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It’s still unclear how rapidly A.I. technology will develop, but at least some of the breathless hype of the earliest years has calmed as the tech’s near-term limitations have become more clear. The notion that artificial intelligence would immediately help us cure cancer, for instance, has been refined to the (still exciting) expectation that the technology can help drive major advancements in drug discovery. In 2024, Nvidia chief Jensen Huang declared that we are on the precipice of “computer-aided drug design.” Microsoft C.E.O. Satya Nadella has similarly talked about how the company’s latest A.I. models will advance the effort.
Surrounding all of these pronouncements, of course, is an environment of investments, partnerships, and deals between A.I.-focused biopharmaceutical companies and many of the major A.I. players. Formation Bio, which is backed by Sam Altman, raised some $372 million in 2024 from the usual Silicon Valley V.C. suspects; Retro Biosciences, also backed by Altman, was in talks to complete a $1 billion funding round earlier this year; and Google spinoff Isomorphic Labs completed a $600 million round in March. The list is vast, growing, and overflowing with zeros.
And yet, while A.I.’s role in drug discovery is indeed promising, it’s by no means a silver bullet. The reality is that the drug development pipeline takes, on average, between 10 and 15 years. The molecule selection process is time-consuming, but much of that time is spent gathering data to convince regulators a given drug is safe. That’s why a handful of tech companies, like the Israeli firm QuantHealth, have built their businesses around using A.I. to speed up those costly, time-consuming clinical trials—an overlooked race that’s quietly running in parallel with efforts to advance A.I.-assisted drug discovery.
Orr Inbar, the C.E.O. and co-founder of QuantHealth, told me that clinical trials “account for a larger budget than drug discovery by a wide margin.” Yet he said a “herd mentality” has consumed investors, who have largely neglected the promise and challenges of clinical trial optimization. “This problem that QuantHealth solves is really, really hard to solve, and it just so happens that this space is actually not a niche by any stretch of the imagination,” he told me. “It’s a $100 billion-plus market that’s received very little solutions.”
Indeed, a 2021 Congressional Budget Office report found that an outsize portion of larger drug companies’ R&D budgets is “devoted to conducting clinical trials.” A 2020 study, meanwhile, found that the median cost of developing a single drug, without adjusting for the costs of failed trials, is around $319 million; when you include the price of failed trials, the median price tag is upward of $1.1 billion. “Pharma has always wanted to do this. This is the holy grail for them,” Inbar claimed. “It’s just really hard. It’s taken us $30 million, five years, and 50 people to build this. You have to be a little obsessive to tackle a problem like this. Luckily for me, I have my obsessions.” Eli Lilly and Pfizer—both of which are listed as partners on QuantHealth’s website—didn’t return requests for comment regarding how they use QuantHealth’s technology and what effect it has had.
“It’s a Lift”
Inbar, who co-founded QuantHealth in 2020, said he started developing the technology as a graduate student. Several years ago, he was conducting research on medical records and found it was possible to build a model capable of predicting how a patient would respond to a preexisting drug. But the real challenge, in his view, was predicting how a patient would respond to a novel drug. He started looking beyond clinical and pharmacological data, and found that “there’s a huge amount of data that does, in fact, characterize the biology of almost all the drugs that pharma would like to develop. It’s just scattered and highly disorganized.”
When he founded QuantHealth, the company set about assembling—and licensing—those data sources, while building a series of algorithms designed to stitch them together. When applied to simulated clinical trials, Inbar said, the resulting A.I. system can predict “how any patient responds to any therapy,” which lets R&D teams at pharma companies adjust the design of their clinical trials to avoid costly failures. In one example the company shared, the suggestions of a QuantHealth simulation apparently led a major pharmaceutical company to refine its patient population, resulting in $31 million in savings.
QuantHealth operates in a space without any off-the-shelf model to iterate on, so the company has to build everything from scratch. That means proprietary “biological foundation models” trained with data from more than 100 million patients on average. These models, which Inbar said are much more complex than L.L.M.s, are also transformer-based deep learning systems, but they are designed with more classical statistical approaches in mind. Inbar said that QuantHealth develops one of these foundation models for each therapeutic area it’s targeting—such as oncology or immunology—and trains each one roughly twice a year. “It’s a lift,” he said.
Last year, QuantHealth closed a $17 million Series A round led by Accenture, among others, at an undisclosed valuation. As with all A.I. companies, the cost of talent—necessary for data collection, curation, and model-building—is a major source of QuantHealth’s expenses, in addition to the cost of compute. Of course, further investment will likely depend on whether QuantHealth can address some of the early skepticism surrounding the platform. This has involved back-testing on previously completed clinical trials, and testing on older trials on the verge of completion. The company, Inbar said, has completed over 100 tests with the first method and more than 35 with the second, achieving an 85 percent accuracy rate on both. “Pharma has to make these multidimensional decisions that carry enormous risk and R.O.I. for them, but they’re also really, really hard to quantify,” Inbar told me. “So we kind of step in there right at the moment of trial design when those key questions are being asked, and help de-risk the overall process.”