“Eli Lilly Chief Executive Dave Ricks was with Nvidia founder Jensen Huang earlier this year in San Francisco touting the company's tech prowess when Huang teased him about the painstaking process of developing new drugs.
"I'm really hoping that your industry moves from drug discovery, which is kind of like wandering around the forest looking for truffles," Huang said, in front of biotech and pharma investors.
Indeed, Ricks and the rest of the pharmaceutical industry are looking to expand beyond collecting soil samples and bark pieces to find new drugs. They are turning their hopes -- and investment dollars -- to AI. Lilly announced a partnership with chip-maker Nvidia in October to build what it called the industry's most powerful supercomputer, and expanded that in January with a $1 billion, five-year collaboration mixing their scientists and engineers in a new lab aimed at discovering new medicines with AI tools.
Rival Roche already announced it is building an even bigger supercomputer in partnership with Nvidia. Companies such as GSK, AstraZeneca and Merck have announced billions of dollars worth of partnerships in recent months with tech and AI-focused biotech companies aimed at fully exploiting AI.
Drug companies have been talking about the potential for AI to supercharge drug development for years, but it hasn't materialized in a big way yet.
"There was this promise you'd see dramatic improvement" in the rate of success of drug clinical trials as a result of AI, said RBC Capital Markets analyst Trung Huynh. "I don't think that's happened yet."
Part of the problem is that the amount of underlying scientific data to train AI models has been limited, and the cost of running high-volume computer experiments is high, though it is coming down, said Najat Khan, CEO of Recursion Pharmaceuticals.
Recursion was founded on the premise that it could train AI on cell images to better understand drivers of disease and therefore improve upon the 90% failure rate that currently weighs on drug development.
"The first wave, there's a lot of things that failed," Khan said. "The drug hunter mindset was missing for a long time."
Khan, who became CEO in January, said Recursion is making important progress. Its AI platform helped figure out that targeting a certain protein in the body was likely to help treat an inherited colorectal polyp disorder. Recursion used that finding to acquire an experimental treatment that hits that protein. In a small, early stage study, the company said its drug significantly reduced polyps in patients.
AI tools also helped speed up the time it took Recursion to design a new, experimental cancer drug to some 18 months, from an industry average of about four years.
It could still be years before data proves the drug works, because studies in humans take time.
Breakthroughs haven't come quickly enough for investors, though. Last year, Recursion laid off 20% of its workforce after cutting back its research pipeline. It still hasn't brought an AI-enabled drug to market nearly 13 years after its inception.
Some companies are getting closer to proving AI's value in drug research. Japanese drugmaker Takeda has a psoriasis pill that it acquired from a company that used AI to discover it. That succeeded in big studies and the company is submitting it for U.S. regulatory approval this year. Another Japanese drugmaker, Astellas, used AI to perfect its experimental pancreatic cancer drug setidegrasib. That is in late-stage studies now.
All told, RBC Capital Markets estimates the technology could save the U.S. pharma industry about $90 billion in the next five years, boosting per-share profits by up to 13%.
Much of the AI payoff for drugmakers so far has been streamlining back-office tasks or speeding up manufacturing rather than making big drug research advances. The advent of more advanced generative AI -- such as ChatGPT -- opened up new possibilities.” [1]
1. Drug Discoverers Pin Hopes on AI. Loftus, Peter. Wall Street Journal, Eastern edition; New York, N.Y.. 06 May 2026: B4.
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