Good morning or good evening everyone, and welcome to Insilico 2025 full year financial results conference call. I'm Leah Liu, the Head of Capital Markets at Insilico Medicine.

Presenting to you today are Dr. Alex Zhavoronkov, our Founder and CBO; Dr.

Ren Feng, our CEO and CSO; and Dr. Alex Aliper, the President.

Before we get started, please be advised that today's conference call is being recorded, and I'd like to remind you that the conference call contains forward-looking statements related to the likely future developments in the business of the company and its subsidiaries, such as expected future events, business prospects, or financial performance. The words expected, anticipate, continue, estimate, objective, ongoing, may, will, project, should, believe, plans, intend, vision, schedule, and similar expressions are intended to identify such forward-looking statements.

These statements are based on assumptions, analysis made by the company at the time of this conference call in light of its experience and its perception of historical trends, current conditions, expected future developments. However, actual results, performance, and financial conditions may differ materially from the company's expectations.

The inclusion of such forward-looking statements should not be regarded as representations by the board or company that the plans and objectives will be achieved, and investors should not place undue reliance on such statements. Now I will turn over the call to Alex to provide you with more details on our business updates and the 2025 full year results.

Alex, please.

Thank you, Leah. Hi everybody, welcome to our first earnings call after being listed.

Next slide. Today, I wanted to ensure that I explain a little bit the company's strategy, because in the AI drug discovery world, I haven't seen anyone properly present the objectives for an AI drug discovery company, and so far there are no clear performance metrics on how to evaluate an AI-powered drug discovery company, AI drug discovery algorithm, or any platform or system there is.

I would like to explain our philosophy, how we approach drug discovery and also AI, and how we are disrupting the industry, also why we believe that we can remain very productive and continue delivering on our promise of AI-powered drug discovery. On this slide, you can see the different stages of drug discovery and development.

It's coming from a very classical paper by Steve Paul in Nature Reviews Drug Discovery in 2010, How to improve R&D productivity. We only added the target discovery and target disease hypothesis section here.

On the top, you see probabilities of success, number of years, and millions of dollars. Since then, if you actually account for failures, the costs have increased, and some of the timelines have not shrunk.

We will use the 2010 estimates, even though nowadays, due to Eroom's Law, the kind of opposite of Moore's Law, we see some of those numbers increasing. Where I believe AI gives you the most advantage is 0 to developmental candidate.

Nowadays, there is a lot of talk about when are we going to see approvals from AI-powered drug discovery, and I'm pretty sure there will be soon. As a matter of fact, some of the drugs that have been approved in the past used machine learning and kind of primitive forms of AI already, it's kind of a sliding window always.

Where we need to measure productivity is 0 to PCC, one step before clinical trial, before the regulated area. Next slide.

Here, I'd like to kind of represent it in the form of a diagram where we have speed racing. Imagine that Tesla is competing with a Ferrari and Lamborghini, where is it going to compete?

If we are talking about the highway, you can actually talk about the acceleration from 0 to 100, for example. That's where you can significantly accelerate, you can improve, you can add additional tools to your car.

Afterward, after you reach the highway, you have to move with the speed of traffic because there are traffic signs and also traffic cops, and you don't want to break the limits. As a matter of fact, if you break, you can completely derail yourself and the company.

AI can help you only very little after you reach IND, and actually very little after you reach PCC because IND-enabling studies is the same 28-day GLP toxicity studies in two species that we have in our preclinical candidate package, it's just we do it in non-GLP manner. After that, it's actually better to hand over to minivans and trucks that have more resources and actually have the agility to transport more on the highway.

That's our philosophy. We compete in this area between 0 and PCC.

Next slide. I tried to make an attempt to actually create a formula for how to evaluate AI R&D productivity.

You need to look at the number of preclinical candidates. Here, you see the formula for measuring AI productivity, where we look at the number of developmental candidates that the company can develop, for example, every year in a timeframe divided by the cost of those developmental candidates and time, and preferably you need to include novelty on both target and molecule side, probability of success, basically looking at how you transition from one stage to another, or if you out-license, the value of the out-licensing deals.

I hope that this formula is going to evolve, and please feel free to extend it or use it for other companies. In the early stages, investors and pharma partners alike may ask, okay, what's your compute power, what's your raw data volume, teams and talents, algorithms, patents.

Once your algorithm starts churning out drugs, all that matters is going from 0 to PCC as fast as possible, as cheaply as possible, with the highest probability of success as possible, and as you iterate, you increase the target novelty and small molecule novelty, and then you try to scale. AI is all about scaling.

AI drug discovery is also about scaling and learning. Our Insilico Medicine's stats are now 12 to 18 months typical for 0 to PCC with reasonably high novelty.

Some of the molecules that we've designed are targeting targets that have never been in the clinic before, absolutely novel, like TNIK, QPCTL, and PHD1/2 for the indication with a gut-restricted molecule. That's a truly amazing feat of molecular engineering and biology.

Far over the past 5 years since we started drug discovery ourselves, thanks to Dr. Ren, he is the true hero behind the Insilico revolution, to speak, we've managed to develop 28 developmental candidates, launched 12 of them into clinical stage, partnered, made 10 BD deals, and one Phase 2a completed.

Next slide. Every time you launch a PCC, you iterate and you learn.

At Insilico, we try to learn from one company, which is SpaceX. When you ask SpaceX what's your secret sauce, it is the ability to consistently launch to orbit with high probability of success at low cost.

That's what we are trying to do with PCCs. That's why it's very important to measure the number of PCCs a year and constantly iterate.

Every time you iterate, your AI improves. Our challenge is that AI changes every 6 months, in the case that we don't maintain technology leadership, we are going to lose.

We realized that the most important data in AI-powered drug discovery is actually the data coming from 0 to PCC. Every time you launch a PCC, you generate around 1,000 benchmarks, and those benchmarks could be used to improve your AI and prospectively test your AI.

That's why we are consistently improving on the AI platform and consistently launching new software tools. Our software business is actually not about revenue.

