We'll kick things off. Thanks, thanks, everyone, for joining us. My name is Michael Ryskin. I'm on the Bank of America Life Science Tools and Diagnostics team, and I'm excited to host our next session, Schrödinger. We're joined by Ramy Farid, Chief Executive Officer; Richie Jain, Chief Financial Officer; and Patrick Lorton, EVP, CTO, and COO of Software. Ramy, Richie, and Patrick, thank you so much for being here. Thanks for taking the time. Thank you. Thanks so much for having us. Looking forward to the discussion. The format will be a fireside chat with some Q&A. If you've got a burning question, raise your hand. Oh We'll incorporate you in. Maybe just to kick things off, Ramy, maybe one for you. Just, you know, you recently reported 1Q earnings and, you know, reiterated your full -year guide. What are the key takeaways from the quarter, you know, how it played out in regard to your expectations? Yeah. There's been a lot of moving pieces in the model. a couple months. Yeah. Let's talk through it a little bit. I mean, of course, the performance itself was, you know, we were very pleased. We actually slightly exceeded the guidance that we gave. I think, beyond that, I mean, obviously that's good, but beyond that, I think how broad the source of growth was. You know, we saw existing customers, large customers, scaling up their usage. We saw companies adding new products. That's always an exciting thing. You know, when you introduce a new product into, you know, out there in the world, it's not a given that there'll be acceptance of it. That was good. The other thing which is encouraging, I think maybe for all of us, is that unlike last year, where we certainly saw quite a few companies losing their funding, and we were losing customers 'cause they were just disappearing, we saw the opposite this quarter. That's encouraging. Now, we're all hearing kinda things seem to be getting better, but it's nice to see some data to support it. You know, we actually added new customers, new logos, it sort of feels like we haven't seen that in a little while. The broadness, I think, of the growth and the quality of the discussions; the interest, you know, the interest in the platform; and then again, particularly the interest in new products was quite encouraging. Is there anything else you think we should add? I'd just add, as we drive towards profitability over a three-year time period, you know, first quarter out on that path. Right Increasing revenue across software and drug discovery while maintaining expense discipline. OpEx was down 4% quarter-over-quarter, so just the start of that journey. Yeah. It was a pretty encouraging start to the year. Okay. Yeah. Then in terms of the 2Q guide and sort of the p acing through the year, there's always, you know, some pretty seasonal- Yeah quarters. Nothing unusual call-out there, sort of in line with historical trends in your prior expectations, right? That's right. It's typically a smaller quarter. Well, oh, not typically, always. Most of our renewals are in Q4, but, you know, we're optimistic about the continued growth opportunities in Q2, and I think the guidance reflects that. Yeah. Yeah. Just Q1, Q2, we're out of the gate strong on hosted conversion as well. Yeah. You know, hosted revenue as a percentage of total software revenue was 34% for the quarter compared to 24% a year ago. We're pleased with that, you know, performance out of the gate there. Okay. on the 2Q guide, when you were talking about ACV. You had a slide in your deck where you did a sort of like a year-over-year comparison, and you had sort of, you know, adjust the last year's comp. Could you just walk us through that, remind sort of like what the adjustment is and why the year-over-year is what it should be? We guided $19 million-$23 million of ACV for the quarter. A year ago was $23 million total, of which five was a contribution ACV related to the grant funding we have from the Gates Foundation on predictive toxicology. On an apples-to-apples basis, the year -ago comparison is really $18 million, and our guidance is $19 million-$23 million compared to that. Okay. 4%-27% growth. In terms of 1Q, you saw effectively de minimis ACV contribution from that. Correct. There was no contribution from Q1. No contribution. Yeah. Yeah. We called it out in Q2 just to make sure that you're looking at the comparisons the right way. Yep. Right. Okay. Okay. Maybe let's talk about that ACV transition, sort of the focus on the ACV as the KPI of the business going forward. Yep. You know, you made a big change to the model on that, the prior earnings call. You know, it's been about three months now where you've sort of been talking to investors about it. What's been the feedback on that? How has that gone over? Because it is a pretty, you know, meaningful disruption to the model. Yeah. No, I think people get it. I think they understand that, because we're not