Google AI Could Challenge Big Pharma in Drug Discovery

Google AI Could Challenge Big Pharma in Drug Discovery

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A win for DeepMind, the AI arm of Google’s parent Alphabet, at a biennial biology conference could upend how drugmakers find and develop new medicines, according to Bloomberg. It could also dial up pressure on the world’s largest pharmaceutical companies to prepare for a technological arms race.

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.

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 labored to devise software equal to the task.

With limited experience in protein folding but armed with the latest neural-network algorithms, DeepMind did more than what 50 top labs from around the world could accomplish. The simulation doesn’t yet produce the kind of atomic-level resolution that is important for drug discovery. It will be years before anyone knows whether such software can regularly spot promising therapies that researchers might otherwise have missed. But DeepMind’s victory points to a possible practical application for the technology in one of the most expensive and failure-prone parts of the pharmaceutical business.

Some observers said that the fact that a team of outsiders could make such significant progress in untangling one of the most vexing problems of biology is a black eye for researchers in the field. It could also be a portent for the drug industry, which spends billions on research and development, but was beaten to the punch. Finding new drugs and bringing them to market is notoriously difficult. According to some estimates, big drugmakers spend more than $2.5 billion to get a new medicine to patients. Just one of every 10 therapies that enters human clinical trials makes it to the pharmacy.

In the nearly 20 years since the human genome was sequenced, researchers have found treatments for a tiny fraction of the approximately 7,000 known rare diseases. Further, there are approximately 20,000 genes that can malfunction in at least 100,000 ways, and millions of possible interactions between the resultant proteins. It’s impossible for drug hunters to probe all of those combinations by hand. AI could also be used to scan millions of high-resolution cellular images, more than humans could ever process on their own, to identify therapies that could make diseased cells healthier in unexpected ways.

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