U-M College of Pharmacy Awarded $3M Grant to Speed Discovery of Drugs

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U-M research will aim to discover new chemical reactions that can be used for drug discovery with funds from the $3 million grant. // Stock Photo
U-M research will aim to discover new chemical reactions that can be used for drug discovery with funds from the $3 million grant. // Stock Photo

The laboratory at the University of Michigan College of Pharmacy in Ann Arbor has been awarded $3 million grant by Schmidt Futures to develop molecular reaction data using high-throughput experimentation, and the software to process it to speed the discovery of new drugs.

The data uncovered will be available to all and the software developed will be free to academia.

To develop successful new drugs, agrochemicals, and other futuristic materials, invented molecules must first be synthesized — and outcomes are a gamble.

U-M researchers solve this problem by specializing in nanoscale synthesis, in which more than a thousand chemical reactions a day can be analyzed using high-throughput experimentation. They achieve this through miniaturization, the same way miniaturized transistors led to hand-held phones that would have been considered supercomputers a decade ago.

“High-throughput experimentation gives a systems-level look at any particular reaction,” says Timothy Cernak, assistant professor of medicinal chemistry at the U-M College of Pharmacy and assistant professor of chemistry in the College of Literature, Science, and the Arts. “It lets you see the forest instead of the trees in a new chemical transformation.”

The project is expected to yield 250,000 new chemical reactions to be made freely available for the development of machine intelligence in chemical synthesis. Cernak’s lab also will develop software called phactor, which will enable researchers to perform these experiments themselves. The software will be free for academic use and accessible to industry with a license.

“Schmidt Futures is excited about empowering researchers that are solving hard problems in science and society,” says Tom Kalil, chief innovation officer at Schmidt Futures. “By leveraging high-throughput experimentation, open datasets, and machine learning, Dr. Cernak’s project could lead to the discovery of new classes of life-saving drugs.”

Current chemical reaction testing methods typically are time-consuming and expensive because most chemical reactions fail, Cernak says. For this reason, drug makers gravitate toward a few well-known chemical reactions. As a result, just five chemical reactions comprise two-thirds of all reactions used in the pharmaceutical industry.

“Presently, these types of experiments are done with a handful of Excel worksheets and a whole bunch of Post-it notes,” Cernak says. “That’s fine for smaller experiments, but once you get up into a thousand reactions a day, a robust data management system is needed.”

Billions of unknown reactions exist and predicting a recipe for each of them will be critical to the future of drug discovery, he says. While some reaction data exists in literature, it’s sparse and not machine readable.

Two of those popular reactions rely on a rare metal called palladium, and Russia is the world’s largest producer. Finding replacement metals will diversify supply chains and accelerate discovery of new bond types that will be important in medicines of the future, according to Cernak.

Most nanoscale synthesis facilities are in industry, so a publicly available database and software is good news for consumers.

“The whole point of this project is to make data available to the masses,” Cernak says. “It’s likely that modern AI and machine learning could make short work of inventing the next generation of drug discovery reactions. There’s just no meaningful data available.

“In the future, we will design medicine based on patient needs and not on ease of synthesis. Today, we are somewhere in the middle — drug design necessarily weighs heavily on the ease of synthesis so we gravitate toward molecules that can be made by just a handful of reactions.

“Someday, I hope we can incorporate any of the billions of reactions that can be conceived today but not yet experimentally realized. We could make great strides in personalized medicine this way.”