Okay, I think we're gonna get started. Awesome. Welcome back to the 46th Annual TD Cowen Healthcare Conference. I'm Brendan Smith, one of the Tools/Dx analysts here at TD. It is my pleasure today to be joined by the CEO of Simulations Plus, Shawn O'Connor. Shawn, it's great to have you. It's good to be here. Thanks for the invite. Awesome. As with all of our other fireside chats, just a reminder, if you've got questions, feel free to kind of flag me down at some point, or you can send them over email to brendan.smith@tdsecurities.com. Shawn, maybe before we even get into kind of the outlook for 2026 specifically, let's kind of level set, really how you're kind of framing the Simulations Plus platform these days. Really maybe let's just kind of highlight what you see as kind of the most important differentiating features that really set SLP's platform apart versus some other folks in the space. Yeah, sure. We have several point solutions in the biosimulation space that run the gamut from applications in the discovery space with ADMET Predictor through into the clinic with GastroPlus, Monolix, our QSP platform, and additionally, our Pro-ficiency platform that supports clinical trial protocol training efforts. All of these platforms, I'd say an umbrella characterization is their ease of use. Scientific tools are often developed by scientists and not necessarily software developers and differentiation very significant in terms of the ease of use of our products. These platforms, however, compete at a scientific functionality and capability level, and our strengths are quite loud in terms of ADMET Predictor in the discovery space in which common in our industry to have bake-offs. Accuracy of the predictive output of these applications are tested against each other. We always come out on top, through GastroPlus, which is viewed as the PBPK platform that covers the breadth of functionality, the breadth of use cases for that type of modeling with real strength in translational medicine, formulation, a very hot topic these days with animal testing announcements. And Monolix, which competes with a product that dominant market share product, first to market 30 years ago. Monolix is in fact our largest or highest growth software platform because of its workflow orientation and integration capabilities, breadth of functionality across report writing and other ancillary functionality around a PK application. So science first in terms of the models, predictive capabilities of the solutions, ease of use is a very distinguishing factor as well. Gotcha. Okay, great. Can you help us maybe understand, across some of the different products that you just outlined there, are there any kind of important differentiators when it comes to the different customers that are using different aspects of the platform itself? Is it, and I guess I'm really getting at kind of end markets. Is like, you know, pharma especially inclined towards one versus the other, emerging biotechs? Is there any kind of important distinction there, or is it kind of, you know, you have kind of a single touch point that tends to evolve across the landscape? Yeah. Our business covers the gamut of, you know, large pharma through, you know, distinguishing sizes of pharma into biotech. These applications are used at different stages of the drug development cycle. An orientation with biotech more typically being earlier stage in their needs, our ADMET Predictor and PBPK platform is on the uptake there. Large pharma is across the board in their capabilities. In the end, we're probably about 75% pharma, 25% biotech in our user community, really driven by the fact that our solutions, our revenues are driven 20% in discovery, excuse me, 80% in clinic. That differentiating in terms of the timeframe in which biotech sits, drives that percentage breakout. Gotcha. Okay, great. Now maybe we can turn to the FQ1 update, right? I think on the call you guys kind of reaffirmed expectations for, you know, roughly flat to 4% growth in top-line revs for FY 2026, I should say. Can you maybe just walk us through what are some of your more important underlying assumptions behind that number? Kind of in that context, what you see as kind of the more potent and potentially easier opportunities to kind of beat on the upside of that as you kind of look at the broader market recovery. You're less aggressive than some of the investors I've been meeting with today. Our second quarter ended on Saturday, they're looking for second quarter reports already. I got to stay within first quarter environment. We came into the year and set guidance of 0%-4% growth pretty conservatively for the year. Came into the year out of the year-end budgeting cycle of our clients with some real momentum. That translated into a first quarter in which our service business overperformed to expectations, reflecting the fact that as budgets loosen, the head of modeling is more quickly able to open up contracts for outside services as opposed to growing his organization and software needs. We exited the first quarter with a backlog which was up at two-year two years ago heights. This window of time in which cost constraint and biotech funding has certainly hampered the flow of business in our marketplace, we do see some real positive positive trends there. I think the thing to look for as we move forward through the year is to see that service overperformance continue, obviously, but most importantly, see the software side of our business pick up. First quarter is we have a seasonality to our software business driven by the renewals, timing of renewals in terms of 12-month licenses. First quarter is always the smallest quarter in terms of software contribution. It performed well in the first quarter, but being a small quarter, second, third, and fourth quarter, as we progress through the year, key metric we'll be seeing that software number pick up. Our guidance or expectation in terms of this 0%-4% growth is really undercovers a trend or covers up a trend, I should say, of relatively slower percentage growth in the first half of the year, given that we had some pretty stiff comparables a year ago. Our growth in the first quarter was negative. Overall, our growth will be in that ballpark of, you know, zero to maybe a little bit positive. Back half of the year steps up in