the first company to undergo this transition, right? From on-prem to hosted. In the year that you facilitate that transformation, the revenue has to go down. I mean, it's math. I think companies are used to it; investors are used to, Okay, let's look at the thing that actually measures the business, which is ACV, and really, it's okay to ignore the revenue. It will catch up. In the meantime, focus on the ACV, and it seems like people are focusing on the ACV, right? I mean, that's good, and we're in a funny situation where we're supposed to be bragging about lower revenue, right? The lower the revenue, the better the hosting transition is. I know that sounds weird, but again, I think we're not the first company to be doing that, so we're happy about the lower revenue because it means that the transition to hosted is working. That's great, of course, the faster you do the transition, the better, so that you rip that Band-Aid off, you focus on ACV in the while you're doing that transition, and then soon enough, we'll be able to go back to revenue when most of the transition's occurred. It's working. It's working quite well. The other thing that's great is we're not meeting with that much resistance. We even had some companies, we reported, switching from on-prem to hosted before the contract renewal. That's a good sign. You know, a few years ago, that wasn't the case. I mean, everybody was all worked up about hosted. What do you mean? You're gonna have access to our data? It's just a normal thing now, so, right? I mean, Right It doesn't seem like there's that much resistance. Yeah, there's virtually no resistance. Yeah. It's really gone from five years ago, it was almost impossible. Right A top 25 pharma to do anything hosted. Right. Now it's almost the opposite, where the CIOs are coming and saying. Yeah Do you guys have a hosted offering? Yeah. It's easier for them, right? Yeah. I mean, we're taking care of the things that we're good at, and they don't have to deal with it. It all works out for everybody. We should be clear. There'll be some regions; I think we've said in Asia, they're a little bit behind, so that might not happen, but that's okay. You know, it's progressing where we expected it to, very, very nice. Very well. Yeah. Yeah. I mean, on that point, you said, as you called out, 34% hosted. 24 years ago, you know, a 10-point jump. Yeah. I can do that math in my head. I hope. When you first announced this three months ago, I think you talked about 75% in three years. Right. Kind of gave us a roadmap. You're not guiding quarter-to-quarter on what hosted should be, 'cause that's kind of ridiculous. Yeah Still, could you put 34 in the first quarter in context of that? Sure. Are you a little bit ahead of schedule? Is it, you know, too early to read into that? Yeah, that's a good question. I'd say we're right on track. Yeah. The journey from 23% at the end of 2025 to 75% at the end of 2028—we've said many times that we don't expect it to be linear. I think the first year's gonna be a little bit slower on the transition as you measure it in revenue. Our actual progress in ACV will be much stronger, but the conversion of that to revenue in the fir st year is just reduced because it's the math part of the accounting. I think year one will be slower. Years two and three should be much faster on that path to 75%. I think we're right where we expect to be. Is 75 like a terminal limit? Is that just a three-year goal? I mean, do you expect it to get to 90, 95, 100? 75 was really set. I'll start, and Pat should add to this. You know, that was set on our evaluation of the customer set at the time. They're how they embrace hosted solutions, our view of the geographical differences, and customer differences. As this evolves, as we move forward, we'll, you know, keep you updated on it; that was our framing around 75%. Material science customers, right, are a little bit more- Yeah resistant than life science. It was a consideration of all of those things. Yeah. Yeah. Okay. It's worth, as Richie just said, it's worth mentioning that the 75 estimate comes from a moment in time. Yeah. Had you taken that moment in time three years ago, we would not have been saying 75%. It's hard to say what the ceiling will be in three years. Yeah. 75 is definitely a reasonable target today. Okay. We should add, too, that a lot of the new products that we're rolling out are hosted solutions. Again, that's another variable that we're thinking about, you know. Is 75% the endpoint, or is there some different number? Okay. Okay. As you said earlier, as you get higher and higher up, and as the change in hosted year-over-year diminishes, then the gap between ACV and revenues should shrink. Oh. Exactly. Yeah, absolutely. They'll converge. Yeah. Okay. Good What have you seen adoption pick up in. The past three to six months, is it broad-based? Is it more big pharma? Is it a