Welcome everyone to our annual healthcare forum. We're kicking off things here with Schrödinger. I'm happy to have both Ramy Farid, the CEO, and Rishi Jain, the CFO. Welcome both of you, and thanks for-
Thank you.
joining us today on our virtual fireside chat. I guess Ramy and Rishi, maybe walk through for the investors that are new to the story, because a lot of it has even changed in the last six to twelve months about the Schrödinger platform and how you've made some changes?
Of course. Yeah. I'll start us off. At the highest level, what our goal is? Our mission is to develop a computational platform that allows researchers both in life sciences companies and in materials science companies to design better molecules more rapidly, more efficiently. That requires developing a platform that can replace experiment. Because the traditional way of doing drug discovery is by trial and error, right? You make a molecule in the lab, you assay it, check its properties, and if it doesn't have the properties you're looking for, which of course will always be the case when you start off a project, you start to optimize it. You try and make a change to the molecule, and obviously that's very time consuming and prone to huge failure rates, as we all know.
The whole goal of computationally driven drug discovery materials design is to do all of that on a computer and do it accurately, in other words, replicate the experiment, and do it on a massive scale so that you can test huge numbers of molecules and find that perfect molecule that somehow magically balances all the properties that are required to be a drug or a particular material. What we have actually successfully done, and we would argue, and I think with a lot of evidence to back it up, that we are the first and only company so far that has developed a platform that can replicate experiment reliably on novel molecules, completely novel molecules, new chemical entities, on a scale that is large enough to actually be able to find those very special molecules.
Here's the key, it's validated. We have, for the last 15 years, been actually using the platform ourselves to advance a number of drug discovery programs with companies that we've either co-founded or with pharma companies or on our own behalf, and the success rate and the track record is extraordinary. We've got 15, 16 programs in the clinic. All the biotech companies that we've co-founded have had really highly successful exits. The pharma collaborations are going well. Our own programs are progressing and going very well. On top of all that, we've been licensing that software to the whole entire industry. Every pharma company is using our platform, some at different scales, some at very large scale, some at a little bit lower scale, but everybody is using it.
We'll get to that in a second. Essentially our customer retention rate is 100%. What does that mean? That means the platform. That's another validation, right? I mean, you don't keep renewing an annual license contract if the platform isn't having a profound impact on the projects. Now, here's the thing, the most exciting sort of technology that we've developed is relatively new actually. We're still in the early days of sort of scaling up and achieving the true TAM of this business, which is far larger than where we are right now at around $200 million per year. That's, we have a handful, let's say, roughly of pharma companies that are using the technology at scale. That's very exciting.
There are other companies that are sort of still ramping up, and we see that as a tremendous opportunity for growth in the coming years for every pharma to be using the software at scale. Then the last thing I'll say is we're very excited about. We have a big investment in the platform. We're a science company, innovation company. We're leading the field. This year it turns out that we have a few very exciting new products that we've released. Probably one of the most exciting is Predictive Tox. It's one of the biggest challenges in drug discovery is predicting toxicity associated with binding to off targets. We released that this year. We continue to make scientific breakthroughs, lead the field, with new products.
We expect that will continue to also lead to growth in the coming years from adoption of these new technologies as we continue to reduce the time it takes to get to a development candidate and a drug discovery or a material and increase the probability of success with much higher quality molecules.
Great. I'll just add a couple of the recent changes that we've made reflecting the strategy that Ramy just outlined. We have made some changes to kind of simplify and clarify the business structure. We had been executing a few programs in the clinic on our own. We are seeking now to partner those programs. Ramy referenced 16 programs in the clinic that are advancing. Those are advancing in the hands of our partners, where we have downstream milestones and royalties on those programs. We put out a three year goal of achieving adjusted EBITDA profitability, which is achieved by growing both the software and drug discovery businesses and maintaining expense discipline. We also announced a change to focus and emphasize hosted contracts in the software business as opposed to on-premise contracts. We think that will also take about three years to transition over.
Today, it's about 25% hosted. We expect to get to 75% hosted. Given the way revenue recognition works, this is a very common transition for companies to make towards hosted and SaaS solutions. In the near term, it will have the impact of reducing revenue in, especially in 2026, all the while the business is still growing. We've changed and emphasized ACV this year as a business operating metric to track the business growth, while the revenue catches up to ACV over the course of the transition.
Right. Maybe as a follow-up there for both of you, why pivot towards this strategy? Maybe I'll just broadly leave it there.
