The Terray Therapeutics laboratory is a symphony of miniaturized automation. The robots rotate and transport small tubes of fluids to their stations. Scientists in blue 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 in custom silicon chips that are like microscopic muffin molds. Every interaction is recorded, millions and millions every 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 artificial intelligence-assisted drug discovery and development in Monrovia, California. It is part of a wave of young and start-up companies trying to harness ai to produce more effective drugs faster.
Companies are taking advantage of new technology, which learns from huge amounts of data to generate answers, to try to remake drug discovery. They are moving the field from laborious craftsmanship to more automated precision, a shift driven by ai that learns and gets smarter.
“Once you have the right kind of data, ai can work and get really good,” said Jacob Berlin, co-founder and CEO of Terrarium.
Most of the early commercial uses of generative ai, which can produce everything from poetry to computer programs, have been to help eliminate the monotony 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 the ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality#/” title=”” rel=”noopener noreferrer” target=”_blank”>consulting firm McKinsey & Company.
Just as popular chatbots like ChatGPT train with text on the Internet and image generators like DALL-E learn from large amounts of images and videos, ai for drug discovery is data-driven. And it is very specialized data: molecular information, protein structures and measurements of biochemical interactions. ai learns from data patterns to suggest potential useful drug candidates, much like matching chemical keys to the right protein locks.
Because ai for drug development is based on precise scientific data, toxic “hallucinations” are much less likely than with more trained chatbots. And any potential drug 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 data to help train ai, enabling rapid experimentation and the ability to identify patterns and make predictions about what might work.
Generative ai can then digitally design a drug molecule. That design is translated, in a high-speed automated laboratory, into a physical molecule and tested for interaction with a target protein. The results, positive or negative, are recorded and returned to the ai software to improve your next design, speeding up the overall process.
While some drugs developed with ai are in clinical trials, it is still early days.
“Generative ai is transforming the field, but the drug development process is complicated 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 and haphazard process. Studies on the cost of designing a drug and conducting 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 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 to smaller research projects. Today's ai drugmakers typically focus on accelerating the preclinical stages of development, which have traditionally taken four to seven years. Some may try to participate in clinical trials themselves. But that stage is where large pharmaceutical corporations often take control, carrying out expensive human trials, which can take another seven years.
For established pharmaceutical companies, the partner strategy is a relatively low-cost path to leveraging 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 biotech investment banker at Bank of America Securities.
Major pharmaceutical companies pay their research partners to reach milestones toward drug candidates, which can reach hundreds of millions of dollars over years. And if a drug is eventually approved and becomes a commercial success, there is a stream of royalty income.
Companies like Terray, Recursion Pharmaceuticals, Schrödinger and Isomorphic laboratories They are chasing progress. But, generally speaking, there are two different paths: those that build large laboratories and those that don't.
Isomorphic, the drug discovery spin-off from Google DeepMind, the tech giant's core ai group, believes that the better the ai, the less data is needed. And he's banking on his software prowess.
In 2021, Google DeepMind released software that accurately predicted the ways amino acid chains would fold into proteins. Those three-dimensional shapes determine how a protein works. This was a boost to biological understanding and useful for 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, a further step in drug design.
“We are focusing on the computational approach,” said Max Jaderberg, ai director at Isomorphic. “We believe there is enormous potential to unlock.”
Terray, like most drug development startups, is a byproduct of years of scientific research combined with more recent developments in ai.
Dr. Berlin, the executive director, who earned his Ph.D. in chemistry from Caltech, has pursued advances in nanotechnology and chemistry throughout his career. Terray emerged from an academic project started more than a decade ago at the City of Hope cancer center near Los Angeles, where Dr. Berlin had a research group.
Terray is focusing on developing small-molecule drugs, essentially any drug that a person can swallow in pill form, such as aspirin and statins. The pills are convenient to take and inexpensive to produce.
Terray's sleek 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,” recalled 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 advancing apace. Terray has relied on advances from third-party manufacturers to manufacture the microscale chips that Terray designs. Their labs are full of automated equipment, but almost all of it is customized, thanks to advances in 3D printing technology.
From the beginning, the Terray team recognized that ai was going to be crucial to making sense of their data warehouses, but the potential of generative ai in drug development became apparent only later, although before ChatGPT was launched. become a big hit in 2022.
Narbe Mardirossian, a senior scientist at Amgen, became Terray's chief technology officer in 2020, in part because of its vast amount of lab-generated data. Under the direction of Dr. Mardirossian, Terray has developed its data science and artificial intelligence teams and created an ai model to translate chemical data into mathematics and vice versa. The company has launched an open source version.
Terray has partnership agreements with Bristol Myers Squibb and Calico Life Sciences, a subsidiary of Alphabet, Google's parent company, that focuses on age-related diseases. The terms of those agreements are not disclosed.
To expand, Terray will need funding beyond its $80 million in venture funding, said Eli Berlin, Dr. Berlin's younger brother. He left a job in private equity to become co-founder and chief financial and operating officer of the startup, convinced that the technology could open the door to a lucrative business, he said.
Terray is developing new drugs for inflammatory diseases such as lupus, psoriasis and rheumatoid arthritis. The company, Dr. 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 10 years from now you look back and you can say that the clinical success rate has increased considerably and that we have better medicines for human health,” said Dr. .Berlin.
