Recursion Pharmaceuticals Inc (RXRX) 2025 Q4 法說會逐字稿

Good morning, everyone, and thank you so much for joining us.

各位早安，非常感謝大家的參與。

I want to start by briefly framing where recursion is today in its journey and evolution.

我想先簡單介紹一下遞歸在其發展歷程中所處的階段。

Over the past decade, recursion has built something truly special, a differentiated platform, pioneering the integration of large-scale biological data generation, machine learning, and compute to better understand the complexity of biology.

在過去的十年裡，遞歸技術已經建構了一個真正特別的東西——一個差異化的平台，率先將大規模生物數據生成、機器學習和計算相結合，以更好地理解生物學的複雜性。

We have also deliberately strengthened the foundation in chemistry and AI through the acquisitions of Excientia, Valence, and Cyclia, creating a truly powerful foundation.

我們也透過收購 Excientia、Valence 和 Cyclia，有意識地加強了在化學和人工智慧領域的基礎，從而打造了一個真正強大的基礎。

Today, we're at an important inflection point.

今天，我們正處於一個重要的轉捩點。

We're harnessing everything that we've built to date.

我們正在充分利用迄今為止我們所建立的一切。

To do two things. Number 1, translating insights into evidence. Evidence that this platform, the use of AI end to end, can generate medicines that matter.

做兩件事。第一，將洞見轉化為證據。有證據表明，該平台（即端到端使用人工智慧）可以研發出真正有價值的藥物。

And we're doing this both across our wholly owned portfolio and through our partnerships with strong momentum across both fronts. I'm excited to share some of the updates today.

我們正在透過全資擁有的投資組合和合作夥伴關係來實現這一目標，並且在這兩個方面都取得了強勁的勢頭。今天很高興和大家分享一些最新消息。

In parallel, we're also continuing to advance the platform itself. Today, we have what I like to call a trifecta, that's required to make impactful medicines, AI-driven biology, AI enabled chemistry, and AI applied to clinical development.

同時，我們也在不斷推動平臺本身的發展。今天，我們擁有我稱之為「三重奏」的要素，這是研發出具有影響力的藥物所必需的：人工智慧驅動的生物學、人工智慧賦能的化學以及應用於臨床開發的人工智慧。

We continue to invest to ensure we're defining the standard for how AI is applied across the full life cycle of R&D.

我們將繼續投資，以確保我們能夠定義人工智慧在研發全生命週期中的應用標準。

And look, as we look across the sector, we are encouraged by the broader momentum in the field, new models, new players, partnerships being announced, but the industry is clearly entering a new phase, where value is being defined, not only by the models you build and the collaborations that are announced, but by actually translating those. This is the hard work into capabilities, into real application and measurable impact.

縱觀整個產業，我們受到該領域整體發展勢頭的鼓舞，新的模式、新的參與者、不斷宣布的合作關係，但該行業顯然正在進入一個新階段，在這個階段，價值的定義不僅取決於你構建的模式和宣布的合作，還取決於如何將這些模式和合作轉化為實際成果。這是將能力轉化為實際應用和可衡量影響的艱苦工作。

The important question now is not only what you build, but what you can unlock.

現在重要的問題不僅是你創造了什麼，而是你能解鎖了什麼。

And that's the chapter recursion is in. Our focus is on unlocking that value.

這就是遞歸的全部內容。我們的目標就是釋放這種價值。

Using AI end to end consistently to generate better targets, better molecules, and advanced programs faster with repeatability. The ultimate goal is to deliver medicines that matter.

持續運用人工智慧技術，以更快的速度、更高的可重複性產生更好的標靶、更好的分子和更先進的程式。最終目標是提供真正有效的藥物。

So this quarter reflects that focus. We're making progress across all fronts. First, on the clinical side with our first positive proof of concept with FAP.

因此，本季也體現了這一重點。我們在各方面都取得了進展。首先，在臨床方面，我們首次在 FAP 方面取得了積極的概念驗證。

On the partnership side, a 5th milestone with Sanofi, reflecting our growing joint portfolio, tackling highly challenging targets. We're excited to share more about that today.