Of course we generate healthy revenue from that business, but it's more about validating many, many, many different AI models at scale. Instead of the need to synthesize and test ourselves, we put the model out for the entire community to use, they use it, we get more confidence, we push for a PCC, learn, iterate, make it better for the entire community, and every community player contributes to the success of the platform as well.

That's why it's important to scale. Next slide.

Here is another very important finding for all of you on the call is that nowadays what I found is that we are technically competing, if you were to name a single competitor in the West that has very clear 0 to PCC benchmarks across many, many PCCs, I wouldn't be able to name any. Many of the traditional Chinese pharmaceutical companies managed to reinvent themselves as innovative drug hunters, and some of them have hundreds, hundreds of innovative drug programs with the ability to scale.

Nowadays, we are essentially competing in China, we call it the China gym, and we compete on speed, cost, the molecular novelty, but also on the novelty of the target. AI gives us the ability to actually bet on more novel stuff where you've got higher risk, but we believe that we have higher confidence in both the molecule and the target, and our kind of differentiation is actually on the novelty side.

We try to innovate on biology and chemistry at the same time. Next slide.

Our company strategy, our real secret sauce, is that we specialize in highly novel biology and chemistry, improve our AI, and now we actually decided that we cannot compete on scaling in foundation models with the top players in this field, like the OpenAIs, the Googles, and the Anthropics of this world, and of course in the Eastern world, that would be the Tencents and Alibabas. We decided to actually become a teacher in the foundation model front.

Instead of trying to battle in this gladiatorial multi-billion dollar industry where you burn cash so quickly and the models become obsolete, we decided to develop smaller AI models that can act as teachers to teach large foundation models AI for drug discovery. Actually now we are shifting our customer base to frontier AI model developers that really want to give you the comfort to access frontier AI-powered drug discovery capabilities from the comfort of your prompt window.

That actually gives me the ability to deploy those tools at scale within my organization because the adoption gap between frontier AI and the bench work is actually pretty substantial. It takes people time to learn new AI skills if you deploy them as specialized software, but if you deploy them in a prompt window, they actually use it very well, very easily.

We now are constantly improving and trying to build models that understand fundamental human and animal biology in time. We started working on life models together with foundation model developers, and you've seen some of the papers probably that just came out.

We've been doing it for the entire 2025. Now we are trying to identify the next kind of GLP-1.

Our strategy on the drug side also has evolved, and we are looking for new pipeline-in-a-product targets. Next slide.

The pipeline-in-a-product targets is something that is supported by aging biology. This is kind of one of the unspoken secrets about Insilico.

We do a lot of work in longitudinal data analysis in humans and animals, trying to identify common biological patterns that unite all of us. We really love the story of GLP-1s where the drug was developed for an indication, expanded into broader disease biology, and then became a lifestyle drug.

I don't deny it, I am on tirzepatide, I am on Mounjaro. I think it's one of the best drugs ever invented by man.

By the way, don't buy, sell, or take any drugs based on what I say. This drug makes me extremely happy because I don't want to eat and still feel happy.

The idea for us is to find targets that work in similar fashion, that affect everybody, and could in theory be repurposed from a disease to a basic fundamental biology, preferably biology of aging, that will help all of us live longer, more productive lives, and of course tackle more indications, making them much more profitable. Next slide.

If you look at our current pipeline and look at where we repurpose some of those targets, in TNIK, we've identified this target and were the first ones to bring it to clinic and decided to purpose it to fibrosis. If you read the seminal papers that we've published around this target in Nature Biotechnology and other journals, you will see that we identified it using a combination of AI and aging research with a fibrotic angle, identified the indication where we could potentially outperform the standard of care, but the potential of the target is vast in multiple disease areas.

As we progress through the clinical trial, we uncover additional areas of fundamental human biology that we can pursue with this target. We also have the second and third generation of the molecule for this target as well, we have a pipeline of drugs that are progressing in time, helping unlock the full potential of this target.

In other areas like QPCTL, same story. We are going after IO, but in reality, this target with the molecule at low dose has the potential to work in cardiovascular diseases.

NLRP3, we've already partnered our CNS-penetrant molecule with a subsidiary of Fosun Pharma in 2025. Beautiful drug, beautiful high-potential pipeline-in-a-product target.

Same with TEAD. The idea is to create more of that, and currently in our early stages, we have many more.

Next slide. I'll walk briefly over all of the business highlights, because mid-year last year, I became the Chief Business Officer of the company.

As we've done reasonably well on BD recently, and you should expect greatness in 2026. On the software side, our revenue has increased, but again, as I've mentioned, we do not care about revenue on the software side as much as we care about the usage of the platform.

I would have loved to open source most of what we have just to get more community usage of many of our tools, and we actually open source quite a bit. What we've noticed is that if you open source, people use it less, because the specialist users really need to understand the value of the product, evaluate it, go through the budget reviews, and then use it at the community level.

We do a lot of work also properly pricing the products, not trying to make it too expensive, and some of the models we open source. Some of the models, like NACH01, it's our Natural and Chemical Languages foundation model, and that's our true venture into multi-modality.

One model can perform many tasks, and that was kind of the premise of our MMAI Gym, where we realized that foundation models can now also perform some of those specialist tasks in drug discovery. We also put it on hyperscaler platforms, AWS and Microsoft, and scale through there.

Next slide. We've also managed to significantly progress our pipeline, and six PCCs were nominated.

The year was very productive on the PCC front, giving us the ammo to go and partner with the pharmaceutical companies. Here, some of those targets are extremely popular, we actually had to decrease the novelty of the targets from time to time in order to satisfy the appetite of big pharmaceutical companies that actually very often shy away from target novelty.

They like to see clinical proof of concept most of the time. There, we focus on the feats of molecular engineering, where we try to achieve absolutely unique molecular properties that are not possible with traditional approaches.

You see that some of those PCCs are being partnered very successfully. We also have 10 pipelines on the clinical development front.

We have multiple wholly-owned pipelines, and Dr. Ren is going to cover it at length, that's why I'm going to be very brief.

Rentosertib in 2025 progressed into Phase 2a complete, with the results published in Nature Medicine. We're planning to go forward with this drug.