Yeah. Yeah, you're talking about the transition, of course, from on-prem to hosted. That, right?
Exactly. Moving away from the internal pipeline.
Okay.
Away from the clinic. Yeah.
Both things.
Broad strategy.
Yeah, yeah, yeah.
The whole broad strategy, why now, and why do this?
Yeah. Yep. Yeah, it's an excellent question. What we recognized is that we have the reason why we believe that we have developed this platform and why it's working as well as it is, why it's as validated as it is the result of this unique synergy between our software business, licensing the software to, you know, thousands of users, and using the software ourselves in collaboration with other companies or on our own behalf in the discovery phase. What the result of that is, you can imagine how straightforward this is actually, that, by using the software ourselves, we're learning what works, what doesn't work, and we can improve it in real time. Of course, the validation is quite nice, right?
All this validation that comes from you know, creating development candidates in these high-quality molecules is very helpful in convincing a whole industry to change the way they do drug discovery. That's what we've done, right? Drug discovery used to be done by trial and error, and now we're using computation. That's what we wanted to focus on, those synergies. That doesn't require running clinical programs. Those synergies are sort of loss. Now you start getting into other factors, other things at play, right? Other than just designing molecules, which is where our core competency is, where our competitive advantage is. That's a, and i 'll tell you something else too what.
I mean, to be very honest, when we started the clinical programs, since then, the world has changed dramatically. You know, what is occurring in China and the way they're able to do clinical trials much more cheaply and more quickly, the cost in the U.S., the Project Optimus, oncology itself, you know, there's a lot of things have changed that have resulted in the cost associated with running clinical trials being quite a bit higher than we had expected. Again, focusing our capital on where it makes sense on these synergies, on the software business, on advancing the platform, and on the discovery portion of our therapeutics group is a big reason why we made this change.
I'll hand it over to Rishi to elaborate on that, and also it'd be good to talk about the hosted, I think, transition too. That's important. Yeah.
Yeah. I'll cover the hosted piece, which is we have been moving toward hosted contracts gradually over the past few years, and through that process have surmounted a number of key hurdles. We have transitioned some of our largest customers from on-premise deployments to hosted, as well as doing initial deployments hosted. With our largest customers, we've passed all the tests, from vendor audits, supplier audits, quality requirements. We've passed the bar with our most difficult customers, and customers are increasingly moving towards cloud-based solutions, especially in the Western world. It satisfies their objectives. It also, as we were looking through the business, a number of our deals were moving towards hosted solutions over the last couple years at the customer's direction.
We were kind of reaching a tipping point where it made sense to move all the way over. From a customer-facing point of view, from a support point of view, we can support better, we can deploy faster, we can get the customer up and running in a shorter amount of time from when we get the order to when we can deploy. From an investor point of view, obviously, our profile over the last few years has been lumpy, just given the on-prem accounting recognition rules. We think this will provide a better picture for investors to measure our business and have a more smoother, predictable profile.
Finally, from a you know renewal perspective and an ongoing support perspective, we are just better positioned to understand the value customers are getting from our services, from our software, and ensure that they're deploying it properly across our organizations, that every site, every location is using it uniformly, such that when we approach a renewal, we are better positioned to understand their needs and our needs and capitalize on that opportunity.
Great. That sums it up perfectly. Okay. Moving on to the end markets here. How is the funding environment? What are you seeing budget allocations looking like? We'll talk about that broadly, and then we'll go dive into maybe across the different tiers, the customer tiers that you guys organize around.
Yeah. I think, like, has been widely reported, we're obviously, like these other reports, optimistic about what this year looks like, especially compared to last year. You can see that we have guided to 10%-15% ACV growth relative to lower growth last year, which was the result of, right, the budget pressures both in pharma, which is sort of scary, right? Again, I think that's getting better and obviously in biotech. We're confident with our ability to achieve that sort of growth following a very difficult year obviously for the whole industry.
And then maybe-
Just to add to that, Scott.
Yeah.
Our growth outlook for this year, you know, reflects not just one budget category.
Right
We released a number of new products this year.
Yeah, that's a great point.
Are releasing a number of new products this year that will-
Yeah
touch additional budget. That's a part of our growth story for this year.
Yeah, let's dive into that maybe more. I think a big part of your next growth algorithm is unlocking more budgets that you haven't had exposure to in your life science end markets and your customers. Predictive Tox is one of them.
That's right.
Let's talk about that strategy and where you think you can take that, over the next several years.
