This skewed stability sheet drives the pharmaceutical trade’s search for machine studying (ML) and AI options. The trade lags behind many different sectors in digitization and adopting AI, however the price of failure—estimated at 60% of all R&D prices, based on Drug Discovery Today—is a crucial driver for firms trying to make use of know-how to get medicine to market, says Vipin Gopal, former chief information and analytics officer at pharmaceutical large Eli Lilly, presently serving an analogous function at one other Fortune 20 firm.
“All of these drugs fail due to certain reasons—they do not meet the criteria that we expected them to meet along some points in that clinical trial cycle,” he says. “What if we could identify them earlier, without having to go through multiple phases of clinical trials and then discover, ‘Hey, that doesn’t work.’”
The pace and accuracy of AI may give researchers the power to shortly determine what’s going to work and what won’t, Gopal says. “That’s where the large AI computational models could help predict properties of molecules to a high level of accuracy—to discover molecules that might not otherwise be considered, and to weed out those molecules that, we’ve seen, eventually do not succeed,” he says.
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