Cannot comment too much about this yet, but there is a plan. PHD1/2 gut-restricted, we've initiated Phase 2a trial in China and dosed our first patient.

MAT2A, highly promising target, you've seen many business development activities around this target. Our job is to demonstrate that it's safe, and that will most likely lead to partnership deal.

We've completed already the first-in-person dosing in a global multi-center Phase 1 trial. This is a hot target.

Every pharmaceutical company that has PRMT5 is considering MAT2A. It's a very important companion for PRMT5, also for TOP2, and even for KRAS.

TEAD, that's an extremely important target for us for BD. Also, the idea is to demonstrate safety, but also hopefully some efficacy.

We've completed the first-in-patient dosing in a global multi-center Phase 1 trial. We've also partnered multiple assets, and while we do not comment on partner pipeline progress, it's been amazing, and we started getting milestones from out-licensed and partnered assets.

Of course, we have extended to new modalities, we're going after alpha-helical peptides, ADCs, nanobodies, antibodies, siRNA, PROTAC. We have developed the software called Generative Biologics that a lot of people use and love, and we are of course utilizing those same capabilities to currently evaluate those methods, I wouldn't be surprised if you see some biologics partnerships from our side.

Next slide. On the business development front, we've managed to sign over $1.3 billion in total deal value in 2025, an increase of 39%.

We've entered partnerships with Eli Lilly, that's a $100 million deal last year. That was actually a kind of stepping stone after our partnership in software, I think Lilly is one of the top AI players in the world because they took most of our AI very early, now many are catching up.

We've partnered with Servier and announced the $888 million deal, Tenacia, and achieved multiple milestones with Theragent, Huasun, and others, and also achieved milestones with Menarini and TaiGen. We've entered multiple research collaborations, strategic collaborations, and now we've even announced our first partnership with a foundation model developer, with a very famous genius, Ramin, who is the CEO of Liquid AI.

You probably remember some of the seminal papers in foundation modeling out of MIT in Boston demonstrating that you can pack a lot of intelligence in a very small foundation model that is actually non-transformer-based. These guys have progressed, and now we realized that those smaller models can perform at state-of-the-art performance after being reinforced in our MMAI Gym.

We really hope that foundation model developers are going to notice that, read the papers. We just got a paper accepted for ICLR demonstrating the capabilities of MMAI Gym, and we believe that this could be a very significant market going forward.

Next slide. We've just partnered with Eli Lilly, you probably have seen the announcement over the weekend.

This deal carries $115 million upfront and about $2.75 billion in total deal value. It doesn't touch any of our clinical assets, this is early preclinical asset deal and also collaboration.

This is a testament to the high quality and novelty of the small molecules that are being generated by our Pharma.AI platform, and also gives you much more confidence in Insilico as a stable partner for research collaborations. We're also deeply proud to be a member of the Lilly Gateway Labs, which is one of the really top ecosystems in the world, I would say the top, that we've seen so far, that allows you to move R&D even faster.

Next slide. Now I'm going to pass the word to Alex Aliper, who is going to briefly go over AI platform.

I've taken so much time, I'm sorry, but I wanted to give you the gist of our strategy as well. Alex, please take the floor.

Thank you Alex, and hi everybody. At Insilico, we have been and remain committed to pushing the boundaries of our AI platform and advancing it forward.

Our recent addition to AI platform is called Science MMAI Gym. As Alex explained, it's a framework for post-training and boosting the intelligence of frontier foundation models, frontier LLMs, in science.

Currently, it covers six scientific domains: chemistry, biology, clinical, longevity, material science, and agriculture. We plan to gradually expand its utility as we go forward.

It integrates millions of proprietary data samples and over a thousand different benchmarks that helps us evaluate the performance of every model in specific task. The Gym is also sits on top of strong foundation of reasoning datasets.

We have over 300 reasoning datasets used to fine-tune and post-train frontier models and also many curated databases. The way the framework work is that we provide a frontier model as an input and you put it through the Gym.

First, we evaluate the baseline performance, we see which tasks the model performs well and which tasks the model lacks behind, and then we put it through post-training protocol in the MMAI Gym that combines proprietary reasoning datasets and experimentally validated reward models to boost its intelligence and do SFT-RFT post-training to reach up to 10x increase in performance compared to the baseline. Next slide.

We have shown that this model and Gym specifically works on diverse set of tasks and benchmarks. One particular application is in target discovery.

We showed that after post-training of one of the open source models, in the Gym, we're able to dramatically increase its utility in uncovering therapeutic targets. Another application that we've just published and Alex referred to is in collaboration with Liquid AI, where we showed that if we put their model, LFM2 2.6 billion parameter model, through MMAI Gym, not only we outperform the frontier LLMs in the performance, but we're also achieving state-of-the-art performance among other specialist models.

You can see the power of the Gym where a single architecture can tackle many different modalities of data and domain-specific reasoning and it's capable pretty much of handling many different tasks across biology and chemistry within the drug discovery space very efficiently. Next slide.

In terms of the business model for MMAI Gym, we tackle two two main tracks. First track is a membership or licensing of MMAI Gym.

One one way to license the Gym is provide us with your LLM model and we will do customized post-training of the foundation model and will provide resulting model to the client. The second track is in providing teacher models or training data for post-training those frontier models.

The second vertical of the business model is around commercialization of resulting model that are coming out of MMAI Gym. Here we target two main tracks: one is annual licensing, where we provide a license to resulting model to end user, and the second track is pay-per-token licensing, where we put the models, we deploy them on the cloud and charge by token for every user's consumption of those models.

It's a cloud consumption model for MMAI Gym models. Next slide.

Another frontier what we're targeting from angle of AI platform is increasing efficiency of experimental validation, and here last year, we were proud to launch the next iteration of our automated robotics facilities, the lab called LifeStar 2. It's a fully automated AI-driven laboratory that accelerates drug discovery and development.

