How A.I. Is Revolutionizing Drug Development
Most of the early business uses of generative A.I., which can produce everything from poetry to computer programs, have been to help take the drudgery out of routine office tasks, customer service and code writing. Yet drug discovery and development is a huge industry that experts say is ripe for an A.I. makeover.
A.I. is a “once-in-a-century opportunity” for the pharmaceutical business, according to the consulting firm McKinsey & Company.
Just as popular chatbots like ChatGPT are trained on text across the internet, and image generators like DALL-E learn from vast troves of pictures and videos, A.I. for drug discovery relies on data. And it is very specialized data — molecular information, protein structures and measurements of biochemical interactions. The A.I. learns from patterns in the data to suggest possible useful drug candidates, as if matching chemical keys to the right protein locks.
Because A.I. for drug development is powered by precise scientific data, toxic “hallucinations” are far less likely than with more broadly trained chatbots. And any potential drug must undergo extensive testing in labs and in clinical trials before it is approved for patients.
Companies like Terray are building big high-tech labs to generate the information to help train the A.I., which enables rapid experimentation and the ability to identify patterns and make predictions about what might work.
Generative A.I. can then digitally design a drug molecule. That design is translated, in a high-speed automated lab, to a physical molecule and tested for its interaction with a target protein. The results — positive or negative — are recorded and fed back into the A.I. software to improve its next design, accelerating the overall process.
While some A.I.-developed drugs are in clinical trials, it’s still early days.
“Generative A.I. is transforming the field, but the drug-development process is messy and very human,” said David Baker, a biochemist and director of the Institute for Protein Design at the University of Washington.
Drug development has traditionally been an expensive, time-consuming, hit-or-miss endeavor. Studies of the cost of designing a drug and navigating clinical trials to final approval vary widely. But the total expense is estimated at $1 billion on average. It takes 10 to 15 years. And nearly 90 percent of the candidate drugs that enter human clinical trials fail, usually for lack of efficacy or unforeseen side effects.
The young A.I. drug developers are striving to use their technology to improve those odds, while cutting time and money.
Their most consistent source of funding comes from the pharma giants, which have long served as partners and bankers to smaller research ventures. Today’s A.I. drugmakers are typically focused on accelerating the preclinical stages of development, which have conventionally taken four to seven years. Some may try to go into clinical trials themselves. But that stage is where major pharma corporations usually take over, operating the expensive human trials, which can take another seven years.
How A.I. Is Revolutionizing Drug Development - The New York Times (nytimes.com)