How Recursion Is Using AI to Rethink Drug Discovery

**Ed Gotham** (0:00)
Hello, everyone. We've got Ben Taylor on the show today, Chief Financial Officer and President of Recursion UK, bringing more than two decades of healthcare finance and biopharma transaction experience, including senior roles at Accenture, Goldman Sachs, and Barclays.
At Recursion, he helps lead the company's financial strategy and UK presence as it advances its technology-enabled drug discovery platform. How are you doing today, Ben?

**Ben Taylor** (0:28)
Great. Thanks for having me on.

**Ed Gotham** (0:30)
Where are you coming from? Are you based in the UK?

**Ben Taylor** (0:33)
I am. Well, I technically live in London, although I'm on the road so much, sometimes it feels like this is just where I changed the clothes in my suitcase.

**Ed Gotham** (0:42)
The company is based out of US. Is that right?

**Ben Taylor** (0:46)
We've got a good mix, actually. The legacy Recursion headquarters was in Salt Lake City. The legacy Accenture was in the UK.
We have large operations in both, and then we also have some smaller offices around it, including New York, which is where our CEO is.

**Ed Gotham** (1:05)
Okay.
Just for everyone who doesn't know the company, I thought we'd just give a brief overview of what Recursion is. If you could do that, that'd be great.

**Ben Taylor** (1:16)
Yeah, of course. I'll keep it really tight.
Basically, we use AI and lab automation to improve the quality and efficiency of making novel medicines, and thereby improving the probability of success of really getting that idea into a final drug that can be used with patients.

**Ed Gotham** (1:38)
I think I'm right in saying you're coming across from Accenture in a relatively recent merger. What's changed at Recursion or how has that influenced the company at large?

**Ben Taylor** (1:50)
Yeah, it was really transformational all around, both for us and I think for the industry as well.
Recursion and Accenture were both two of the leaders in the AI drug discovery and development space. They had started around the same time, almost 14 years ago now, but really focused on two different areas. Recursion and focused on the biology, Accenture focused on the chemistry, and how do you take AI and lab automation to get two better solutions than were traditional possible. Both companies had developed their own internal pipelines, partnerships, as well as a fairly large platform. By bringing them together, what we created was the ability not only to find novel targets, but also then create the drugs that could be used to hit those targets.
That's a really important part. Not only the idea, but also the translation into a real product. Since then, we've actually been able to take the expertise. Both companies have a little different expertise in some of the AI and the capabilities. We've been able to bring that together and we built out a clinical technology platform as well, to really round it out, and we applied that to all of our clinical trials also.

**Ed Gotham** (3:07)
I've been excited to have this interview because obviously, everyone knows AI is advancing at an exponential pace, and it's really impacting certain areas. But I think we're yet to see what is possible in health care yet, because there's a long cycle that has to go through. It's not immediate like getting access to a chat, to a PT or whatever. So AI is advancing really quickly. If that continues, well, I'm assuming we can assume that it will continue.
What would the drug discovery process look like in five to 10 years, based on what you're seeing today, and where the recursion fit there?

**Ben Taylor** (3:44)
I mean, I think it will be wildly different in a lot of ways, because if you think about it, we talk about the industrialization of the sector. Like right now, almost all aspects of health care are very artisan. You've got a very manual process or a creative process that people are doing, put it together, and all of the different steps are very segregated. And so what AI is able to help bring together is really, how do all of those different parts function more efficiently, be able to evaluate data in a much better way, and then bring them together so they actually build on top of each other?
And so that's part of why we've seen such differentiated results. And I know we'll talk some more about that, but just being able to actually drug targets that haven't been drugged before or go into new areas of biology. And so as we go forward five and 10 years, I don't think of this as being like the Industrial Revolution, where it was craftsmen and now you're suddenly on an assembly line. This is really more like the creation of CPUs. We're going from people putting together vacuum tubes to now how things are microfabricated.