Good morning, everyone. Welcome to the last day of the JP Morgan Healthcare Conference in 2024. My name is Cindy Shu. I'm an associate at the JP Morgan Healthcare Investment Banking team. Today, it's my great pleasure to introduce you, Dr. François Nader, the CEO of BenevolentAI. Thank you. Thank you, Cindy. Good morning, everyone, and thank you for being here with us. So it's my pleasure to present BenevolentAI. Actually, I'm the Chairman and Acting CEO, and for those of you who are not familiar with Benevolent, we are an AI-augmented drug discovery company, and the uniqueness is we really blend science and technology, and we focus on finding solutions for complex diseases. I'll give you a nanosecond to read this forward-looking statement. I'm sure all of you are familiar with its content, but then moving to who we are and what we do. So this is what we do, frankly, on one slide. We're very privileged to have invested over the last many years in what we call our Benevolent Platform, and I will give you a little bit more of what it is, but basically, it is, it is the foundation, the backbone of the company, and a lot of investment were really made into this platform. And the uniqueness, as I've shared with you, in a few minutes, it's a platform that was developed by scientists for scientists, okay? So there is a commonality there. And the three pillars, how we deploy our platform, are the following: The first is what we call end-to-end drug discovery. In plain English, this is what we do with partners, and we're very pleased to have two partners so far, AstraZeneca and Merck, and I will give you a little bit more detail about what we do, but this is a collaboration between BenevolentAI and our partners, and this is our first revenue stream. The second one is self-serving, frankly, and we deployed over the years our platform to generate our own pipeline and our own products, right? And we went through a very tough, very interesting selection process, and currently, we have five products that are in our pipeline, and I will give you the overview of what they are and where they are. And the third component is what we call our knowledge exploration tool, and if you think of it for a second, we have our own pipeline, we have the partnership, but we also thought that that could be a good idea of developing knowledge exploration tools, which is more a SaaS service for scientists, leveraging our platform and frankly, targeting the scientists that we work with in multiple different ways, whether partnership, our pipeline, or others. So that's a little bit the overview of what the company is all about, and to zoom in now on giving you a little bit more detail, this is the platform. This is what... When I joined Benevolent, coming from pharma and biotech, I called it the black box, and the folks at Benevolent were kind enough to train me, teach me, educate me on the AI component. But when you boil it down to the uniqueness of what we have at Benevolent, it's these four components. The first one is what we call the ingestion and insight extraction. Here again, practically, what does it mean? It means that we have about 85-plus data sources, and our technology enables us to take all these data sources, whether structured, unstructured, genetics, omics, clinical, experimental, chemistry, and others, and make something out of it, okay? And so, for example, in the unstructured one, we use the NLP, the natural, the NLP process, if you will, so that we take these unstructured and make something out of it, something that the scientists can use. Once we have these 85+ data sources, the second step, it's frankly to integrate them, and that's probably the core of the core of the technology that that Benevolent has. So we take these 85 sources and integrate them and create inferences. Okay? Now, as with any technology, if we stop here, it's intellectually interesting, not terribly practical and useful, and therefore, the question is: How do we deploy this knowledge? We have multiple ways of deploying the knowledge through what we call our AI-driven drug discovery and development in clinical subtyping, mechanism recommendation, and you can go the list. The strong suit of Benevolent is it has to do with the target prediction, the target ID, and target assessment, even though we do other things, and the partnership with Merck actually takes us to do more work in silico-led hit ID, in silico lead optimization as well. Then, once we identified what we can do, we selected our deployment in three areas, as I mentioned: the partnerships, our own pipeline, and the knowledge exploration tool. That's the beauty of our platform. I cannot say we have the best platform in the industry. I can safely say that we have one of the best platform in an industry, and we have one of the rare platform that has been validated, and I will show you how. Our deal with AstraZeneca, very important for the company, started back in 2019, and they gave us two challenges. They said, "Why don't you find us target, targets for two, conditions: chronic kidney disease and IPF?" And the team worked, and the outcome, one of the many outcomes of this partnership, is the fact that come May 2023, there was a presentation at the ATS on one of the targets that we worked with AstraZeneca on. Come 2022, AstraZeneca decided to