It reaches different areas of drug discovery, including target discovery and validation, and also small molecule profiling, within the same facility. It is supported by AI platform in the name of different modules, such as PandaOmics and Chemistry42, which sit on top of it, and basically we utilize LifeStar 2 to seamlessly validate the AI platform hypothesis and also, in turn, make the AI platform smarter with every iteration.

Next slide. In terms of the module capabilities, we have many different stations and utilities of the module.

We have organoid and high-throughput safety profiling platform, we have gene editing platform, HTS platform for high-throughput screening and validation, high-content imaging platform, and NGS multiomics platform for deep sequencing and gene expression data generation at scale. Next slide.

In terms of utility for therapeutic areas, we're pretty much covering all our core target therapeutic areas, including oncology, metabolic disease, I&I, autoimmune and inflammation, skin diseases, and longevity-specific assays, where we already demonstrated and published many of the case studies showcasing how we can integrate the AI platform reasoning and idea generation with seamless laboratory automation. Next slide.

With that, let me turn over to Dr. Ren.

Thank thank you, Alex. Let's go to the next slide.

I'm going to brief you with with our strategy on the pipelines and also our current progress. For the strategy of pipelines, we have two strategies.

One is a so so-called first-in-class, we use AI to discover novel targets and novel molecules getting to the clinic. The second strategy is so-called best-in-class.

For for those programs, we are using, we are pursuing the validated biology, but we deliver develop, using our AI to develop well-differentiated molecules to the clinic. On the ratios, these two strategies, about 20% our internal programs are first-in-class, and more than 80% are best-in-class.

As Alex mentioned, we have 28 PCCs already till now, and that includes our own internal pipelines and also the collaboration pipelines. This is a highlights of of what we have in the most advanced pipelines.

I want to give you updates on some key highlights. If we go to the next slide, next slide, yeah, the the first highlight is our TNIK program.

This program is a first-in-class, we use AI to identify the novel target, TNIK, and also use AI to develop to discover the novel molecule, 055, also called rentosertib. The target, we have combined the literature and also our internal biology, we understand this target is regulating Wnt, TGF-beta, and I-kappa-B, YAP/TAZ pathway, which are all related to fibrosis.

If we go to the next slide, we have completed the Phase 2a trial in China. This is a comparison of our data with the available anti-IPF data.

For all the commercial approved IPF drugs, nintedanib, pirfenidone, and PDE4 inhibitors, their molecules in the Phase 3 trial, they can only slow down the decrease of the patient's FVC, forced vital capacity, which is a golden standard for the clinical trial for IPF. You can see on the left-hand side, our molecule is now it can revert, it can really improve the patient's FVC after 3 months of treatment.

This is really remarkable, and if we can reproduce this result in the Phase 3 trial, we can really bring the breakthrough therapy for IPF patients. In the next slide, based on these really impressive results, the CDE allow us to go directly to Phase 3 without doing a Phase 2b.

You can see this is our Phase 3 trial, we are going to enroll 320 patients with two groups: one is a placebo, the other one is a 60 mg QD dosing, and we anticipate to have the first patient in in the second, third quarter this year, and the data will be is expected in 2029. That's one highlight of our leading asset.

If we go to the next slide, that's another highlight of our IBD program. The IBD is a also use AI to identify prolyl hydroxylase domain-2 as a novel target for IBD, and we use our Chemistry42 to develop a novel molecule called [dertadastat?]

. It can not only it's anti-inflammation, but also it's drives the intestinal barrier protection.

It's a dual-mechanism molecule, which is very rare in current, comparing to all the available drugs for IBD and also the current clinical trials. If you go to the next slide, we have finished two Phase 1 trials, one in Australia and the other one in China.

The molecule shows very good tolerability up to 1,000 mg. This is our Phase 2a trial in China.

We have four cohorts: placebo, 200 mg, 400 mg, and 800 mg QD. We have achieved the first-patient-in in December last year, and we are now very actively recruiting patients, and the topline data is expected by next year.

By next year, we'll have the safety and also some preliminary efficacy results. Those are the two Phase 2 trials.

If you go to the next slide, we also have two oncology programs. I want to highlight one this time, it's we call the best-in-class pan-TEAD inhibitor targeting the Hippo pathway.

We are now the second of the global for the pan-TEAD inhibitor in the clinic. The first one is Vivace, but their molecule have very long half-life in human, 12 to 15 days, and because of that, their molecule have the accumulation in the clinical trial, so that they observed very severe side effects, including proteinuria and others.

For us, our molecule is a, have the half-life of 21 hours in human, so that we don't have observe the dose accumulation problem. For the Vivace's molecule, they have to take holiday dosing.

For our molecule, we can do it once daily. If you go to the next slide, currently we are in the dose-escalation stage, but even though it's a dose escalation, we did observe confirmed PRs, we observed at least two confirmed PRs already in the dose escalation with the patients, mesothelioma patients with already 4 to 5 prior lines of therapy.

Also, not only that, we also observed very favorable safety data. For our molecule, we didn't observe, the all the side effects are limited to Grade 1, and also the proteinuria problem, which was observed very frequently in the Vivace's trial, but we it's very rare in our trial.

This is not only for mesothelioma. The TEAD can provide a basic for as a backbone to combine with EGFR inhibitors, ADCs, KRAS inhibitors, et cetera.

We are expecting to have some data available in second half of this year, and we're going to start the part two dose expansion in end of this year. If you go to the next slide, in addition to that, we also have a lot of early programs.

One is a pan-KRAS, it's a we have the pan-KRAS has a very selective comparing to the pan-RAS. Our selectivity of the KRAS versus HRAS and NRAS is greater than 100-fold, for that reason we should observe a better safety profile.

Also on the right hand, you can see that our molecule also showed very good, comparable or even better efficacy comparing to the Revolution molecule, RMC-6236. Next slide, please.

Our oncology strategy is we use the combination, yeah. We have the MAT2A in clinical trial, we also have PRMT5.

If we combine MAT2A and PRMT5, it shows really good synergy, so that's shown in the right-hand side bottom figure. Also our pan-KRAS inhibitor can also combine with CBL-B, CBL-B is currently in the IND-enabling stage.