Yeah. There are a number of things that are pretty exciting about that. The obvious thing, of course, is tapping into new budgets. It clearly increases the TAM for the business. Here's one of the things that we're most excited about with regard to Predictive Tox. Because of the nature of current computational methods and, so the current methods require huge amounts of training 'cause they're machine learning based solely. What does that mean? That means that they get done later in projects, way later. What does that mean? That means that essentially so you're doing a discovery project, working on a molecule, you're working on it for a few years.
You've spent tens of millions, $20 million, $30 million, $40 million, three, four, five years, and then you'd start testing it first, maybe using these machine learning models, which now start to work kinda because you've generated a huge amount of data, which is of course what's required when you're using machine learning-based methods. If it lights up and is starting to show toxic, that's it. Project's done. It's very difficult. What are you gonna do about it? I mean, you have to go back to the drawing board. You have to start redesigning the molecule even though you've kinda started to hone in on all the properties. Here's what happens. You start trying to improve the toxicity profile, and of course, you start messing up everything else. It starts not being potent. It starts not being soluble. It's not permeable. Whatever, right?
It's a multi-parameter optimization problem. The nature of what we've built is, first of all, it's highly accurate, but remember I said at the beginning, it can be used on completely novel molecules because it's physics-based. It's not machine learning based solely. That means is that you can use it early in projects, way earlier. Of course, that has huge impacts on its TAM because so not only are we tapping into new budgets, but we're creating a new sector in some sense, a new budget that is those same people that are running the toxicity, but moving it way up in the process, which requires, of course, way more usage. Here's the other thing.
With regard to these other solutions, they just tell you, yes, no, you're toxic or not. They don't tell you why. You can't do anything about it. The methods we've developed not only work on novel molecules. You don't need to train 'cause they're physics-based, but you get a picture literally of the molecule bound, you know, the structure of the molecule bound to the target that's causing the toxicity, and that means you can start to dial it down and again use it early on in the process as part of the multi-parameter optimization, you know, workflow.
Two follow-ups there. One, are these new products that you're developing in response to customers' needs that are coming directly from them? Secondly, can you build these all yourselves, or is there a way that you need to have some bolt-on acquisitions to advance this growth initiative of developing more solutions to expand the budget?
Yeah, it's a great question. It ties back to what I said earlier. Every company almost in every field struggles to innovate through asking customers what they want. You know, there's the famous quote from Ford, right? If he said if he had asked people what they wanted back before cars existed, they would have just said faster horses. It's a company's. You know, you've heard quotes like that from so many innovative companies. In order to really understand what it is that's going to change things fundamentally, right, and really innovate, you have to have a deep understanding of the problem. In our field, where are you gonna get that? By doing it ourselves, and that's why we have a therapeutics group and why we built.
The impetus for developing this technology came from our own projects and our own collaborations. We kept finding that we were running into this toxicity problem, of course, this off-target problem, right? Late in projects, right? You're hitting hERG and you discover that kind of late. Now hERG is an example where people are testing that a little bit earlier, but that's an example, and there's so many targets like that. What we did is we went and we had meetings with senior people at these companies and interacted with them and said, "Hey, what do you think about this idea?" Of course got really great feedback. The original idea has to come from within the company that's actually innovating to really make groundbreaking sort of scientific advances.
On the second part of the question.
To address the second part.
Yeah.
Yeah, Scott, I think we have positioned ourselves from a balance sheet point of view to have capital. You know, we have a capital position to fund the business for the next few years to get to the point of profitability. As it relates to M&A, within Predictive Tox, I think what we're developing is truly unique and has the potential to transform the workflow. But from just taking one step back and looking at where we sit in the entire workflow, if there are capabilities that are complementary to us, that are, you know, near where we sit in the workflow, we will of course consider M&A. I don't think we're gonna do clinical trial optimization. That's very far downstream from where we are.
If we can find additional capabilities close to us that are complementary with the platform and are complementary with our customers, we'll take a look at that.
Great. Maybe let's talk about AI? On your recent earnings call, you talked about embedding agentic AI in your platform. Maybe just talk about the benefits of this and how many applications we can see, with agentic AI, as we head throughout this year and beyond?
Of course. Yes, it's very much on top of a lot of people's minds. Let's first make sure we understand really what agentic AI means, and then we'll tell you why it is something that we think is very important and what we're doing there. At the moment, you can imagine this being the case, especially when a technology is just sort of, you know, coming online that, and again, a lot of companies and a lot of different spaces that are innovators run into this issue, which is there aren't a lot of experts that know how to use it and truly use it, right? Use it correctly at scale.