its growth, not because we've got a hockey stick and expectations of the market picking up, but really driven by, the comparables from last year. Underneath the 0%-4% guidance for the year, is really a trending back on an absolute dollar basis towards our 10% growth, and that'll be reflected in the back half of our year. All right. Great. You referenced kind of this, revenue mix between software and services, right? I guess, given some of the volatility last year just across the sector, I mean, you mentioned the, you know, we should watch for kind of broader services recovery this year in particular. Where do you kind of envision, the balance between software and services revenues at out of 100% between the two over the course of this year? Is there any kind of, recovery momentum that you can kind of point to that would suggest any kind of differentiation from what we'd expect historically? You know, we have consistently over many years, been in that sort of 60/40 is what I put out all the time. It ranges on the software side in terms of contribution between 60% and 65%. I think we will stay in that sort of range, as we work our way through this fiscal year, not a dramatic change there. We are focused for all the obvious reasons in terms of that software growth, and I think the potential is to drive that split towards the 70% or maybe even above, not necessarily in fiscal year 2026, but a longer-term basis, something we'd like to do to reap the benefits, obvious benefits of recurring revenue, more stable revenue flow, and margin. Got it. Okay. When we're talking about growth of the software business, how should we think about kind of the different initiatives within SLP now? I guess there's a kind of a constant conversation of, you know, obviously the biotech pharma funding recovery, and that environment is kind of improving versus last year. Also, you know, there's the context of the initiatives coming out of FDA. There's also just broader awareness of a lot of these technologies too. I guess, how do you kind of think, you know, when you're calculating that growth over the next 12-18 months, what's kind of the bucketed contribution in your view and how that's kind of contributing to your sales strategy and outreach? Large pharma, the pharma segment is as it has been going to be a more significant driver. Certainly biotech funding increases our opportunity in that segment, especially in terms of new logos. Entity creation on the biotech side is more frequent, new entity, new logo opportunities. The pharma side is a wealth of opportunity for us that comes from both seed expansion growth in terms of those departments, which have been held pretty stable through this cost-cutting environment, and yet we see those budgets opening up. The postings for hiring for modeling and simulation are getting more rampant. Very important in terms of protecting our scientists, and retention there on that side, so there's always two sides to the story. We see growth in terms of staffing there. We see opportunity that in the cross-selling. A big focus on our part is cross-selling. The number of accounts that have our full suite of platforms is relatively small. Most common, they're using one, maybe two, but not all four, five, six of our platforms. We in this past year reorganized our sales approach, our go-to-market strategy, and translated our sales force from product quota-carrying salespeople, I sell one of my platforms, to a geographical and account ownership structure such that I own Lilly or I own a geographical territory. My quota can be achieved by new sale, new logos are a part of it, but my ability to cross-sell into existing accounts is high motivation in our sales organization, and rightfully so. It's a big opportunity for us. It also fits with market dynamic in terms of a consolidation of the various techniques of modeling being used more and more often in conjunction on the same use cases. Having tools that are integrated, such as ours, that can approach use case problems from a multi-biosimulation disciplinary approach, is of greater and greater value to our clients. On the software side, there's growth as we've historically enjoyed through growing modeling and simulation organizations, but real focus on cross-selling. Yeah. Okay. Got it. Maybe kind of tied to this, are you at this time kind of targeting any new products, development areas, kind of over the even near to medium term, or is this really kind of a matter of, really fully monetizing and maximizing your exposure with the existing portfolio of offerings that you've got? Today and tomorrow we gotta maximize what we've got in hand. We recently in January presented a product, new product roadmap for the organization, which is really reflective of the evolution in terms of our clients moving from a development process that has been organized and workflow oriented around trial and error, and evolving towards a more data-driven MIDD is the acronym, development program. They are investing in retooling their organization. We might talk about AI at some point. That's a driver in what they're doing, and they're investing in their data capabilities, organization and access and curation internally. Our product roadmap as we introduced in January, translates our presentation to our clients from a group of point solutions, our engines, our ADMET Predictor, GastroPlus, Monolix, engines, into a ecosystem. Layers on top of those engines, the use and deployment of agentic AI, into an ecosystem that can fit into certainly large pharma, their internally built ecosystems, and provides an ecosystem for the medium-size and smaller accounts who are not resourced to leverage AI opportunities independently. This effort, you know, is staged in its delivery, six, 12, 18 months out, as it rolls out. We delivered the first instances of it in GastroPlus, released back in September. They've been well-received at the GastroPlus level. The real value and impact to our clients will come as that spreads across the other engines, the other platforms, and the ecosystem is available there. Very excited about our revenue expansion opportunities that come with this product roadmap and marries well with change that's taking place in the development approach in drug development. We've built this off of close collaboration with three significant