expand the partnership and added two conditions, heart failure and SLE. As you both, I mean, as you all know, this is not easy target, but the teams have worked together very, very nicely, and so far we have four promising targets that are moving forward. Now, for the company, it's extremely interesting because we had an upfront, but then we have milestones, payments, and downstream royalties that will come as these products continue down the path of development and commercialization. In September, we signed a completely different deal with Merck, and we leveraged the wet labs. Again, this is one of the specificities of Benevolent. We have our own wet labs, so we can do our own research. And the challenge that Merck gave us is, "Can you help us find three novel small molecule drug candidates in oncology, in neurology, and immunology?" So the deal was signed in September, and we have started working with Merck on identifying these targets. For the company, it was a very lucrative deal, close to $600 million, and it included a low digit, low double-digit, million-dollar upfront payment, and then the rest will follow in terms of milestones. So extremely important for the company as well, and I know that our team is working diligently on adding more partnerships as time goes by. So this was the first pillar of our revenue. The second pillar is our own pipeline, and with this pillar here, the pipeline, we look like a traditional biotech company. And we have our lead product is a PDE10 inhibitor that is about to complete its phase 1. The indication is ulcerative colitis, and the important point here is because we own these products and we work very, very carefully on selecting them, all of them, with no exception, are either best-in-class or first-in-class. Okay? So if I take the PDE10, it's really first-in-class, and it's peripherally restricted small molecule for the treatment of ulcerative colitis. So we're very pleased with where we are today with this product, and we look forward to announcing top line very shortly. The second product is a CHEK1 inhibitor, and we selected GBM as our indication. And again, it's first-in-class, and it's a product that is CNS penetrant. And as you all know, this is an absolutely critical metrics, if you will, for potential success. We believe that this product could potentially treat patients who are resistant to the current standard of care, and it could be used in combination. The CHEK1 inhibitor, our glioblastoma, is IND ready, and we're looking at partnering this product. The third one is a product that we developed for ALS. Again, best-in-class in this particular situation. We're working on the IND, and we believe this product will be IND ready by Q2, 2024. We have a couple of other products, one in Parkinson and one in fibrosis, and we look forward briefing you on these two additional products later on. Okay? So this is our pipeline, and this, well, I mean, we look very much in this particular vertical like a traditional biotech company, with the differentiation of having, again, best-in-class or first-in-class products. The third is the application that I mentioned, which we call our Knowledge Exploration Tool, and the question that we ask ourselves is: can we leverage the platform to offer scientists tools that they can use every day in their practice, if you will? You know. And because at Benevolent, we have scientists, both on science and technology, they know firsthand what scientists need in their daily practice. So we have a couple of products that are currently in the development stage, and we're developing also our internal resources to take them to the market. So as you can see, in the last paragraph here, these products are currently being assessed with end users, and we look forward, again, sharing with you more information about these product launches. The beauty of our model, if you will, if you think of it, is partnerships are very important financially because these are big, big amount, and they're spread over time. Quite a bit of resources, but once the investment is made, we can start through milestones, gain, if you will, from the partnership over a long period of time. Our pipeline is, again, long development, but then if we are or when we out-license them, it's an immediate return to the company. Those knowledge exploration tool is, again, a very different revenue stream because once these products are there and we have the hockey stick, they can generate immediate return over a relatively short period of time. So that's our business model and at Benevolent. Now, when we talk to partners, when we talk to customers, when we talk to scientists, they say, "Everyone and his cousin now has something in AI. Why are you different?" Okay? Well, we are different, and probably our major differentiation, besides the uniqueness of our platform, is the fact that what we've done has been validated, so we can show results. So, for example, as I shared with you with AstraZeneca, now the products are in the pipeline. They're real. When we look at Merck, again, they run a very, very extensive process, and they selected us for our capabilities. But I'd like to draw your attention to the third point here. Now, it sounds as if it was a century ago, but it has not been, because if I take you back to early 2020, okay? I'm sure