Generate Biomedicines, Inc. Common Stock Bernstein Insights: Healthcare Leaders and Disruptors – 3rd Annual Healthcare Forum

Cool. Well, thanks, Courtney, and it's great to be here. Generate's a company that was founded in 2018, pretty much ahead of the curve, using generative AI to design protein therapeutics. This is a company that was founded in kind of a pre-AlphaFold, a pre-ChatGPT world to use the power of machine learning to understand and program biology. From the outset, the early results have been just extraordinarily encouraging that we can fundamentally understand through big data sets how a protein sequence ultimately translates into protein function. At the heart of the company is kind of an integrated dry and wet lab-based approach. We push the frontiers of computational models, diffusion models, to create good priors of what is an extraordinarily large search space. Then we have a wet lab and a verification engine to actually build and test all of those computational suggestions.

I think if you think about the domains of generative AI where we've seen greatest promise, it's always been in cases where you have a rapid verification cycle. The reason DeepMind started in gaming was you had a scoring system that you could rapidly train models on. Why we're seeing such great progress in coding and math is a similar feature, that you have a rapid verification engine. Biology doesn't have a natural verification engine, and so having this tight coupling of dry and wet lab is what allows you to create highly performant models to ultimately drive toward a world where we can program biology.

Well, I think it's changing really quickly. Even since Generate Biomedicines was founded in 2018, there have been two fundamental revolutions in the computational space. First was the introduction of large language models back in 2022, and the second is now this agentic science revolution. In both cases, these sort of technological inflections have actually extended the reach in which we use these technologies.

Now every program that we work on starts with an agentic design phase. What we're finding is that you can basically have about 1,000 PhDs interrogate any space in biology and come up with answers in what would historically have been months in down to a week's time. Your ability to understand the foundations of biology are changing rapidly. At the same time, what also is happening is that our ability to molecularly design and come up with answers from a molecular standpoint is radically improving. I think if you were to say, where are we furthest along, it's probably in the molecular design space.

where we're starting to understand how to, in some ways, code for molecules. I think you can see over the next 5 to 10 years, a world where molecule design will become commoditized which I think has huge implications for the industry because you'll then be able to basically create the molecule for any target that you so desire to perturb.

Where we're going next, I think this is partially just the maturation of the company, is thinking about how you use these tools in the clinic. We're seeing there's just a huge amount of waste in the clinical development process. Our lead asset's in a phase III trial for severe asthma. The enrollment period for that trial is 2 years. Nothing to do with the clinical endpoint. This is about how do you find patients and how do you find sites. We're going to 40 different countries, about 400 different sites. Each site is enrolling at a rate that's almost embarrassing.

The idea that we can't use these sort of technologies to identify patients who have clearly left digital signatures of the hospitalizations of their asthma exacerbations in a much more rapid pace Yeah I think this is low-hanging fruit for the industry.

There are huge ways where we can, I think, take out the waste in clinical development. I think there's huge opportunities to radically improve the filing timeline.

Usually from study, last patient, last visit to protocol submission or BLA submission, you oftentimes are looking at a 3-to-6-month process. The idea that an LLM can't draft your regulatory submission on the back end of a study almost instantaneously, I think we can envision those sort of worlds. There's a huge amount of waste in that cycle that could be taken out. Then where I think it becomes even more interesting is then, where do we find digital signatures and digital biomarkers to enrich populations so that the N in the study can go down and you can show greater treatment effect? I think all of these tools are going to come together, I think, at different stages of maturation. I think the complexity of biology is still going to puzzle us for some time.

Yeah. But the reality is, I think we are on this journey toward a more programmable future of medicine.