large pharma accounts and are very excited at what lies in the future despite the challenges the software market has had in the market over the last month or so. Gotcha. Okay. Yeah, I mean, let's maybe dig into some of the AI capabilities a little bit more. Yeah. I think that's a good segue. I guess maybe first off, help us understand. I mean, you kind of benchmarked six, 12, 18 months as kind of a lot of these products continue to roll out. Yeah T he kind of next gen capabilities. How do you maybe expect to incorporate, you know, potentially new pricing models into the conversation? You know, what kind of context can we think about for what impact that could have on kind of customer adoption and ultimately, like, what they're able to kind of do with that added premium? Yeah. Multifaceted. We've lived and succeeded off of a seat-based license sort of concept over the years. You know, like a lot of startups, I look back 30 years ago when we started out, we priced to get into the door. We certainly 30 years later are not priced, value priced with our clients in terms of the benefits they receive in using our application. In part, this supports our ability to value price our capabilities. How do you get there? You get there through an ability to deliver higher productivity and efficiency and capability, and that can translate into a more aggressive pricing of our ecosystem, if you will. We have historically. It's a very sticky set of applications. We have historically always leveraged with annual price increases. Those price increases can be much, much more aggressive when you're delivering more value to the clients. Some of the capabilities may be specific to function or department and may lend themselves to separate modular pricing. Something that our price list, if you will, reflects already with our existing applications, with multiple modules to build the configuration of platform that our clients want. More modules can be created and more revenue streams created out of this. Finally, as well, as this lends itself to, you know, in two direction areas in which there's higher computational activity, a token or per-click pricing scheme will be appropriate in some areas. As it becomes more of a shared service capability to the medium or small-sized companies that couldn't necessarily, A, build their organizations internally or access the full complement of capabilities on a standalone basis, may through a SaaS environment be able to start using on an as-needed basis the functionality of our products. It may open up clients that heretofore weren't necessarily available to us for as software clients. Gotcha. Okay. If we're talking about, you know, assumptions built into 2060 guidance and your projections into, you know, the next 18, 24 months, is it fair to say that, you know, the upside impact from potential pricing increases down the line is not necessarily baked into where numbers are now, but if, you know, uptake of the initial AI rollout really does kind of gain steam, that would potentially kind of inflect upwards, if not in the next couple of quarters, you know, 12-1 8 months time horizon. Is that fair to say? It'll be the fifth quarter, midway through the fifth quarter of which we could be so accurate as that. Let me put it this way. We did not bake any of this into our guidance for fiscal year 2026. Maybe a little bit to the extent that we were a little bit more aggressive this year in terms of our price increase. The real value driver of the AI deliveries, we held that out in terms of any guidance. Maybe there's a little bit of upside. I think we're gonna see the real value accrue to us in our fiscal year 2027, which starts in September of calendar year 2026. Got it. All right. I think all of this conversation does also kind of lends itself to questions about kind of these initiatives coming out of FDA, right? I know that's something you and I have talked about a few times. Help us understand, you know, within the different offerings that you guys have today, number one, are you hearing from customers or potentially new potential customers in light of kind of the new alternative methodologies push out of FDA? Really kind of which software offerings within your portfolio kind of fit most cleanly into some of those pushes? You know, it is still a conversation, topless conversation item. It's not an engagement traction at this stage of the game. You know, it was April of last year when it was first announced. It was early December when first pass of guidelines came out. That'll go through a comment iteration, maybe two. You know, inevitably, I think even when the guidelines are set, they'll be nebulous enough that the first opportunities for an altered approach being approved by the FDA will come from a couple of drug sponsors that wanna be the guinea pig. They'll probably in the background say, "Let's do the animal test at the same time just to be sure." My point is only that, as we've seen with other use case developments, applications of biosimulation, there's a gestation period that accrues over time. You know, we've built expanded biosimulation through these long gestation period use cases that provide tailwind over time, but they do take a while to get over those hurdles of both scientific and regulatory acceptance. This will be no different. Feel very well-positioned as it comes together. Our GastroPlus application and its focus on preclinical and first-in-human studies, our QSP capabilities for toxicity. These are tools that are used today in defining animal testing protocols with an objective of ensuring that the quality of output of that animal test meets the expectations of need for first-in-human, and as well reduces species populations for those studies. The bar is raised. How can we get it to zero population and avoid the animal test? These approaches that we've had been using to optimize the process will be the same approaches that are used to develop the answer to how can we avoid the test. There may be some different data points, there may be some different end objectives, but our tools are well-positioned to reap the benefits of this. Yeah. I mean, ultimately, it feels like, it's not like you need to flip a switch, and then animal testing is all of a sudden gone, and then who else can kinda step in and fill the void. Seems increasingly