little bit of everything, sort of? It really is; that's what I was trying to say at the beginning. It really is. This is a great sign. It's a little bit of everything. You know, new small biotech companies, material science companies. Certainly, you know, large companies are contributing in the way you'd expect them to. It's broad. Yeah. Really broad, yeah. One thing that's very exciting that we mentioned about how new products are contributing is if we close a new product sale with big pharma, it's almost always off -cycle because they have multi-year big bundled contracts. What that does mean is we've now, you know, by adding new products, the next refresh is going to be an even bigger bundle. And we're successfully selling our new products into places off cycle, which is really exciting. Yeah. Okay. I mean, something else, Ramy, that you mentioned earlier w as the new logos, in the quarter. Seems just sort of a more constructive tone on end market dynamics. It's just one quarter. It's been, you know, very choppy there for a while, but you know, we track biotech funding. Right New biotech funding. IPOs IPOs. Right. Things like that. Certainly feels, you know, a little bit better. Yeah. Does one quarter make a trend? You know, how much more do you need to see? There's a lag, right? Yeah. You know, it's gonna take time, but it's encouraging at least, right? It's certainly better. If we just look at the analysis of the quarter, it's better than Q1. That's last year. I think it is. That's a positive thing. I think it's obviously worth saying, too, that the lack of a headwind is better. Exactly That's— Yeah That's net negative, and we have to make up for it in other places. Right That reduction- That's right. is very useful for us. Yeah. I mean, any ideas on what's you know, spurring this? Is it the pickup in M&A? Is it newer technology? Sort of what's driving this? Funding environment, right? I mean, private funding, that certainly seems to. All the stats point to that. I don't think, you know, to the point we were saying earlier, probably the increase in IPOs, we're not gonna see that right away. That's gonna take time. Yeah. Right? To recognize that. I think it's that. It's actually funding. It's kind of what Pat's saying. It's sort of the lack of shutdowns, right? The ones who have- Yeah who have made it through the winter Yeah are heartier. Right. I'd say. Exactly. That's a good way of putting it, yeah. Okay. Okay. Once those customers, I mean, it's still early, but once those customers get their hands on money, are they spending it or are they kind of hoarding it? it? Because we've seen this dynamic play out. A couple times in prior years where y ou'll get a quarter or two of good funding, but you don't really see it come out. Yeah Still, you know, they're worried it'll go away. Yeah hoarding the cash. Okay. I think that's an exciting thing to think about because I think, and let me finish the thought because it's gonna start sounding off not good, but it's gonna end well. I think they're hoarding it, but that's good. They should be. What does that mean? That means they should be doing what they should be doing: investing in things that are actually more efficient and that actually work, and that's obviously computation. They can see that. I mean, look at the success of Ajax just recently, you know, that, and that's like one of many. They look at that company and say, How did they accomplish that? They have five employees, right? And got to this huge valuation and this great exit. I think it, the environment, is still such that, you know, we need to all be efficient. It's not like all of a sudden everybody's rolling in money. You need to be doing things in a really efficient way, and obviously using computation at scale is significantly more cost -effective and has a higher success probability than doing drug discovery by trial and error. We're also very fortunate; our track record of success with our collaborative partners, as those companies go, as the Nimbus and the Morphic and the Ajax, all these companies go through exits, a lot of their employees end up re-entering the life cycle of a biotech, and they have demonstrated success using our technology. Who's the first person they call? They call us before they get their CRO because they know they can start getting results immediately in computation. It's very promising, you know, the ecosystem we've built from these successful biotechs is very promising. Okay. You touched a couple times during your earlier comments on new products from your end. I mean, maybe let's start with predictive toxicology. Sure. Sort of, what's been the uptake? What's been the feedback? Yeah. You know, what impact are you seeing on the rest of the portfolio? Yeah, yeah. As we said before, we were very pleased with the results of the method internally. We started to get this into the hands of other customers, you