DeepMind, the artificial-intelligence arm of Google’s parent Alphabet Inc., has the potential to 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 labored 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, DeepMind did more than what 50 top labs from around the world could accomplish.
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. And science moves slowly: 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.
“If we want to understand the other 97 percent of human biology, we will have to acknowledge it is too complex for humans,” said Chris Gibson, the co-founder and Chief Executive Officer of Recursion Pharmaceuticals, a Salt Lake City-based startup that uses machine learning to hunt for new therapies.
Companies like Recursion are rapidly luring investors. Venture capitalists poured $1.08 billion into AI and machine-learning startups focused on drug discovery last year, according to data provider PitchBook, up from just $237 million in 2016, and have already put in $699 million more so far this year.
Machine-learning methods “are going to be critical” to drug discovery, said Juan Alvarez, an associate vice president for computational chemistry at Merck & Co. The giant drugmaker is developing AI tools to help its chemists accelerate the laborious process of crafting chemicals to block aberrant proteins. Early machine-learning efforts have already contributed to drugs in human testing, while the first drugs based on more advanced neural-network methods could hit trials in several years, Alvarez said.
Other parts of Alphabet, as well as the AI research unit of social-media giant Facebook Inc., which quietly released a paper using deep learning to analyze 250 million protein sequences in April, are creeping into pharmaceutical-company turf. This spring, AI researchers at Google unveiled a neural network that can predict the function of a protein from its sequence of amino acids, which can help biologists understand what a newly discovered protein does.
AI proponents say that nobody is talking about taking human researchers out of the equation. The goal is “augmenting and enhancing the decision-making capacity of scientists,” said Jackie Hunter, a former GlaxoSmithKline research executive who now leads clinical programs at BenevolentAI.
In the short run, it’s more likely that AI-based simulations will be used to game out whether prospective drugs will be effective before going to a full-on clinical trial.
An aerospace company “won’t build and fly a plane without building it on the computer first and simulating it under many conditions,” said Colin Hill of GNS Healthcare, a startup using AI to model disease, whose investors include Amgen Inc. In the future, drugmakers won’t begin clinical trials without a virtual dry run, Hill said.
Still, the surprise that unfolded in Mexico has increased the tempo. DeepMind “basically beat everyone by a sizeable margin” said AlQuraishi, the Harvard researcher. If drugmakers don’t take the threat seriously, he said, they could be left in the dust.
By Robert Langreth