We for our oncology programs, we are doing a lot of combinations. If you go to the next slide, oncology, I talked about fibrosis, oncology.

Now I want to share with you some of the early programs. One is for the obesity, metabolic diseases, right?

For us, we are targeting the programs that can not only reduce fat mass, but also can preserve muscle, right? We have two programs: one's a GIPR antagonist, the other one is a biased APJ agonist.

You can see on the right-hand side of the animal models that both both molecules can when combined with semaglutide, it can further reduce the body weight by 10 to 15%. At the same time, it can increase the lean mass to fat mass ratio.

That means our molecule, when combined with semaglutide, can really preserve the muscle loss, but can also increase the fat decrease the fat mass. That's our metabolic strategy.

If you go to the next slide, we also have CNS programs. Targeting, focusing on pain, we have the best-in-class Nav1.8 inhibitor.

Our molecule shows better efficacy comparing to the Vertex VX-548, and also our molecule shows great solubility so that we can do the injection or infusion, IV dosing. That's is our key differentiation.

Another one is we use AI identify a novel target, we call it Target Z. Target Z is a targeting different MOA of Nav1.8, and in the preclinical studies, we've shown that our molecule compare it shows very good efficacy in multiple pain models.

It shows better efficacy comparing with the SOCs, pregabalin, right? That we are very excited on that novel target, Target Z.

If you go to the next slide, Alex has mentioned, this is our inflammation and autoimmune projects. We have the best-in-class STAT6, it's shows much better potency comparing even to dupilumab.

Also we have the VRK1 small degraders, protein degraders, and also we have IRAK4. We use our protein degradation platform developed quite a few of the anti-inflammation, autoimmune programs.

If we go to the next slide, so as mentioned the one-drug pipeline, right? Our NLRP3 inhibitor is the one typical one-drug pipeline program.

You can show efficacy in the MPTP model, which is a PD model. It also show shows efficacy in the peritonitis model.

Also, I want to mention that the foot ulcer model, our molecule shows great efficacy in that, which is a huge unmet medical need. Also it shows great activity in the dry AMD model.

This molecule, this program is a we call it one-drug pipeline, which means one molecule can cure a lot of diseases. In the next slide, I also want to share with you another excitement is our AI-identified first-in-class program as a next generation of one-drug pipeline.

This target, we haven't disclosed the target yet, but we have already developed the molecule. You can show that this molecule also shows good efficacy in the Parkinson's disease model, and on the left-hand side bottom figure, you can show that this molecule, when it combined with semaglutide, it further reduced the body weight by 10 more than 10%, and more importantly is once you retract the semaglutide and our molecule, the body weight rebound.

If you retract semaglutide, but continue to take our molecule, the body weight remains, right? That that means our molecule can prevent the body weight rebound if you retract the semaglutide.

This is very exciting. Also on the right-hand side, the molecule shows the best result in the dry AMD model and also the MASH model.

This is a novel target, and we developed the molecule for this target for the one-drug pipeline purpose, this will be the really big future program for for us. I will stop there, and if you go to the next slide, I will stop there and pass the word to to Leah.

Leah, we cannot hear you. Oh,

sorry, can you hear me now?

Of course.

Thank you. Can you go to the next slide, please?

Yes. We run as a company a multi-pronged revenue-generation business model.

We have three main business models: one is our drug discovery and pipeline development, the second is our software solutions, and the third is our non-pharma sector business. The first one is our main focus, the drug discovery and pipeline development.

Out of that, we have generated so far, as Alex and Dr. Ren mentioned, 28 pipeline assets already, and then we've done more than 10 out-licensings and collaboration deals with a total contract value of, as of the annual report, $4.6 billion, but really after the Lilly deal, as we spoke about earlier, we have now $7.4 billion in total contract value.

Then in 2025, we collaborated with more than 75 customers for the drug discovery business. With the software solution part, as Alex and Alex mentioned before, we have our platform and we sell software to our business partners as a door opening for them to have deeper relationships with us.

For that, we also came out with a new product that is the MMAI Gym. Then finally, for the non-discovery-related non-pharma products, we actually collaborate with non-pharma sector leaders in their sector.

For example, we have done a partnership in the past with Syngenta on agriculture and pesticides. We have collaborated with a major global energy company on carbon-capture material and other new materials.

Also, we have developed next-generation nutraceuticals and cosmetics products, all of which are generated by our proprietary generative AI platform. Next slide, please.

As you can see, this again is based on at the cutoff of our annual report. Now the total contract value is $4.7 billion, of which we have recognized $205 million in revenue by the cutoff of the report.

Then, we have more than actually $4.4 billion or rather we have more than $6.4 billion over in the future. Most of our business is actually based on our future receiving of our milestone revenue and our profit-sharing revenue.

We have more than 12 partnerships at this moment now. Next slide, please.

This gives some details about our business and our milestones that we have achieved. These are the partnerships that we've formed in the past, and this, it shows you some of the major partners that we've worked with, but again, we have more than 75 partners in the entire system.

Next slide, please. Our business, as I mentioned, not only is in our drug discovery business, our drug discovery business is the core validation of our AI platform, and it's a way that we have been able to improve and continuously build on our platform with validated data, as Alex mentioned before.

However, our platform can be used for multiple different sectors, and going into the future, we will look for more collaborations in those sectors with major leaders in their fields. Next slide, please.

Next, I will go over the financials with everyone. As Alex mentioned, we were the largest biotech IPO in Hong Kong in 2025, and subsequently we were included in the Stock Connect on March 9th this year.

Next slide, please. For the financials, our revenue declined in 2025 by $29.6 million to $56.2 million.

The decline was due to the recognition of revenue generation and deal negotiation. As you can see, for now, we've already closed more than five deals as of this year in 2026.

The revenue from software solutions increased by 23.8%, while the customer base only increased by a bit over 18%, which means that the per-customer revenue actually increased from the software solutions product. The cost of revenue increased slightly by $2.1 million to $10.4 million, primarily attributable to the revenue composition of an increase in co-development revenue.

Gross profit margin decreased slightly by similar reasons. Selling and marketing expense increased slightly by 0.8% due to the increase in share-based compensation expense.

