The pharmaceutical industry operates under one in every of the best failure rates of any business sector. The success rate for drug candidates entering capital Phase 1 trials—the earliest variety of clinical testing, which might take 6 to 7 years—is anywhere between 9% and 12%, depending on the yr, with costs to bring a drug from discovery to market starting from $1.5 billion to $2.5 billion, in response to .

This skewed balance sheet drives the pharmaceutical industry’s seek for machine learning (ML) and AI solutions. The industry lags behind many other sectors in digitization and adopting AI, but the associated fee of failure—estimated at 60% of all R&D costs, in response to —is a vital driver for corporations seeking to use technology to get drugs to market, says Vipin Gopal, former chief data and analytics officer at pharmaceutical giant Eli Lilly, currently serving the same role at one other Fortune 20 company.

“All of those drugs fail resulting from certain reasons—they don’t meet the factors that we expected them to satisfy along some points in that clinical trial cycle,” he says. “What if we could discover them earlier, without having to undergo multiple phases of clinical trials after which discover, ‘Hey, that doesn’t work.’”

The speed and accuracy of AI can provide researchers the flexibility to quickly discover what is going to work and what is going to not, Gopal says. “That’s where the massive AI computational models could help predict properties of molecules to a high level of accuracy—to find molecules that may not otherwise be considered, and to weed out those molecules that, we’ve seen, eventually don’t succeed,” he says.