they had done it. Within hours, the The Guardian said DeepMind’s AI could “usher in new era of medical progress.” In a blog post, the company bragged that its protein models were “far more accurate than any that have come before,” opening up “new potential within drug discovery.”
DeepMind’s simulation doesn’t yet produce the kind of atomic-level resolution that is important for drug discovery. And though many companies are looking for ways to use computers to identify new medications, few machine-learning-based drugs have progressed to the point of being tested in humans. It will be years before anyone knows if such software can regularly spot promising therapies that researchers might miss.