Of course, the scientists at Schrödinger can, and we put a huge effort into making the software easier to use, building workflows, writing, you know, lots of online courses and so on to train the next generation of computational chemists. Another solution to that problem is agentifying technology, automating it, making it so that not replacing humans, and I think a lot of people understand this. You're not gonna replace humans, but you're going to make them more efficient and augment their capabilities, so they're not doing sort of the menial sort of things and allowing them to scale, right? Make one human significantly more efficient, being able to support more programs and take advantage of this, you know, extraordinary technology. That's the goal, and that's what we're working on.
Now, we should be clear, this is not easy. The agentification of many technologies is taking longer than people thought, because it turns out humans are pretty good at driving cars, for example, right? Look how much longer that's taking. That, believe me, is way easier than designing a drug. Way easier, way less of a complex problem. It's still nevertheless, all of these are complex problems. If we can start to encode the knowledge that humans have, the most expert, you know, humans into the technology and make humans more efficient, obviously that will have a really huge impact on the whole field and of course on our business as well, because of course it increases the demand for the technology in a really serious way.
We're very pleased to be working with a number of the sort of large, you know, companies that are building these LLMs. Anthropic is one of them. As we mentioned in our earnings call, great discussions with them. We think this is something that requires a partnership? like that, where we supply the expertise, but of course the sort of foundation that these companies have built is quite difficult to replicate, obviously.
Just to add two-
Fully internally, right. Yeah.
To add two points, Scott. We've set up the business to capture this additional demand on the software. What I mean by that is we have throughput-based licensing for the majority of our products. As there are additional workflows being called by our user base or an expanded user base, we capture that all by selling additional licenses and tokens as opposed to seats. The other piece around agentification is that you know, we have had historically a relatively small user base that has the experience and the capabilities to run our tools at scale. With agentification, we expect that we can over time increase that user base by converting over chemists trained on traditional methods over to computationally based methods.
I think those are two really important points. One, you're not seat-based, you're utilization-based, throughput-based, and two, that these agentic will be able to. You know, part of that, I think the barriers to entry was not having people be able to use-
Exactly
the software in the most effective way and having these agents should certainly improve that. Those are really important points.
Yeah.
I guess let's walk down the financial model a little bit more just to clear up any misunderstanding. 10%-15% ACV will translate into future revenue streams next year. This year is the sort of transition year. We're expecting profitability by 2028. Let me walk through that, right? Currently in the legacy model, we had a lot of multi-year contracts that renewed in the fourth quarter that were not hosted. We're moving towards a more stable quarterly distribution of revenue streams as we head out over the next several years. Then margins should take a stairstep function lift in next year and beyond as we get towards profitability. Maybe this is a question for Rishi.
Maybe walk us through what the implications are for your guidance in case there's any misunderstanding about the financials of this process of getting to a more stable quarterly revenue, you know, stream.
Yeah. Thanks. ACV is. At the end of 2025, ACV and revenue were in lockstep. Software revenue was $200 million, software ACV $198 million. To reemphasize, ACV and revenue will always equal each other over the course of time. If we sign a $1 million contract ACV, the revenue will equal $1 million over the duration of that contract. In this year where we are in earnest starting the transition, and what I mean by that is the majority of our contracts are one year or less. At the renewal over the course of 2026, we expect to convert the majority of these contracts over from on-prem to hosted to the end goal of 75%. Why not 100%?
There are always gonna be some customers where conversion is just not an option. It's based on the geography, based on what the end market is. For the most part, Western world pharma biotech customers, we expect we will be able to convert. Actually, by the time we had our Q1 call, we had already converted over one of our pharma customers from on-prem to hosted. Over the course of the past few weeks, actually, we've tackled one of our multiple year on-prem deals and converted that over to hosted, actually before the renewal date.
We're tackling these throughout the year, but given the majority of the business is booked in Q4, just given the budgetary cycles of pharma companies and biotech companies, in that quarter, when you switch from on-prem to hosted, you're gonna go from on-prem, almost 80%-90% revenue recognition in the quarter of the booking to ratable. Let's just pretend a deal is booked November 15th, you're gonna be picking up one and a half months of revenue recognition this year and creating a large deferred revenue balance that will be recognized in 2027. That is what has driven our focus on ACV for the year, in a year in which we expect revenue to decline because of the phenomena I just walked through.