know, outside the company, and there's one very, very encouraging sort of development that we're pretty excited about. Our vision for this was the way toxicology screening is done is it's done generally very late in drug discovery, right? You make molecules, and you're trying to optimize them since you're not able to predict those properties and you're not gonna run some expensive animal tox study, you know, through the whole discovery. You do it towards the end when you think you're close. That's a horrible way of doing things because, of course, you could be late. Failures are always bad, right? That's very expensive. That's what's happening, right? You put a molecule that you think is clean into a GLP tox study, and all of a sudden it doesn't work, and that's it. You have to go back to the drawing board. Usually that's when the projects get killed. Our vision is to move prediction of toxicology way earlier in the process, and we're really encouraged that our customers that are starting to get access to this have recognized this. That's how they want to use it. They want to use it early in what's called MPO, right? The multi-parameter optimization part of drug discovery, which is the very early stages. You want to be designing safe molecules from the beginning; you don't have these kinds of surprises. The cool thing about that for us is that requires using the technology in a much larger scale than the way tox screening is used now, which is what we were just talking about, right? Just a few molecules at the end. If it's being used on that scale, obviously that has a pretty big impact on the revenue that can be generated from it. We're really encouraged that people are recognizing what we were hoping would happen, is that this is getting moved up way earlier in discovery. It's probably worth reiterating, as we announced initially, we had a few targets, a handful of targets enabled at the initial release. Last year, we released even more, but this is part of the ongoing work with the grant, and we're getting more and more targets. The broader the domains we cover, the more interest this is to people. That's right. They might already know, you know, we're worried about this liability in this space. Unless we have that in our panel, Predictive Toxicology isn't interesting for that project yet. Virtually every day, we're adding more targets to the panel. That's going to make it more and more interesting for every drug discovery project out there. Yeah, that's a great point. How broad is that database or knowledge set now? Okay, yep. What part of this? Yep What portion? Where are we? real, yeah. No, it's a great question. Here's where we are. Internally, we've enabled approximately 100 targets. Okay. It's around 65 kinases, and then the remaining are other very important, well-known targets that you want to avoid, that you don't want to hit, that are associated, and that are clearly associated with toxicity. It's on that order of magnitude. Yeah. As Patrick said, it's true, we are adding them on a you know, very regular basis, almost on a daily basis. What's the end goal? It's probably a couple hundred targets I think is, would cover a very significant portion of the, as some people now are referring to it, avoid-ome, right? The targets you want to avoid. The avoid-ome. I think the fact that we've demonstrated that we have the infrastructure and the technology to enable these targets, it's become an engineering problem now, right? There's not a lot of uncertainty that we can get there. It's just a matter of enabling these targets, adding more targets to our tox panel, right? That when you run it, you don't have any surprises; that, you know, there's a target, an off -target that you're hitting, but that wasn't in our panel, and we couldn't predict it. That's our goal, you know, 150- 200. The good news is, I think the targets that we've enabled right now is enough to really get companies intrigued and starting to use it in production; we're actually seeing that. I think it's one of these things that'll keep getting better. Yeah It's good enough right now to get started. Yep. Is that- Absolutely, yeah. Yeah. Okay. When I think about the Schrödinger platform, you know, it is a platform. It is a number of solutions. We rarely go, or almost never go product by product, other. Mm-hmm. Do you want to do that now? Sure, yeah. Yeah. Line by line. Let's go. No, I mean, we've talked about FEP+ in the past. Sure. Now we're talking about predictive toxicology. Yeah. Is there anything else that really jumps out at you as sort of like, you know? Yeah No single item, I think, really moves the needle. Yeah What are the other important things? Yeah. FEP+ is very important because that's a workhorse for predicting the affinity of molecules to proteins, which is kind of one of the most important things. I mean, if your molecule isn't bind tightly to the target, it