In 2008, François-Henri Boissel was leading a charmed life. He was a young, successful investment banker working in Tokyo, Japan. And then the market crashed.

He thought of sticking it out, waiting until things improved, but then he remembered a conversation he’d had with his father, Jean-Pierre, in the summer of 2007, and it started gnawing at him.

His father had had a long career in clinical research and had always dreamed of using mathematics to “find truly innovative therapies and dramatically improve patient outcomes,” François recalls. The pair had discussed the idea of using mathematical modeling to improve innovation in the pharmaceutical industry, but François had put that idea to the side because he was enjoying the banker’s life and the pharmaceutical industry seemed risky. But in 2008, things changed.

“After having spent a number of years analyzing companies through financial statements and market research reports, I was curious to actually get my hands dirty,” François says. He was 28, single, and had no kids. “It was the ideal setup to take on serious risk.”

The result was Novadiscovery, a startup founded in 2010. In essence, this fledgling company is trying to build a community of virtual patients that scientists and drug companies can use as on-demand digital lab rats. Its goal isn’t to understand how patients interact or behave, but to help curb the costs of discovering new drugs by providing a means of screening potential drug candidates — and screen them quickly — using mathematics and intelligent algorithms.

“This is going on before you get anywhere near a person. It’s the first point of research,” François told Wired. “It’s a major disruption.”

In 2008, when he first left the banking game, François moved back to France and spent the next year brainstorming with his father on how they would try to solve some of the inefficiencies that had plagued the drug pharmaceutical industry for decades. “Our skill-sets were very complementary. [My father] would bring the fundamental science, and I would contribute my business expertise,” François says.

After several months spent ironing out concepts, and recruiting scientists and engineers, Novadiscovery was born. Nova is part of a growing group of companies that are turning to model-based approaches to circumvent some of the inefficiencies that have plagued the pharmaceutical industry in recent years. Pfizer, for example, published a paper in May on the cost benefits of incorporating predictive quantitative modeling into their R&D pipeline.

“This won’t replace [clinical] trials in humans and animals, but it will inform much earlier in the process which [molecules] are worth spending on and which ones should be cut,” François says.

Currently, pharmaceutical companies can invest 10 to 15 years and billions of dollars in basic research before they know whether their drug candidate is a dud. There isn’t a reliable way to predict how well a potential drug will work in people so a majority of funding pays for failure. The end result is an industry rife with wasted resources, little innovation, mediocre products and astronomical prices.