What's Your Moonshot? Abraham Heifets of Atomwise Is Using AI to Speed up the Development of Medications Targeted at Specific Cancer Cells

Abraham Heifets is the CEO and co-founder of Atomwise, a biotech company using patented deep learning artificial intelligence technology to predict and discover which drugs will be better, safer and more potent for cancer patients.

What is your moonshot?
To make novel, better and safer drugs, with the ultimate goal to get medicines into the hands of patients faster.

How do you do that?
We're trying to solve how you modify a cell that's in the runaway disease process, and figure out what's causing a cell to keep growing and dividing. Think of proteins in your body as machines on the assembly line. If the machine governing cell growth and division breaks and goes haywire, then the cell will keep growing and dividing. That's a tumor and how cancer happens. If you see a machine going haywire, you'd want to throw in a monkey wrench so the machine is busy chomping on that instead. Today, it takes about 15 years and several billion dollars to find a new drug. Every day that you don't have good treatment, that's real people, patients, lives and health in the balance.

How does Atomwise go about searching for the right drugs?
Every other industry uses computers for design. But in pharma, you have to physically make and test every one of those prototypes. If you think about designing a new airplane, you'll simulate a thousand wings before you ever build one. And only after the computer says wing #88 will fly, be fuel efficient and be quiet, and only after you simulate thousands of wings do you then go build the prototype that you take to the wind tunnel for the test flight. Atomwise is about bringing that efficiency and that design into biology and drug discovery.

Using deep learning artificial intelligence?
That's exactly right. My co-founder, Izzy, and I were grad students at the University of Toronto when deep learning and this current era of Artificial Intelligence was being invented. Our computational biology group was on the same hallway as Geoff Hinton's deep learning group. He just won the Nobel Prize of computer science, the Turing Award, for inventing deep learning. We saw pretty early on that the kind of work that was happening for image recognition and speech recognition could be applied to molecular recognition.

How exactly does AI enable safer, more effective and potent drugs?
Imagine you're a biologist and you've been studying pediatric cancer. You've done tons of experiments and you've determined that if you could just block protein X, that would halt the disease. Rather than trying to kill off every rapidly dividing cell, you want to be able to arrest the disease without harming the healthy cells. Now you need the drug that is effective and safe. AI lets us begin by testing 2,000 times as many molecules as has been tested before. Once you find some sets of molecules that look pretty good, you try to make variations that will improve the molecule. The computer lets you evaluate billions—instead of tens or hundreds—of molecules in one go, which means you're going to find better answers and you're able to discover that winning lottery ticket. Our 10-to-the-10 project is the next step in that, which is running 10 billion molecules against pediatric cancer targets.

Does it feel amazing getting that "winning lottery ticket"?
Absolutely. I think everyone goes into this because they want to help people. And frankly, patients shouldn't have to be patient. It's our job to get medicines to them as fast as possible.

What is "success" to you, and are you close to achieving it?
Success for everyone in this field is helping patients. A measure of success is that if you look at our large pharma partnerships, you can see they're embracing this new approach; you can see there's trust in Atomwise's AI systems. We recently announced a deal with Eli Lilly for over half a billion dollars. You'll see Bayer and Pfizer. The previous big deal we announced was with Charles River Labs. These industry-standard players have embraced AI approaches.

How do you picture the industry in 20 years if you succeed?
The industry is shifting toward AI. We're actually running the biggest application of AI-to-drug discovery in history, and we have over 200 projects in every therapeutic area. So, I think the potential application is huge. About 35% of those projects are in cancers. At the end of the day, our success is patient success.

Correction 7/23, 11:48 a.m. Abraham Heifets was incorrectly referred to as Aaron, and Geoff Hinton was incorrectly spelled Jeff in a previous version of this article. This has been updated, and Newsweek regrets the error.