isn't gonna work. That's kind of a pretty important step. Now, that technology, the same exact technology, the same physics that underlies that technology, can be used to predict another property that's really important, which is solubility. Obviously, if a molecule is not soluble, it isn't gonna be a drug. It's not gonna be bioavailable. There's a solution for predicting permeability. Again, if the molecule can't get through the cell membrane, it's not a drug. It's not getting to the target. It's not gonna be bioavailable. Those are areas that we're excited about. The other really big area that we're focused on is what's required as input to these calculations. The input to these calculations is an actual structure of the protein, and it has to be an accurate structure. You gotta get the atoms all in the right place. You know, physics actually cares about where the atoms are and that they're in the right place. There is a lot of work going into, and we're making really cool progress here, really exciting progress on being able to determine the structure of a target to high resolution. That's enabling us to work on targets that were not possible to work on before. That's pretty important. Of course, it's the technology that enables the whole predictive toxicology initiative. Protein structure prediction, another really big area. We're also investing in our enterprise informatics system. This is essentially the platform where a scientist comes in the morning and works and sees everything that's going on in the project: what molecules have been made overnight, what the predictions were, what the experimental results, and what I'm going to make next. They design in that interface. They collaborate. They collaboratively design molecules in this platform we've developed called LiveDesign, and it's getting used very heavily in industry. Very large number of our customers have it. That is essentially the interface, you know, to all of this technology. It's fun to talk about all the science and these advances, but, you know, if you can't access the science, it isn't as valuable. We put a big effort into making sure that the interface, the way you access these very computational tools, is also something that's highly robust, scalable, and can deal with massive amounts of data, which we're now creating. That's another area that we're really investing heavily in. The other area, just to focus on just for a moment, I won't spend too much time in, but a lot of these physics-based methods that we're developing that are aimed at designing drugs are immediately transferable to designing materials. We do have lots of exciting technologies around, for example, designing batteries. That's obviously a pretty important area. Polymer coatings, for every application from aerospace to food packaging, and lots of other things. I don't wanna spend too much time on it given the focus of this conference. Those are very important areas, and we're very excited about them. And- Now let's make it. I was gonna say I'd love to highlight two of the big new things that we're focusing on. Ramy talked about LiveDesign, and it is used at the overwhelming majority of top pharma as well as many biotechs. It is used primarily for small molecule design right now. In the last year, we did our initial release of LiveDesign for Biologics, which targets, you know, the other 50% of R&D out there, and we're seeing really great interest. We have several big pharma that have signed up for pilots. We've a couple big pharma who have entered in co-development because they're gonna integrate it in their entire system. We see this as an opportunity to extend from our, you know, our base of small molecule strength into the biologic space where there's, you know, an equal sized R&D budget. In addition, what we announced in our last earnings call, Bunsen, is a new agentic AI that we've released. We are extremely excited about this. We've been using it internally since late last year. It has the ability to take a computational chemist who is completely overloaded and can only do a fraction of the work they'd like to do and give them a dramatic speedup similar to what we're seeing with Claude Code and developers. We might have referenced it before, but internally, when we're running programs, the reason we have the success rate is we have something like four computational chemists per discovery program. In the industry, that it's the inverse of that. It's something like a half to a quarter per discovery program. We're putting 8x-16x the computational resources, just human -wise, not even GPUs. We think Bunsen can help close that gap. We can take somebody who only has a half of an FTE to put on a project and allow them to accomplish, you know, 2 to 5 times as much, and in the process, use 2x- 5x times as much of our throughput-based licensing. It fits perfectly with our commercial strategy and our