R&D expense decreased notably by $10.5 million. This is due to the enhanced efficiency for developing internal pipelines.

Administrative expenses remained flat. Other income decreased by $2.6 million to $8 million, primarily due to the decrease in bank interest and income and subsidiary income.

The loss increased due to the change in fair value of financial liabilities, as recorded a loss of $296.7 million. This is due to the conversion of preferred shares issued in previous financing series into ordinary shares based on the share price upon our listing.

This is a common practice for newly listed company and is a financial-basis non-cash item. The loss for the year increased by $335 million to $352 million due to similar reasons of mostly related to the fair value conversion.

The loss for the year on non-IFRS measures increased by $21.2 million due to the decrease in revenue, which was partially offset by the decrease in R&D expenses. Our cash balance remains healthy and very strong at $393 million at the time of the financial reporting.

However, that number has gone up dramatically now again with the closing of several deals this year, including the Lilly deal, as just mentioned. Next, please.

I will turn the mic back to Dr. Ren for closing remarks.

In summary, we will have more key milestones across both platform and therapeutic pipelines this year. In the first half of this year, we will have the preliminary safety, efficacy, biomarker data for the Phase 1 trial of our pan-TEAD inhibitor.

We're having the inhalable rentosertib and the approval for the IPF in China. For the NLRP3, we're going to have the first subject in the Australia trial, and also we're going to have the IND approval for Phase 1 trial in China.

In second half of the year, our Phase 3 of rentosertib for IPF will be started and have the first patient in, our NLRP3 first subject in trial in China, and also we're going to have the MAT2A and TEAD for the close of the dose escalation for the Phase 1 trial globally. Also we are going to have multiple new PCCs and multiple INDs, and in addition to that, we're going to have more BD deals to be announced in both first half and second half of the year in addition to the Lilly deal.

For the AI platform, we'll continue to upgrade our Pharma.AI, and I want to mention that we're going to continue to execute MMAI Gym collaborations. The Liquid AI is the first one, and we're going to have more of the MMAI Gym collaboration in the future.

That will conclude our presentation, and we'd love to answer any question if you have.

Thank you, Dr. Ren.

Now I will open it up to Q&A. If you'd like to ask a question, please click the Raise Your Hand button at the bottom of your window to queue up for live questions, or click on the Q&A button at the bottom of your Zoom window and type your question into the text box.

Please include your name and organization so we know who the question is from, and when I read your name, please unmute your microphone to ask the question. Thank you.

The first question comes from Jack Lin, Morgan Stanley. Hi Jack, can you please unmute your phone?

Jack?

Hello, hi. Can you hear me?

Yes, we can hear you now. Great.

Hi, yeah, thanks again for taking my question and congratulations again on the collaboration with Eli Lilly. This is Jack Lin from Morgan Stanley.

I just have two quick questions for the company. First one is about kind of the PCC cadence and system-level learning, right?

Was wondering if the company could update us a bit more on kind of the year-to-date progress in the PCC nominations, and how do you might see the cadence of PCC generation evolving as the discovery loop becomes more model-driven and more efficient? Also in particular, how do we think about the kind of a timeline in terms of further target discovery and disclosure timing given that today we've shared a lot of developing pipelines and undisclosed assets?

Just curious in terms of how we could expect the pipelines to be gradually disclosed to the investors and the market. The second question is about kind of foundational models and our kind of overall platform setup, right?

I think you shared a bit on the kind of MMAI Gym, and if I understand correctly, it provides an environment to train and optimize foundational models against clinically relevant benchmark. I was wondering if the company could elaborate more on what role do you ultimately want foundational models to play within kind of the broader Insilico platform and where they might have the most incremental leverage to kind of our existing already very expensive suite of application modules?

These two questions, thank you.

Dr. Ren, maybe you go first on the pipeline and I'll comment on the MMAI Gym.

Yeah.

For the PCCs, since we've got the IPO, we've got more resources to support our early discovery programs, we are expecting much more PCCs comparing to the previous couple years. If you want me to say a number, we are targeting 8 to 10 PCCs every year since 2026, and if you want to understand how many of those PCCs will be progressed into the clinic by ourselves, I want to say that all depends on how many BDs we're going to do, right?

Oftentimes our partners will buy our molecules at the PCC or the IND-enabling stage, so that if we sell more programs, and that means we have less internal programs will push into the clinic. We really want to push our first asset, TNIK inhibitor for IPF, into the Phase 3 trial, and we want to achieve the clinical POC, get this compound approved.

That will really complete our circle of the technology to drug discovery and also to the clinic and to the approval.

Yes, thank you Jack for the question. Love your work, by the way, and for everybody on the call, please do follow the research, and also for everybody on the call, please do read the recent article that was just published in STAT on our deal with Eli Lilly, and it also talks a little bit about our platform.

On the MMAI Gym, I just want to explain the entire framework there. About this time last year or maybe just a little bit earlier, we realized that foundation models could be trained to perform very complex drug discovery tasks, not all, but many.

That was kind of our most revealing discovery last year. We realized that we could turn many of the specialist models that we have into reasoning data, basically trying to explain what is happening to the model in text in reasoning traces and fine-tune foundation models to actually perform the same tasks almost at the same level.

Think of it as a blind samurai. We kind of got some inspiration from Zatoichi, if you know the movie and also the story, where very often you lose a sensory system, like you cannot see, but you can still perform a very important function like samurai functions without seeing by relying on different senses.

We can describe what is happening to the model and that would provide you with the expected result. That was one thing.

Then another revelation was that if you later utilize a specialist model to reinforce the foundation model using reinforcement learning, to basically in adversarial setting via reward and punishment, foundation model achieves close to superintelligence in multiple tasks, and it can perform multiple tasks simultaneously. At the same time, you can also measure some of the reasoning capabilities of the model, and as you reinforce it in more and more AI drug discovery tasks, it actually starts reasoning between the tasks and acquires the ability to do zero-shot, performing the tasks it has never seen before.

