Roche Holding AG IR Digitalization Day (Virtual) Transcript

The following is excerpted from the question-and-answer section of the transcript.

Question: Steve Scala - TD Cowen - Analyst : Thank you so much for a really interesting presentation. I'm just wondering how Roche's efforts in this area, do you believe, compare to those, say, among competitors or in academia or government? Are Roche's efforts unique in any way? Or are they more uniform throughout the industry? Aviv Regev - Roche Holding AG - Member of the Enlarged Corporate Executive Committee, Head Genentech Research and Early Development (gRED) So I can never exactly know what is happening in other companies. I think I can comment quite reliably on the academia side and what the government is doing. But in other industry partners, it's always harder for us to tell. So I'll caveat that. I believe that in several of these areas, we are absolutely at the very forefront of the state-of-the-art. And I think this is reflected in things like the kinds of papers we put out, the publications we have and the performance that we see internally, including in our ability to really do it internally rather than rely on only on partnerships for it. The reason I think that is really important is that the models really rely on training with a lot of data and that quality and quantity of data is hard to share whole (technical difficulty) with external partners. I will also say that as a result of it, anything that happens in the learning loop doesn't just provide for us a target or a molecule, it actually provides to us also the better algorithm for all targets and all molecules. So in that area, I think we were a very early investor, and we built an extremely strong team. They have a lot of external visibility in the best places, and that is the way in which I can measure that I do think that they are at the cutting edge. When I compare to academic efforts, what I described for molecules is very, very hard to do in an academic setting. I think some of it is hard and some of it is on the verge of impossible. And the reason for that is that the training data don't exist in the scale and scope that they need to exist because this work is almost invariably done by companies. In small companies, they don't exist because a small company doesn't have decades of history for its data. And the ability to do all the iterative work does not exist, the amount of synthesis you have to do, the number of iterations you need to do on the synthesis and so on. And if you look at where the literature is on that, which is in conferences and talks, I think it's pretty illustrative of that. You see a lot of work happens where the data are. So on things that you can do, for example, on protein structure, you can do a lot because a lot of protein structure is in the public domain, but antibody structures are not like that. And antibodies are a very unique creature. They're not similar to others. We're interested in the most variable regions where evolutionary information is not as helpful and where you need to have complex data and where you need to know the epitope. And so that type of richness really is hard to mimic externally and requires a substantial amount of investment. When I turn to the biological side, again, I think we were not only early adopters, a lot of the pioneers of these methods experimentally and computationally work in Genentech. And REFINITIV STREETEVENTS | www.refinitiv.com | Contact Us consent of Refinitiv. 'Refinitiv' and the Refinitiv logo are registered trademarks of Refinitiv and its affiliated companies. NOVEMBER 25, 2024 / 3:30PM, ROG.S - Roche Holding AG IR Digitalization Day (Virtual) by that, I include the two people who are on this call. And that has given us an edge. But everyone in biology works in a more open way because data tend to abound more publicly and the iteration happens in a more open way. Where our competitive advantage lies is in having patient data from clinical trials. I alluded to that when I described the IBD case that is invaluable and very hard to achieve with detailed clinical information and very high resolution data and in the commitment to combine it together with drug discovery, like the work I described that we do with large-scale screens to build the cell Oracles, again, an area that is very, very hard to replicate in academia and mostly does not exist there.

Question: Steve Scala - TD Cowen - Analyst : Thank you so much.