scientific strategy, so we're super excited about Bunsen and LiveDesign Biologics. Okay. A bunch of that I wanna follow up on real quick. Yeah. First, Ramy, your point on protein structure elucidation. Yes. Just to be clear, is that entirely computational, or is there an experimental component there as well? Oh, that's a- You've used cryo-EM in the past. That's a great question. There are many methods for producing low-resolution structures, which are an input to determining high-resolution structures. Experimental methods like X-ray crystallography, cryo-EM, and applications like AlphaFold, they all produce sort of a good starting point, but they're not the end result. Those structures are simply not accurate enough to use in sophisticated physics-based methods. There's another computational layer on top of that that relies very heavily on rigorous physics-based methods to take those kinda low-resolution approximate structures and get them right. We're developing those technologies, the latter. Of course, I mean, thank goodness for those other methods, right? Otherwise, there would be nothing to apply those sophisticated physics-based methods. It's highly collaborative, and you need all of that. You know, the experimental methods, the low-resolution computational methods, and then the high-resolution computational methods that we're developing. Okay. Okay. Yeah. On Bunsen, yeah, I think, I mean, I think you just announced that, a couple weeks ago, I think early access this summer. Yes. Right? Yes. On track for that. Yep. How do you expect the initial commercialization of that? to go? Are you targeting any specific accounts? Sort of what's that roadmap gonna look like? Yep. Yeah. As always, when we initially release something like this, we go to our close partners; we look for feedback. We, of course, with all new technology, I mean, everything, there'll be things that work really well and things that need to be improved. It's nice to know what those things are so we can prioritize them and continue to develop them. We have a set of our partners that we generally work with. We've already demoed the product to some of those. They're blown away. It. So far, it seems like the reaction is universally positive, that this is something they've been waiting for because they're struggling, right? They're struggling. They know that if they use the technology on a larger scale, they're gonna have a bigger impact. They're seeing that happening. They see that happen at Ajax. They see it happening at Schrödinger, and they want part of that, and I think they see this as an answer to that. We'll, you know, we'll get do the usual thing. We'll get that feedback. We'll incorporate that, have another release, and we'll do that in a pretty rapid, you know, pace. Yeah. We're pretty excited about it. It's. Yeah. One additional point I'd say that really does differentiate us from a lot of people in this space is we already have 200 drug discovery people using this. Yeah. We're because we have our own internal drug discovery. Yeah We can dog food with them, we're not going to release something that we think, "Oh, this might work. Hey, work." The customer feedback is the polishing phase. We already have computational chemists in-house that are telling us, you know, they're multiple times more efficient. We have medicinal chemists accessing technology that was impossible before. Yeah. We know this works. It is really just getting it to that final stage because there is a gap between an internal platform and a commercially sold platform. Yeah. We're really getting that final polish done to the point where it is a commercial quality platform. Yeah. Yep. Good. I mean, just on that point, just to play devil's advocate a little bit though. Sure. You're not the only ones launching some of these agentic solutions, right? Just in the last 3, 6 months, a lot of companies come out of the woodwork, both traditional tool vendors and more traditional AI or software vendors. Yeah. I don't wanna say that the competitive landscape is crowded, but again, it's not the only platform hitting the market. Yeah, absolutely. Do you have the name recognition? Do you have the relationships to sort of give you a leg up there? You know, have you been asked to do, like, head-to-head demos or sort of like what's that market look like? It's really interesting. We haven't been asked to do head-to-head demos, but we just actually demoed this to 14 of our top customers last week and got 100% interest. We haven't asked to do head-to-head demos because when they've tested the generic solution so far, they just fall flat. They don't have the specialist knowledge they need. One thing we've been able to do is through constructing specialized harnesses and skills that know exactly how to use our software, exactly how computational chemists work; we can build something that a generalist just cannot do. There's