From aerospace to mechanical engineering, professionals across the world rely on powerful supercomputers to aid their work each day. But in the pharmaceutical industry, one of the most high-tech industries in the world, the majority of drug development is still done by hand.

Researchers physically synthesize each and every promising compound, then test to see if it's safe for human use. Creating a drug that effectively treats an illness while producing the least amount of side effects is a process that can take years—and the cost of research has been rising dramatically, too.

But ?a young Canadian startup called Atomwise thinks it has an alternative approach. The company, which was started by a computer scientists at the University of Toronto's Im?pact Centre, has created a machine learning algorithm that they hope will not only help researchers develop the next generation of pharmaceutical drugs, but do so faster and cheaper than ever before.

According to a report released in 2003 by the Tufts C?enter for Drug Development, pharmaceutical companies were paying about $802 million ($1.04 billion when adjusted for inflation) to develop a new drug between 1983 and 1994. When Tufts completed a follow up study in 2013 that looked at the cost of drug development between 1995 to 2007, development costs had risen to $2.5 billion per successful drug.

"There are health threats out there for which there are no adequate solutions today, and it's unclear if the status quo will be enough"

The increase in cost is due to several factors, the most important of which is that, as scientist attempt to develop treatments for more complicated chronic and degenerative diseases, more and more drugs are failing to pass human trials. It's currently estimated that eight out of 10 drug development projects are abandoned.

However, there are other factors, as well. According to the Tufts report, increased trial complexity and larger clinical trial sizes have all led to rising development costs.

It's a trend that, if allowed to continue, will have worrisome consequences on human health. "There are health threats out there for w?hich there are no adequate solutions today, and it's unclear if the status quo will be enough," said Alexander Levy, chief operating officer of Atomwise.

Levy says one of the health issues he and his colleagues are most worried about is antibiotic resistance. Antibiotics that were developed in the 40s, 50s and 60s are becoming increasingly ineffective in treating bacterial infections— and he's not the only one that's worried. According to the C?DC, each year some 23,000 Americans die from bacteria that have become resistant to current antibiotics.

"One day even more recent antibiotics won't work either," he said. "That will lead to a world where people can't have safe routine surgery and a world where minor injuries may lead to fatal infections. We will live in a world where mortality goes dramatically up."

It's not just bacterial infections that could pose a serious threat to human health, either. A host of other illnesses—including the resurgence of diseases like polio, measles and whooping cough, as well new strains of the avian and swine flu—all have the potential to severely stress the scientists' ability to develop new treatments fast enough to keep up.

The algorithm Atomwise developed is similar to the Deep Learning Neural Networks used by DeepMind, a startup that was acquired by G?oogle last year for $628 million. While Google has been happy to let the AI teach its?elf how to play Space Invaders, Atomwise has asked it to learn complex biochemical principles instead.

"We let it take thousands of simulated [processor] years to teach itself the factors that are ultimately most predictive when it comes to the effectiveness of a drug," said Levy. "Our system doesn't look at a dozen or two dozen factors, it looks at thousands of factors at the same time and combines them in complicated and nonlinear ways."