Generate Biomedicines, Inc. Common Stock Morgan Stanley 24th Annual Global Healthcare Conference

Thanks, Sean. Thank you. Yeah.

Yeah. I think China, Sean, has been, I think nothing short of miraculous in terms of the ability to brute force their way to exceptional answers across a broad range of therapeutic modalities and therapeutic targets. Certainly for us, it's forced us to be very thoughtful about how far we push the innovative frontier, because I do think if you're not pushing pretty far out, China can rapidly copy. I don't think it'll stop at just copying. I think they'll continue to push their own innovative frontier. What we've tried to do is actually think about where we can use our machine learning-based platform to drive answers to more complex therapeutic questions. Our TSLP program was largely an affinity maturation program where we basically took the affinity of a molecule like TEZSPIRE and improved it by about 20-fold.

China can get to those sort of answers now. For us, we've started to ask, what complex biological functions are more difficult to brute force your way to answer? Stuff like pH-dependent binding, ultra high selectivity, these sort of things that can combine and answer more and more complex biological questions has been kind of an evolution of our own discovery process.

Well, I think in some ways, the lead times in drug discovery are masking state-of-the-art of AI in drug discovery. For us, I mentioned our anti-TSLP molecule, that was discovered five years ago, and that was on a model that predates the original release of ChatGPT.

The state of the art of these generative approaches have progressed on a comparable basis to what we've seen with the large language models. So, where originally we were solving kind of simple questions around binding with these sort of approaches, now we're able to really take on very complex biological functions. The answers we're getting and the speed at which we're getting those answers really is distinguished from anything we've ever seen in drug discovery. So clearly, I think it's important to note that AI is not a panacea for all drug discovery and development. There are certain parts of the value chain that are probably more prone to disruption, and right now we believe molecular generation is at the forefront of that.

We think there's going to be some really interesting use cases in clinical development in terms of taking the waste out of the system. But the complexity of biology is so great that AI is not going to solve biology in the near term. But if you can be discerning and figure out which parts of the value chain can be disrupted, the tools can be extraordinarily powerful.

Well, I think clearly the U.S. industry has benefited from the gold standard regulatory body for the last 50 years. I think the uncertainty within the FDA has not helped anyone in the industry. When you make these sort of long lead time bets, you want to make sure that you have the confidence in the regulator that when you make those bets, if you're successful, they'll ultimately yield medicines for patients. So I think the uncertainty at the FDA has been one in particular. I do think the MFN question is something that is going to be deflationary for the whole industry, right? I mean, the reality is the U.S. economics have covered or carried a lot of the profitability of the industry and with MFN, I think in the first instance it hasn't been too harmful.

Having spent six years leading large parts of Merck's business in Europe, usually when these sort of policies are in place, it's the start of a slippery slope that ultimately the government will see this as a cost savings lever and will continue to turn that crank. It won't be a one-time opportunity. So I do think having the government negotiate prices in the U.S. is a huge swing given the free market dynamics that have underpinned the industry to date.