not enough information on how this works out there. Our core advantage is kind of what it's always been. We understand computational chemistry better than anyone else, and we're embedding that into the agent. That said, if one of these other agents works, the only way it's going to work is if it has access to our tools 'cause an agent can't make up for the fact that a tool isn't accurate enough. So if another agent beats us at one of these customers and it's using our tools, we will still capture the throughput-based usage of our tools. Yeah. That's still a win for us. We do think to do everything right, at least at this point from what we've seen, we have to own that because the skills of these agents not developed by us is just simply not there. Okay. Okay. We've only got a couple minutes left. Still a couple other topics I want to touch on. Maybe Richie, a couple for you. You talked about profitability and operating leverage in the quarter, just sort of tightening up that cost control a little bit. Could you talk about the ramp from where you are now towards EBITDA positive, and then maybe I'll just throw in, you know, Ajax, parts of the therapeutics collaboration portfolio, and maybe some of the incoming cash flows. You know, you haven't seen the cash come in yet. Yeah. How does that change your approach on where you're investing and how much? Sure. I'll take those in reverse order. We ended the quarter at $402 in cash. We should expect that to increase once we receive our 6% portion of Ajax's upfront with Lilly. That amount was not disclosed, but that will be an increase of cash for us. In terms of the use of our cash, this is mostly investing in operating burn over the next few years and investing in R&D and science to support a growing software business and drug discovery business. The extra use of cash, once we receive it, we'll evaluate what to do with it. I think we've said in many different forums, our focus right now is investing in growth. If there are adjacent, nearby adjacencies for M&A, we'll consider that as well. The outlook over a three-year time period is 10%-15% growth in software ACV while transitioning over to hosted revenue and having the revenue converge with ACV. $150 million of revenue in drug discovery over three years, so roughly $50 million a year. The timing of that can be a little variable, but $150 million over the three-year period. This year we're guiding to operating expenses being lower than last year, and then a disciplined framework on that going forward. The entire package there is what leads us to the profitability goal on adjusted EBITDA on a three-year time period. Okay. Okay. Maybe just with the last 30 seconds, Ramy, I'll toss it up to you. Any concluding remarks? Any last points you wanna leave us with? You know, what's been some of the more interesting feedback you've got, or maybe some questions you've had with investors you'd like to address? Sure. Yeah. I think, first of all, obviously, what we were saying before I think is really worth pointing out. It's very encouraging to see that things are improving. We're happy with the sort of broad-based growth that we're seeing. One of the things that I also really wanna highlight, that I think sometimes gets lost, there's a lot of noise out there, right? There's a lot of companies talking about computation and AI, and you know, I hope that investors recognize that these companies aren't all the same. I think our track record, which is pretty extraordinary, I mean, Ajax Therapeutics is just the latest example of that, but starting with Nimbus Therapeutics and Morphic Therapeutic and Relay Therapeutics and Structure Therapeutics and Petra, you know, the nearly 100% retention rate of our customer base and the tens of thousands of user using the platform and even, you know, we were talking about earlier today, companies, biotech companies that are using the software on this really large scale, seeing these really great successes. This is really an actual track record. We're delivering development candidates. We're delivering molecules that are making it through the clinic. There's, by the way, non-trivial royalties on sales associated with those, so we look forward to realizing the value of those in the future. I hope there's a recognition that, you know, there's a track record here of delivering with science that actually works when it's used at scale. That's differentiated from some of the other things that you might be seeing. I hope that doesn't sound arrogant, and it's not I don't like to sound critical of what other people are doing, but I think it's worth mentioning because it's a pretty big difference. I hope that was okay. No to throw in there. I think that's fair. Yeah. I think that's fair. Yeah. That's a good place to end it. I appreciate the opportunity to say it. Thank you. Of course. Of course. All right. With that, thank you very much.