The life sciences industry is undergoing a transformation, fueled by the integration of artificial intelligence (AI) into drug discovery and development. Last year’s merger between Recursion Pharmaceuticals and AI biotech Exscientia, valued at nearly $700 million, underscored this trend—marking the largest AI merger in the field to date. According to an EY report, this union followed a surge in AI partnerships and acquisitions over the past five years, signaling the crucial role of technology for life sciences companies.
Successfully integrating AI into the traditional drugmaking process requires creative strategies and commitment to the changes ahead, according to Ben Taylor, CFO and president of Recursion UK. He draws an analogy to the growth of biologics about a decade ago.
“You had every different strategy [then]. from going big acquiring assets, or going smaller to grow it from there, or not acquiring at all to grow entirely in house,” Taylor said. “You’re seeing the same thing happen, because most companies haven’t had a lot of validation coming through on AI platforms yet, and that’ll be a triggering part for a lot of pharma companies to start thinking about more.”
While Exscientia navigated challenges, including the loss of a significant partnership with Bayer and leadership changes, Recursion was steadily building its pipeline of AI-driven drugs and forming high-profile partnerships. The companies shared a common vision, and collectively now have 10 programs in the pipeline, with the potential for hundreds of millions of dollars in milestones with major pharmaceutical partners like Sanofi and Roche, Taylor explained.
“You’re not going to solve all of the different aspects to create a new molecule with a single algorithm — you need to integrate them.”
Recursion’s technology, supported by computer chip maker NVIDIA, is promising, but its pipeline is still in the early clinical stages. Its most advanced program, which is targeting the rare disease cerebral cavernous malformation, has advanced past phase 2, while programs in oncology and other rare diseases are in earlier phases of clinical trials or preclinical testing. Taylor believes that these kinds of supersized AI deals will become more common and will ultimately change the way drugs are made.
In an interview, Taylor discussed the AI revolution in biopharma, what companies should look for in partnerships and acquisitions, and the challenges associated with collecting and using big data to drive further shifts in the industry.
PHARMAVOICE: AI is clearly becoming a disruptor in the life sciences—why is that happening at this point in time?
BEN TAYLOR: The best way to think about AI is as a far better tool than [what] has been available previously that gives you functionality that wasn’t there before. I would liken it to when tools like Excel were coming all of a sudden, and it was so much easier to do something on a spreadsheet rather than using a calculator. We’re seeing a similar level jump in computation, going from the traditional methods we’ve been using for the last 20 to 30 years to something where we can perform a highly efficient, multi-parameter function.
“In biology and chemistry, there are no simple problems — everything is a complex, multi-parameter problem.”
Taylor continued, explaining that without companies like NVIDIA, the analysis wouldn’t even function, but fundamentally, the very architecture of the analysis has changed to allow for deep learning on big data. “We’re finding a needle in a haystack, and there’s a huge variety of algorithms to optimize…You’re not going to solve all of the different aspects to create a new molecule with a single algorithm — you need to integrate them.”
PHARMAVOICE: Speaking of integration, why did the Recursion and Exscientia deal make sense from your perspective?
BEN TAYLOR: Both companies were founded about the same time 12 years ago with a very similar mission, which is drug discovery. At the time, there were new technologies but they weren’t predictive, and so even with some of the smartest people in the world, we still haven’t been able to solve the very basic, fundamental problem of a failure rate that’s over 95%. Why is that happening? It’s because we didn’t have predictive ways of understanding what’s going to happen later on in a clinical trial. You actually have to predict how a molecule should work, and both companies had that vision with Recursion focusing on the biology and Exscientia focusing on the chemistry. It’s been fascinating to watch the integration because it’s been much easier than you would imagine. Now the chemists have all these amazing tools to play with in biology, and the biologists have these amazing tools to play with in chemistry, and it came together so powerfully.
PHARMAVOICE: What should drugmakers be looking for as they scan the horizon for AI partners or acquisitions?
BEN TAYLOR: Look for the use case. Look for the validation. We’re in an industry where lots of people have lots of ideas on how to do both, and the ideas all sound like they’re going toward the same goal, or they use the same language behind it. So the only way you can really differentiate is by defining the actual product you produce with a platform or technology…The only way you can really get your arms around that is to see what’s being produced.
PHARMAVOICE: On the flipside, what should AI companies be looking at when they team up with the life sciences?
BEN TAYLOR: The No. 1 thing is commitment, because it’s always easy to find a partner or even some money around new and exciting technologies. The partners that make a difference…their side comes in as committed as we are, and that comes from the top of the organization all the way through. When they’ve had relationships that haven’t progressed as quickly, it’s almost always because you just don’t have that commitment and that sense of doing whatever it takes to get this done. You’ve got to invest the time. If you’re not bought in on the process changing, it’s like taking an analog cable and putting it in the middle of a fiber optic — either way, you’re limited by whichever is the lowest quality point.
“If you think about the pharma decision making process, it takes a lot to move through it. There are a lot of different signoffs, committees, groups — all sorts of things. I can always tell the companies that are committed to AI because you see that smoothness of decision making, and that engagement comes from all levels inside the organization.”
PHARMAVOICE: If we look at data as currency in the life sciences, is there a limit to the availability for AI systems to make use of?
BEN TAYLOR: I don’t think there’s a limit. But most of the data isn’t worth anything. A lot of the data that has been collected historically generally isn’t in the right format or is very specific and not that useful. We use public datasets, or when we’re in a partnership we use their data and dig into everything we can, but honestly it only gets us to a very rough starting position. More useful is creating a fit-for-purpose dataset to be able to answer questions — and it’s fit directly for the purpose of the questions you’re asking. Since we work in proprietary drugs, and if you actually want to come up with novel concepts, you have to start with something that’s going to answer very specific questions. So you’re going to have to create your own data on that as well, right?
“We once had a discussion with a pharma partner who was asking us to come in and take a look at their old data and help them build models with it. The conclusion we came to was that it was going to be far more efficient and probably predictive to actually recreate all of the data that they had been doing in a much smaller context.”
Taylor concludes that it is far better to reconstruct the data than try to use old, unstructured data. He believes that data is a crucial part of this new and rapidly evolving sector.