The key point for investors to really focus on is that everything I just said is cash flow neutral. Our cash flow from operations that you would see at the end of 2026 will not change from all of this accelerated transition to hosted. In that line item, it'll be exactly the same, irrespective of how we've approached the year. If, as you think about that over the course of the three years, we maintain the 10%-15% growth trajectory for the three-year period and expect to get to 75% hosted revenue. The reason we think those are important benchmarks are that revenue will start to converge with ACV. You'll start to see the two track each other.
As we continue to grow the business, we do think that ACV will be a leading indicator of revenue even after the transition period, going out into the longer term future.
Great. I guess my last question, we can open up to the audience if there's any questions. Please feel free to, you know, put this in the chat box here. You know, we talked about new product releases to tap into different areas of budgets for pharma. I guess my question is, you know, is there a set plan of how many new releases we can expect from you guys? You did Predictive Toxicology, and that went through beta for several years. Does that be the process where you announce that you put it into beta to test it with your customers to see? Because obviously, putting it in beta actually helps you to get live feedback and make this the most monetizable product that you can and successful product that you can.
How should we think about that in terms of the framework? Because it seems like it is a growing part of your growth algorithm, and it makes sense because you're tapping into all these new budgets that you had never had access to, before. How should we expect the cadence of new product releases?
Yeah
by now?
Yeah. We did something with Predictive Tox that we haven't done in the past. We've had many new product releases and over the years and new ones that have tapped new budget, but we haven't talked about the beta. We did it this time because, first of all, there was a rather large grant associated with funding this project, which is kind of unusual. That had an impact on our gross margin, so we were sort of talking about that. The FDA kept bringing, you know, talking about the importance of computational methods for tox, you know, for tox prediction and in their attempt to reduce or maybe eventually eliminate animal testing. I don't think that's realistic, but certainly reduce it significantly. We felt compelled to talk about it in advance. We don't normally do that.
It might look like something new was happening, it wasn't. This is the normal cadence. Multiple new product releases, or major enhancements to existing products every year. We have four releases every year. We have a large R&D effort in this area, so there are new products coming out all the time. We won't always announce publicly the beta release, but we will certainly announce, you know, the. We may not even publicly announce, actually. Oh, man, I guess we, of course, to customers, obviously they're hearing about the new products. But this is a pretty normal cadence actually, and it will continue into the future. There's a lot more to do.
We're making fantastic progress, but as long as there's any failure in a drug discovery project, or any project takes more than, I mean, even a few months, you know, you should be able to get to a development candidate much more rapidly than we're doing now. There's still lots of new science to be done, lots more breakthroughs, and we'll continue to lead the field in those in advancing the science.
I guess maybe to end it here, we're coming up on the 35-minute mark here, for both Ramy and Rishi you know, we've heard a lot about advancements in AI and-
Mm-hmm.
pharma, this is big application for pharma. What are your conversations like over the last three to six months with large pharma on this topic? Are there more inbounds than you've had ever before, trying to understand your product offering, do you think this is a partnership? A lot of people will argue this is either a cannibalization or a partnership or a catalyst for partnership. Maybe talk about what you're hearing from customers directly.
Yeah. We see this as a tremendous tailwind, the sort of demand for AI. That word is being used to really mean computation. Yes, these, the excitement around AI has over quite a number of years now dramatically increased the interest from traditionalists. You know, medicinal chemists are generally the ones, you know, running research groups saying, "Something's going on here. We need to be using computers." AI is just used as a way of a shorthand, quick way of referring to computation. They understand what everybody, I think, needs to understand. AI is powered by training sets. It has no utility without the training set. That's what AI is, right? You have to train.
I think they understand very well because they've been testing this for many, many years, that experimental data alone is not sufficient to train these AI models. You have to generate simulated data, just like in self-driving cars, just like in chip design, weather prediction, every field. Those are simpler fields than what we're in. You need to generate simulated data. They understand that they need our platform to generate the massive amounts of data that are required to actually power AI. So yeah, everything is, I think, heading in the right direction and you know, we're pretty excited about the future, and it's great that there's so much attention being paid to computation finally, right?
Yeah. Well, that's great. I think this is the perfect place to end it. Well, thank you so much, Ramy and Rishi, for doing this fireside chat with me.
Thank you. It was really, really great.
If the audience has any follow-ups or ever wants to be in touch, please reach out to us. Thank you very much.
Appreciate it. Thanks, Scott.