all of us remember the crisis we were in, right? COVID was now a reality, and everyone was trying to look for a solution, okay, whether vaccine or treatment, and you all remember the saga about potential treatments. Now, one of our investors asked our former CEO, and the question was a very simple question. The question was: "Can you use your platform to come up with something to treat COVID?" Okay? The question was taken back home, and we had our scientists look to answer the question. Now, the challenge back then was we did not have time, so coming up with a novel target was not a good idea, but time was not our friend. So the team looked at approved drugs that could eventually treat COVID, and lo and behold, they found something, and it was this product, a product that was marketed by Lilly for rheumatoid arthritis. Okay? So what we did is we picked up the phone and called Lilly, and we said, "Guess what? Your product that you're marketing for RA could be used for COVID." They did not hang up on us, but just about. You know, "Who are you, BenevolentAI, telling us Lilly what to do?" So we approached. Actually, we had a partnership with the NIH. NIH ran the trial, and baricitinib demonstrated that it can be useful in treating COVID, okay? So the FDA granted Lilly an emergency use in November 2020, and the drug was approved in May 2022, okay? This is, if anything, a validation, a proof that our platform can generate a product that can be approved by FDA, okay? And I can say safely that we're probably, if not the only one, but among the very few. Actually, I think we're the only one who did that, okay? It's a good validation. And last but not least, is the series of products that we are developing internally as best-in-class or first-in-class. Now, if we look at our business, we have a healthy business. We, back in, June of 2023, we had about GBP 84 million in the bank. As you know, we are a U.K. company, so we report results twice a year. So, stay tuned. We'll be reporting the results in the first quarter, 2024. Okay? Back then, our, for the first half of the year was, thirty-eight million pounds. We reduced this burn quite significantly by reducing, right-sizing the company, and currently, we have about 265 employees, and we have a runway that will take us to mid-2025. Interestingly enough, the Merck deal will start generating revenues for the company in 2026, okay? Even though we will extend, try to extend our runway, we will hope that we will have revenue in 2026. Now the question is: What is it that we do with the cash in bank? We're very diligent and very focused in our investments, so we're spending and investing in our lead product, conducting our phase I trial that is about to be completed. We're funding our IND in GBM, funding our IND in ALS. We continue to enhance our Benevolent Platform because technology is moving very, very quickly, and we need to continue to be among the best in the industry. We are investing quite a bit in our knowledge exploration tool. Cristina Busmalis is here, just joined us as Chief Revenue Officer, and she's building a commercial team that will focus on business development and commercialization, and that's what our investments are all about. So in a nutshell, that's the company. We are very, very pleased to be among the pioneers and the leaders in AI-augmented drug discovery. We happen to be, as many of these companies, at the right time because AI exploded in its different applications. The major difference is we've been at it for 10 years, and frankly, we have validated our platform through its output in multiple different ways. We have also de-risked the company by right-sizing it, by keeping a very, very strong focus on burn, but at the same time, by diversifying our revenue stream through the end-to-end discovery, through our pipeline, and through the knowledge exploration tools. And, one of the reasons, frankly, I joined Benevolent, there were many reasons, is because of its logo. I don't, I don't take credit for it, but at the end of the day, I remind myself, and I remind the team, that AI for AI is not terribly useful, but AI to serve patients is exactly what we intend and we are doing at Benevolent. And the reason is because it matters, and that's very important for all of us. Thank you very much for your attention, and happy to take any questions. Yes? Always at the opposite side of- I know. With so many new companies at the JP Morgan conference, and so on, with coming up and becoming highly capitalized, what are your thoughts on some of the, some of the newer and older companies basically consolidating? Because it seems like it's gonna be inevitable, as you know, some of the more well-capitalized companies start to, you know, stake out their own share of this market. Yeah, I think, to be perfectly candid, it's a safe assumption. And we've seen it. We are starting to see it now, but we see it- we saw it also in different other applications. Remember the gene therapy companies and they cons- I mean, there is a history of consolidation in our industry in general. There is also a history of consolidation in the tech industry, and a company like Benevolent is at the intersection of both. So consolidation will be the name of the game for multiple different reasons. And for Benevolent, for example, we always look at, at