that it's, you know, really an understanding of the proportion of how much of the data has to be generated from actual animals and what your options are elsewhere to kind of fill in some of those gaps. I guess when we're trying to kinda model out the opportunity here coming from the NAMs initiative, I mean, are we talking, like, on a five- 10-year-plus time horizon in terms of a meaningful shift that could actually be seen on a revenue standpoint? Or is it some, y ou know, is this something within a couple of years as people start to get comfortable with the guidance? You know, you're talking about iterations of the letters and stuff. You know, is it fair to say within two-three years, we should start to see kind of at least meaningful improvements in, like, the adoption curves of this and ultimately some of the revenues that are bearing out? I think back in April last year when it was first announced, the over-under was three years before it becomes a reality. You know, I don't think that that timeframe or expectation has changed. You know, we're still a couple of years out before it becomes a, you know, engaged application use case. Okay. This is also something you know, we've kinda come back to yesterday and earlier today and on a lot of the AI sessions this week too. You know, we're kind of seeing record dollars from pharma and biotech kind of put into a lot of these capabilities, really across their entire R&D workflows, right? I think for a lot of us on the outside, there's always a question of, okay, as they're spending more on some of their internal capabilities, what does that ultimately mean for some of the licenses that they kind of outsource? What's kind of your take and what's the feedback you've heard, and what you're expecting to kinda come out of additional pharma dollars being put towards some of their own capabilities as it pertains to, you know, the opportunity for you guys to kinda step in there too? Yeah. You know, AI is a umbrella term that can mean a lot of different things across a 12-year cycle time. There's lots of opportunity. What does AI do really well? It can compute quicker and it can automate processes. These are all great things for biosimulation and where we see our clients, especially those that we've used in the development of our roadmap here, those dollars in the world of biosimulation have been invested in how do we get our data in order? Pharma companies are rich with data, you know, years and years of data that have been accumulated. They haven't always known where that data is. As they were looking for, you know, where do we spend internally on AI, well, the starting point is here, let's get our arms around the data. You know, that is a great first step, again, went into our roadmap ecosystem of our applications to integrate to their data AI investments here. Boy, I've gotten the question over and over again. You know, are they going to replicate our engines, our modeling engines? Boy, you know, we've operated for many years with open source solutions out there that have been free. Those have not been adopted for a number of reasons, but I'd point quickly to investing in a modeling tool is a lifelong investment. it's not just building it once, but maintaining and improving and adding to it over time. Very significantly, you know, open source solutions are not well-received at the FDA when you knock on their door. ultimately, whether that open source solution is made by a large pharma company, the same scrutiny that open source solutions get from a regulatory body will be there. then, you know, very importantly, you know, it's the modeling process is still a hypothesis-driven scientific process. Very few models are built by just give me the data, press the button, and the model comes out. The scientific process that not only starts with the curation of that data, but goes through the validation and perfection of that model to get to a point where the model is truly reflective of what this drug candidate may be exhibiting, or may not be exhibiting, how it does impact the biology in which it's entering, you know, not a simple process that AI can read the manual and do it themselves. It's still a process, and our application of the agentic AI in our ecosystem automates a number of processes that can be automated, but it inserts the scientists very frequently in the process to make the decisions and choices that are necessary to build biosimulation models. Okay. Maybe just in the last minute or so here, kind of wanna zoom out just a bit. I mean, we talked about the platform itself, the evolution, the different offerings that you all have, the kind of, you know, rollout and integration of AI moving forward, you know, expectations for growth this year and kind of into next year, some of the FDA-related tailwinds there. I guess, you know, when we have conversations like this, what are you kind of feeling is inherently kind of the biggest disconnect between where you see the real actual value opportunity for Simulations Plus when it comes to revenue growth over the near term and maybe where a lot of us on the outside are either distracted by or spending most of our time? Where is the actual biggest gap in understanding SLP's valuation now? It-it's kinda where we just left off. I feel like Yogi Berra, déjà vu all over again. It was, you know, five years or so ago when great capital infusion into the startup market, AI companies that were gonna revolutionize the drug development process, the Recursions and BenevolentAIs, et cetera. You know, I fielded all of this competitive questions. Five years later, not to belabor it, many of those, if not, you know, majority of them are clients of ours. They license our software. They did not embark upon duplicating or replacing. They all became drug development business models. So here we are again, and, fair enough, AI's continued to evolve. AI is a tremendous tool, a tool that we started using in the 90s and is embedded in our ADMET Predictor product. We've, you know, developed along with the development of AI, and we're very aggressively utilizing it as a tool today to make this process that much more efficient. All right, great. I think with that, we are at time, so thank you all for paying attention. Shawn, it's always good to see you. Thanks for joining. Thank you. Take care.