How AI is revolutionizing drug development

MONROVIA, Calif. – The Terray Therapeutics laboratory is a symphony of miniaturized automation. Robots hum, transporting tiny tubes of fluids to their stations. Scientists in blue lab coats, sterile gloves and protective glasses monitor the machines.

But the real action is happening at the nanoscale: proteins in solution combine with chemical molecules contained in tiny wells on custom silicon chips that look like microscopic muffin tins. Every interaction is recorded, millions upon millions per day, generating 50 terabytes of raw data daily – the equivalent of more than 12,000 movies.

The lab, about two-thirds the size of a football field, is a data factory for AI-assisted drug discovery and development in Monrovia, California. It’s part of a wave of young companies and startups trying to harness AI to produce more effective medicines faster.

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Companies are leveraging new technology – which learns from huge amounts of data to generate answers – to try to remake drug discovery. They are shifting the field from painstaking craftsmanship to more automated precision, a shift powered by AI that learns and becomes smarter.

“Once you have the right kind of data, AI can work and get really, really good,” said Jacob Berlin, co-founder and CEO of Terray.

Most of the early business uses of generative AI, which can produce everything from poetry to computer programs, have been to help take the drudgery out of routine office tasks, customer service and writing code. However, drug discovery and development is a huge industry that experts say is ripe for an AI transformation.

AI is a “once-in-a-century opportunity” for the pharmaceutical business, according to consulting firm McKinsey & Co.

Just as popular chatbots like ChatGPT are trained on internet text, and image generators like DALL-E learn from a vast collection of photos and videos, AI for drug discovery relies on data. And it is very specialized data – molecular information, protein structures and measurements of biochemical interactions. AI learns from patterns in data to suggest possible useful drug candidates, as if it were matching chemical switches to the correct proteins.

Because AI for drug development is powered by accurate scientific data, toxic “hallucinations” are much less likely than with more extensively trained chatbots. And any potential medicine must undergo extensive testing in laboratories and clinical trials before being approved for patients.

Companies like Terray are building large, high-tech labs to generate information to help train AI, which allows for rapid experimentation and the ability to identify patterns and make predictions about what might work.

Generative AI can then digitally design a drug molecule. This design is translated, in a high-speed automated laboratory, into a physical molecule and tested for its interaction with a target protein. The results – positive or negative – are recorded and fed back into the AI ​​software to improve your next project, speeding up the overall process.

Although some AI-developed medicines are in clinical trials, it is still early days.

“Generative AI is transforming the field, but the drug development process is messy and very human,” said David Baker, biochemist and director of the Protein Design Institute at the University of Washington.

Drug development has traditionally been an expensive, time-consuming and unpredictable endeavor. Studies on the cost of designing a drug and conducting clinical trials until final approval vary widely. But the total expense is estimated at $1 billion on average. It takes 10-15 years. And nearly 90% of drug candidates that enter human clinical trials fail, usually due to lack of efficacy or unforeseen side effects.

Young AI drug developers are striving to use their technology to improve those odds while reducing time and money.

Its most consistent source of funding comes from pharmaceutical giants, which have long served as partners and bankers for small research ventures. Today’s AI drugmakers typically focus on accelerating the preclinical stages of development, which conventionally take 4 to 7 years. Some may try to enter clinical trials themselves. But it is at this stage that big pharmaceutical companies usually take control, carrying out the expensive human trials, which can take another seven years.

For established pharmaceutical companies, the partnership strategy is a relatively low-cost path to explore innovation.

“For them, it’s like taking an Uber to get somewhere instead of having to buy a car,” said Gerardo Ubaghs Carrión, a former biotechnology investment banker at Bank of America Securities.

Top pharmaceutical companies pay their research partners to reach milestones toward drug candidates, which can reach hundreds of millions of dollars over the years. And if a drug is ultimately approved and becomes a commercial success, there will be a stream of royalty income.

Companies like Terray, Recursion Pharmaceuticals, Schrödinger and Isomorphic Labs are seeking breakthroughs. But there are, in general, two different paths – those who are building large labs and those who are not.

Isomorphic, Google’s drug discovery spin-out DeepMind, the tech giant’s core AI group, considers that the better the AI, the less data it will need. And it’s banking on its software prowess.

In 2021, Google DeepMind released software that accurately predicted the shapes that chains of amino acids would take as proteins. These 3D shapes determine how a protein works. This was a boost to biological understanding and useful in drug discovery, since proteins drive the behavior of all living things.

Last month, Google DeepMind and Isomorphic announced that their latest AI model, AlphaFold 3, can predict how molecules and proteins will interact – another step forward in drug design.

“We are focusing on the computational approach,” said Max Jaderberg, director of AI at Isomorphic. “We believe there is enormous potential to be unlocked.”

Terray, like most drug development startups, is a byproduct of years of scientific research combined with newer developments in AI.

Berlin, who earned his doctorate in chemistry from Caltech, has pursued advances in nanotechnology and chemistry throughout his career. Terray emerged from an academic project that began more than a decade ago at the City of Hope cancer center near Los Angeles, where Berlin had a research group.

Terray is focusing on developing small-molecule drugs, essentially any drug that a person can take in a pill, such as aspirin and statins. The pills are easy to take and cheap to produce.

Terray’s elegant labs are a far cry from the old academic days, when data was stored in Excel spreadsheets and automation was a distant goal.

“I was the robot,” recalls Kathleen Elison, co-founder and senior scientist at Terray.

But in 2018, when Terray was founded, the technologies needed to build its industrial-style data lab were progressing rapidly. Terray has relied on advances from outside manufacturers to manufacture the microscale chips that Terray designs. Its labs are full of automated equipment, but almost all of it is customized – made possible by gains in 3D printing technology.

From the beginning, the Terray team recognized that AI would be crucial to making sense of its data stores, but the potential of generative AI in drug development only became apparent later – albeit before ChatGPT became a big success in 2022.

Narbe Mardirossian, a senior scientist at Amgen, became Terray’s chief technology officer in 2020 – in part due to the wealth of data generated in the laboratory. Under Mardirossian, Terray built its data science and AI teams and created an AI model to translate chemical data into mathematics and vice versa. The company has released an open source version.

Terray has partnership agreements with Bristol Myers Squibb and Calico Life Sciences, a subsidiary of Google parent Alphabet that focuses on age-related diseases. The terms of these agreements are not disclosed.

To expand, Terray will need funds beyond the $80 million in venture financing, said Eli Berlin, younger brother of Jacob Berlin. He left a job in private equity to become the startup’s co-founder and chief financial and operating officer, convinced that the technology could open the doors to a profitable business, he said.

Terray is developing new medicines for inflammatory diseases, including lupus, psoriasis and rheumatoid arthritis. The company, Jacob Berlin said, hopes to have drugs in clinical trials by early 2026.

Pharmaceutical innovations from Terray and its peers can speed things up, but only to a point.

“The ultimate test for us, and for the field in general, is if in 10 years we look back and can say that the clinical success rate has greatly increased and that we have better medicines for human health,” Berlin said.

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