add-ons because you have folks who have, developed very smart technologies but do not have the means to take them further, and that's something that we're, we're continuing to look at. So we don't have the privilege of knowing everything about everything, and therefore, consolidation in terms of bolt-ons is something we're seriously considering. But at the same time, if I look at the industry at large, you're absolutely right. I see consolidation coming up probably in the short, medium-term horizon. Absolutely. Yeah. Thank you. Good question. Yes? Yes, thanks. So I guess a slightly philosophical question. So it's the year of generative AI. Everyone's talking about it, but as you said, you've been around doing this for 10 years, and you've built up platforms, processes that integrate into the drug discovery pipeline with first—well, the last generation focused ML, AI. How much of your platforms have you repurposed with generation, generative AI? Is that an ongoing process? Do you have a clear view of where to use the focused AI and where to use the generative AI? Yeah. So, we have about 100 and some people in our tech group, and I can tell you that about 25%, so about 25 people, are doing exactly what you're suggesting, right? So we're devoting quite a bit of investment in continuing to be upstream because. A couple of reasons: A, the environment is changing, to your point. We need to make sure that we integrate any new technology we learn from our mistakes and mistakes of other companies. But also, what we're noticing is our partners are becoming more and more sophisticated, okay? Many of our partners in big pharma and medium are starting to have their own AI nucleus, if you will, within their organization, and they're up to speed. So we need to be ahead of them, because otherwise they won't need us, right? About, I would say, 25% or 30% of our internal product and tech resources are devoted to doing exactly that. Yeah. Sorry. Yeah. As a non-specialist in the field, could you give us a little color on the competitive advantage of your platform versus Schrödinger, Exscientia, Relay, so all of these company that's say they started as AI company for drug discovery. What is Yeah, each one- -in the core? That's a very good question. I wish I had the slide here. So we, we keep an eye on them, and they keep an eye on us. That's what it is. I would say what makes us a little bit unique are probably three things. One, we have been at it for a long time. B, our focus is really target ID, other companies have other focuses, and we developed our own pipeline. We did not inherit a pipeline like others. And the third is the slide that I showed, which is the validation, and I'm not saying they do not validate. Some of them did, and did a great job. So in a way, I'm happy that we have competition, because it shows that this is a growing field. Again, with Merck, for example, Merck from the get-go, they said, "We want to have at least two partners working with us." We happen to have one part of the pie. Exscientia got the other part, and that's totally okay. So, we will compete. We'll continue to compete with them, but again, you mentioned companies that are there and doing a great job. Our concern, collectively, is to make sure that others, kind of fly-by-night individuals, do not harm what AI can do in drug discovery, okay? That's probably our concern. So the notion of validation, continued investment, and frankly, innovation, is our stronghold. That's what we need to continue doing. Yes? Following up on that a little bit, a lot of the new companies that have come out as well are heavily focusing on brand-new, wet lab techniques or sometimes integrating huge amounts of data from existing wet lab, techniques, like lots and lots of 10x Genomics. Do you see either a greater emphasis, maybe in your company or in other AI companies, of trying to integrate, some wet lab techniques into their system in order to generate, you know, more, either more or different hits or offer something new? Yeah, that's one of the differentiation for us, because we've had wet labs for a long, long time. And wet labs in our structure, if you will, our, our biology experts, our science experts, and our techs, they work hand in hand. So to your point, that's one of the differentiations that other competitors do not have. And we have wet labs in Cambridge. They're very, very efficient, and we will continue to develop them. One of the, one of the reasons, frankly, partners choose us is because of this specific capability. Absolutely critical, in my opinion. Plus, I mean, we can always contract out and do other things, but, you know, when you have your own, your own scientists, your own wet labs, your own technology... I was talking to our CSO literally last week about exactly the same topic, and the turnaround time when we do it in-house is and the quality, frankly, is ten times better than contracting out, you know? I mean, we can turn around and shift priorities. It's ours. It's our team, and they're committed, versus contracting out, then you take a, you know, you take a spot on the long laundry list of others competing for the same resources. So that's one very important differentiation for the company. Yeah. All right. Well, thank you, everyone. Thank you for being here. Have a good rest of the day.