That was a major revelation, we decided to actually even pause many of our specialist model development and focus on MMAI Gym. This model, this approach took us maybe 7 months to master, where we had to turn all of our smaller models, and we have over 800 smaller models that perform close to or at state-of-the-art performance or surpass state-of-the-art performance, we think that we are state-of-the-art, in multiple drug discovery tasks.

Then you take the foundation model, for example, like an open source Qwen, we like to use it for demonstrations, and give it to this reinforcement learning gym where smaller models act as teachers and teach whatever they know, whatever skills they acquired, to the foundation model. That's why it's called a gym.

Then we also developed proprietary set of benchmarks that nobody else has, only Insilico. That's actually the kind of purpose of those benchmarks, otherwise if those foundation models see those benchmarks, they will hack the benchmarks and become state-of-the-art in the benchmark without actually learning the skill.

We keep those benchmarks private, you can actually test the resulting foundation model in those benchmarks, and in many of those benchmarks, those foundation models perform at state-of-the-art or above state-of-the-art. For us, it was a revelation.

We actually believe that as time goes by and those foundation models become more capable and scale, and also acquire different types of modalities, become highly multi-modal, we will be able to achieve superintelligence in the pharmaceutical industry from the comfort of your prompt window. I'm not talking only about agentic AI.

Agentic AI was of course very important kind of in the meantime where you turn the foundation model into an agent by prompting it to do certain things and follow certain workflows. I think in the future, you'd be able to do the entire kind of prompt-to-drug, basically prompt the foundation model to design a small molecule for a specific target or even for a biological process, and it will do it for you.

It will take time, but we are going to get there. We published a paper together with Jie He, one of the top people in AI in the world, he currently leads AI drug discovery at Eli Lilly, and I very often use him as a teacher because he understands kind of the future philosophy of AI drug discovery and taught us many directions of where to go in chemistry and biology, and we just published a paper from prompt-to-drug towards pharmaceutical superintelligence.

Please have a look at the paper describing this approach. We believe that you can monetize MMAI Gym, and we already started doing so with the partnership with Liquid AI, and of course we have multiple other partnerships with foundation model developers ongoing.

For any investors on the call or analysts, please do query your foundation model developer if they have partnered with us, or what are their benchmarks in target discovery. Of course, the most important benchmark is going from 0 to developmental candidate.

Far, most of the foundation model developers, despite being around for several years, they have not resulted in a single PCC. Some of the very overhyped AI companies with very very popular AI tools on the market have made many claims and raised a lot of capital, but delivered zero PCCs, or zero and of course zero INDs.

The ultimate test is a PCC, and I think that in the future, you'll be able to actually deliver a PCC right from a foundation model with very little human intervention. That is our goal.

In the meantime, we want to monetize that, and we think that the market of foundation models consuming our tools just from financially is larger than pharma. Pharma software market is actually genuinely small.

You would be surprised. Many of the pharma companies prefer to develop software themselves, have billions of dollars in software in AI pharma, it's a myth.

Those kind of companies that just focus on software, they cannot scale too dramatically. With the MMAI Gym and targeting the foundation model developers, we not only can scale, but also we make AI future-friendly, because at the end of the day, we need to enable more and more people to be able to discover drugs from the comfort of their prompt window, make it very simple, and then we just focus on operational efficiency.

We are the SpaceX of drugs. Our job is to deliver 0 to PCC and hopefully offload at that time to the pharmaceutical industry.

In the meantime, we want to be a very significant player in AI. I think currently no company that I know of has so many capabilities in drug discovery AI as we do, including smaller specialist models, but now also large foundation models that work with specialist models and use specialist models for training.

MMAI Gym will be a very viable product, it will also serve a very important role in the industry, and as we all transition towards superintelligence, the world is never going to be the same, we want to be a very significant player in this process so that AI systems of the future remember us as a very significant player.

Got it. Thanks Alex and Dr.

Ren for the response. We'll definitely check out the superintelligence article.

Congratulations again on the collaboration. Thank you.

Thank you Jack. In the interest of time, as we're already overtime, we'll only take another question.

The next question comes from Rui Han from CICC. Hi Rui.

Hey,

hi Leah, can you hear me?

Yes, can hear you now, thank you.

Thank you. Hi, this is Rui Han from CICC.

Thanks for taking my questions, and congratulations on a solid set of results and continued progress in your pipeline and BD. Actually, I have two questions.

First question is about AI. We've seen two emerging technical framework in AI-driven drug discovery, like end-to-end foundation model platforms versus MoE systems.

My question is about how do you think about your long-term architecture choice between these two approaches, especially in our early-stage discovery? Maybe I'll stop here for the first question, and then I'll have the second one.

Thanks.

Sure. By the way, Leah, if we can allow some more time for other questions, that would be great.

Thank you Rui Han for this wonderful question. I think that it actually doesn't matter what AI you use as long as you develop drugs.

One of my favorite quotes in the world is actually a quote by one of the great leaders of the East, that it doesn't matter whether your cat is black or white as long as it catches mice. As long as you can continuously deliver PCCs and you can measure the R&D productivity in terms of the number of PCCs, cost per PCC, and novelty of the PCC, and also potentially, at the end of the day, we need to convert that into money and quality-adjusted life years to the patients, to the real impact to the world, you just need to measure 0 to PCC.

In the future, in terms of what is going to create more PCCs and higher-quality PCCs, I think that foundation models, currently we need to look at what's available today and what's available kind of in the near future. Currently, foundation models are very capable at reasoning, and we utilize them for program-level reasoning, looking at the program from 0 to approval.

It's kind of like looking at human life from birth to death and reasoning within that, trying to identify the right path and also maybe to live longer and make more impact. We use foundation models for orchestration of the programs.

We also even use them for business development now, expect some really cool announcements from us on the automated business development tools utilizing foundation models that allow you for end-to-end reasoning. However, some of the very specialist applications, like physics, when you're doing molecular docking, folding, and some of the molecular property prediction, molecular generation, those tasks are still performed by specialist models with higher level of performance, and in many cases, even those models are not reliable, you have to perform the experiment.