Question: Steve Scala - TD Cowen - Analyst : Thanks for the follow up. You focused mainly on positive outcomes, but are there examples where AI generated the wrong answer? In retrospect, why did that occur? And how was it detected? Aviv Regev - Roche Holding AG - Member of the Enlarged Corporate Executive Committee, Head Genentech Research and Early Development (gRED) Actually, when I showed the graph, you could go later on and look at the slides of the iterations of the lab in the loop. You can see the places where it totally crashed and burned. It wasn't like expressing. Nothing was binding or they were expressing and binding, but the affinity was like tanking. We have graphs like that for everything. The reason we track them, we have a dashboard that tracks every iteration is because it actually fails before it succeeds. And the places -- that's in the molecular sense, I'll get to the biology in a second. The reason it fails is that -- it can be varied. But often, it is -- it can be algorithmic choices that are not set up correctly. It can be going too much off the deep end in being generative all the way into the hallucination domain. It really depends. But it happens a lot, and it is valuable. That's the part that I think is very, very hard initially for experimentalists to accept how useful the failures are. Even though when you run an experiment as an experimentalist, you learn from the failures all the time, the human brain does the same. But those failures are really important as is the need to synthesize, make, do experiments for things that the model is uncertain about because the model wants to improve its certainty. It doesn't just want to make you a molecule. And that's part of the process of understanding that. I'll say in a moment something about the biology, but I'll turn again to John for a second for his perspective. He sees it like day in and day out. REFINITIV STREETEVENTS | www.refinitiv.com | Contact Us consent of Refinitiv. 'Refinitiv' and the Refinitiv logo are registered trademarks of Refinitiv and its affiliated companies. NOVEMBER 25, 2024 / 3:30PM, ROG.S - Roche Holding AG IR Digitalization Day (Virtual) John Marioni - EMBL-EBI - Senior Vice President and Head of Computation at Genentech Research and Early Development (gRED) Yeah. So it is hard when you predict something and it's negative. But as Aviv said, it's actually really powerful and important for the model, learning what doesn't work allows it to then generate better predictions in the next iteration. And you also see that on the graphs that Aviv showed. Yes, you do have those cycles where things didn't work, but then that allowed the model to explore a better area of the space and then that allows it to then make better predictions. So this negative data is something that's very important. It's something that we really treasure as well and having that internally and that access to that large amount of data, both positive and negative is really critical. On the biology side, I think that -- I'll hand back to Aviv in a second, but the generalizability is sometimes rather challenging. And that's just because in the biology space, the volume of data that we have compared to on the molecule space is much less. And it tends to be from generalizing to broader populations or to unknown perturbations is harder just because we have less training data. And so there, I think the challenge is how generalizable are the models. So how could we apply them, say, to an individual with a different genetic background? And do we have enough data to train the models well for that? We're generating that, we have more of it. But I think when we look at the effectiveness of the different approaches, some of it is around the generalizability of the models that we have and how well they are able to be applied in other contexts. But as we generate more data through those partnerships with Recursion that Aviv talked about through our own internal efforts, through other public sources, the models will get better and better and more generalizable, and that will then allow them to be applied in a variety of different contexts. But at least from a scientific perspective, that generalizability at the moment in the biology space is hard because the amount of data that we have relative to the chemical space is just that much more limited when it comes to having a broader range of training data, but I'll pass back to Aviv for her perspective on that. Aviv Regev - Roche Holding AG - Member of the Enlarged Corporate Executive Committee, Head Genentech Research and Early Development (gRED) I'll start actually with something, Steve, I think you asked earlier about how we compare to others. And I pointed out that there is a limit to what you can do in collaborations on some of these problems. One of the limits is the need for these many iterations through failure until you get to really amazing success and that those sometimes don't stick, right? You try with an external partner, you give them the data, they're supposed to deliver some molecule. They don't succeed in the first round. They don't succeed in the second round. They don't succeed in the third round. They don't succeed in the fourth round. And maybe it's going to be amazing by the 15th. But by the 15th, the partnership will be closed because the team will look at it and they will say that they can't deliver. So some of it requires actually sticking by for several years. And then when it works, it starts working for a lot of things, and it becomes a lot more impactful as a result. But those are learnings that you have on the inside. The second piece on the biology, John covered really, really well the generalization problem. I will highlight a different one and the way that we're thinking about it now, which is the volume. These experiments in biology generate massive amounts of results, all of which could be completely valid. It's like endless results. And the challenge becomes not learning a model from it and not delivering it to the -- not even its accuracy, but we don't -- a human doesn't know what to do with that much. It's almost like too much. And then it's hard to reason through it, and there's a huge literature to reconcile together with the results of these massive experiments after the AI, after the model, after everything. And so that is where the agents actually become so impactful because, A, they liberate a much larger number of people to act as though they are computational biologists. They don't need to know how to code and how to run and which tools are out there exactly and so on. And B, because you can ask the agent to reason through this as well. And you can develop agents and models that really try to do next-generation reasoning on these results to say, "Oh, when I do this, that happened because this might be the chain of events that led there," and so on. These are really time-consuming and difficult things, and there is no way, I mean, for a human to do it even in a lifetime, let alone in the speed that we actually needed for drug discovery. REFINITIV STREETEVENTS | www.refinitiv.com | Contact Us consent of Refinitiv. 'Refinitiv' and the Refinitiv logo are registered trademarks of Refinitiv and its affiliated companies. NOVEMBER 25, 2024 / 3:30PM, ROG.S - Roche Holding AG IR Digitalization Day (Virtual)

Question: Steve Scala - TD Cowen - Analyst : Yes, thanks very much.