Currently, we rely on kind of hybrid systems where you have a very capable foundation model or actually a mixture of foundation models for general-purpose reasoning, program-level reasoning, and smaller models for specialist tasks, and also the entire lab, the automated robotics lab that allows you to very rapidly validate some of the hypotheses and allow you to get the data and the answers that some of those models cannot produce at all. It will take time, but in the future, it will all go into foundation models, in my opinion, where you would be able to kind of loop them back on themselves, recreate the entire process, and reason to the point where you go from prompt to drug.

Again, would love to refer you to that paper. Insilico, currently we play in every category.

We managed to develop many smaller models and put them on the market, we cover biology, chemistry, medicine, and even science. We have even tools for environmental sustainability, and those same tools acquire more capabilities in small molecule design that we reuse for drug design.

We also specialize in foundation models for end-to-end reasoning, and we decided again to become a teacher rather than one of the fighters in the foundation model space. Now we can actually evaluate which foundation model is better utilizing over a thousand benchmarks that we have in order, as Alex has described already, and with those benchmarks, we can see which one is performing better, evaluate, and then use it for our own programs.

Preferably, those models must be reinforced by our MMAI Gym.

Thank you Alex, that's really helpful. My second question is a quick question.

How should we think about evaluating the real value of AI in our drug discovery today? I noticed on your slide, page seven, you gave us a formula.

I was wondering, for highly novel targets in particular, how to estimate the potential out-license value as well as the underlying R&D cost in that framework?

You're absolutely correct, and that slide seven I made an attempt to create a formula for how to evaluate pharma AI R&D productivity for the first time, and we all need to think about it because otherwise there's just too much hype and everybody is telling the same story, but at the end, there are no drugs. It's actually very easy to make a PCC if you are going to do the same thing that has already been done before, a small iteration on already existing molecule.

In China, many of the traditional pharmaceutical companies achieve greatness in time, cost, speed, and the quality of those iterations. However, many of them shy away from novelty.

If you are going for an ultra-novel target that has never been in the clinic before, pharmaceutical companies that are now rushing to replenish their pipelines, they are looking for something that has been already very proven to work on the target side and are willing to pay massive amounts of capital in order to get highly differentiated molecules with great clinical potential. However, novel targets are very difficult and you need to push them to Phase 2 or Phase 2 complete in order to be able to license them out.

Again, I don't think that us AI drug discovery companies should be taking drugs in the clinic at all, but if you want to prove the point that your AI is absolutely valuable and demonstrate many, many benchmarks, and also build a bunch of benchmarks, you will need to push a few of those highly novel targets into the clinic. We believe that the potential of those targets, like for example TNIK that we're pushing into Phase 3, as Dr.

Ren has described, in IPF, it's massive already, but I think that this target has life way beyond IPF. It should work in multiple indications because we found it using aging research, and we just need to understand the dose and the molecular properties we need to fully unlock the potential of this target.

That's why we are working already on the second and third generation of the molecules. We want to own this space.

In several other novel targets is the same thing, you just have to grind, understand the biology, that really affects your timelines, that affects your cost, that requires you to do many more experiments, take higher risk, get a lot of heat from the analysts and investors. At the end of the day, we believe we will prevail, and in order to bet on those novel targets that have really high payoff potential also from the science side, we also need to go after low-novelty targets to keep the lights on, and that is why we bet on lower-novelty targets with higher level of chemistry, higher level of novelty on the chemistry side, and create feats of molecular engineering that can sell for tens and hundreds of millions of dollars, as you've seen previously.

That is done to be able to push the highly novel targets forward, but those targets will create the real value for the company, for investors, and also for the patients and humanity. We are here to serve the patients at the end, that's why novelty is extremely important.

Thanks Alex. That's very helpful.

Congrats again on having such a strong year. I'm really looking forward for your new achievements.

Thanks.

Thank you.

Thank you Rui.

Leah, can we take maybe one more?

We really can't, Alex, I'm sorry. We really need to jump on our next Chinese call, which starts at 10:30.

Any last concluding remarks from you, Alex?

Thank you everybody for staying with us for this first earnings call. We'll try to do better next time and better numbers, better performance.

This is our first, and we are here to serve the patients, we're here to serve humanity, and when it comes to aging, every one of us is a patient. Insilico is a little bit unique in this regard because we are here to make money, but we are here to also advance science forward in order for us to all enjoy longer, healthier lives, preferably without losing too much function.

At Insilico, we're really pushing the boundaries in both AI and drug discovery. We are competing with the most advanced pharmaceutical companies around the world, and mostly nowadays in China.

Even though we're a global company, we come to the China gym to win, and so far we are doing extremely well. We also rely on global infrastructure in order to move both AI and drug discovery forward, and please expect great results from us this year.

You've already seen that just with one deal, we kind of doubled the revenue from last year, the revenue play should be there for this year because milestones are coming and definitely I'm not stopping on the business development front. We should have many more great deals.

We haven't licensed out any of our ultra-high-value clinical assets yet, but of course everybody wants them now, especially the ones that are mid-stage clinical trials. We should do well.

Of course, we are reinvesting heavily into AI, and this ability to pivot into becoming a teacher, a trainer, a gym coach for a very large number of foundation model developers, I think it gives us a sense of sustainability that I have never seen before, because in the past, we were racing like a racehorse in a race, now we can actually train a bunch of racehorses to run faster, while actually understanding which horse is the best for our purpose, and then pull the trigger on every specific therapeutic disease target and also on a molecule. You should expect us to scale, and you should expect more PCCs, right, Dr.

Ren, this year? That's the most important metric.

Yes, yes. Definitely.

Thank you. Of course, would love to thank the team, would like to thank the investors.

Some of the investors on the call believe in us as a longevity company that we hope we are, and please stay with us for as long as possible. We really value long-term friendships, commitments, and your feedback.

We are actually very receptive to feedback, please do send us notes. Thank you so much.

Here, we would love to conclude. Please stay with us, and even though now we are kind of in the hands of the macro environment, it's kind of like weather that we cannot control, we want to be the best company in the industry and deliver great success for you.

Thank you.

Thank you.

Thank you.