在合作方面，我們與賽諾菲達成了第五個里程碑，這反映了我們不斷增長的聯合產品組合，以及我們應對極具挑戰性目標的決心。今天我們很高興能與大家分享更多相關資訊。

And the continued evolution of our end to end AI platform. And last, but certainly neverthe least, disciplined execution, which is something we talked about at JPM which has now extended our cache runway into early 2028.

以及我們端到端人工智慧平台的持續發展。最後，但絕對不是最不重要的，是嚴謹的執行力。我們在摩根大通討論過這個問題，現在我們的資金儲備已經延長到 2028 年初。

Look, there's a lot to cover today, so with that, let's jump right in.

今天要講的內容很多，那我們就直接進入正題吧。

Today, we'll be making some forward-looking statements on this call, so please refer to our filings for more information.

今天，我們將在本次電話會議上發表一些前瞻性聲明，詳情請參閱我們的文件。

All right. We always at recursion, start with the end in mind. And in that case, for us, like I said before, it's medicines that matter, that are truly differentiated. But in order to do that, you have to use the right data, models, compute, and more.

好的。我們在遞歸程式設計中總是從最終目標出發。在這種情況下，就像我之前說的，對我們來說，重要的是藥物，是真正差異化的藥物。但要做到這一點，你必須使用正確的數據、模型、計算等等。

So, look, there's a lot of talk about data, but what really matters is data that's high-quality and fit for purpose. And at recursion, our foundation has been building high-quality data at scale, not just one type of data sets, but multimodal across the board. This is where pioneering the lab in a loop, hiring, pioneering the wet and dry lab has become incredibly important, so that we not only generate data, but then we generate purpose-built models that we test, learn, and improve.

所以，你看，現在很多人都在談論數據，但真正重要的是高品質且符合用途的數據。而對於 Recursion 來說，我們的基礎一直在建立大規模的高品質數據，不僅僅是一種類型的數據集，而是全方位的多模態數據集。正是在這裡，循環實驗室的開拓、招募、濕實驗室和乾實驗室的開拓變得極為重要，這樣我們不僅可以產生數據，還可以產生專門建構的模型，進行測試、學習和改進。

The other thing I want to say is we sit in a sweet spot of being able to leverage both public data and our proprietary private data. That's incredibly important to ensure that our models, Are impactful, insightful, and unique.

我想說的另一點是，我們處於一個有利位置，能夠同時利用公共資料和我們專有的私有資料。這一點至關重要，它可以確保我們的模型具有影響力、洞察力和獨特性。

And on top of that, I've mentioned this before, the importance of not just having the ingredients, but actually having a team who knows how to use it well, teams that are bilingual, fluent in science and in AI. But I want to add a third lens. It's also important to have reps under your belt to know what good looks like and having talented teams that have reps is one of our core differentiators. But the ultimate secret sauce, I will say, is how it all comes together.

除此之外，我之前也提到過，重要的不僅是擁有原料，還要擁有一支懂得充分利用這些原料的團隊，一支精通雙語、科學和人工智慧的團隊。但我還想加裝第三個鏡頭。累積實戰經驗也很重要，這樣才能知道好的表現是什麼樣的，而擁有經驗豐富的優秀團隊是我們的核心優勢之一。但我認為，最終的秘訣在於所有元素是如何融合在一起的。

Having an integrated end to end operating system that is a continuous learning loop all the way from novel biology or novel insights through to the clinic.

擁有一個整合的端到端作業系統，這是一個持續學習循環，從新的生物學或新的見解一直到臨床應用。

Look, for many of us that have actually made medicines and have focused on this, which is a humbling effort, we all know that improving one decision in R&D is simply not enough. It's the compounded impact of better decisions across Molecule, biological insight all the way through the clinic, that is what makes the difference. That's how you truly change not just the outcome, but also the time and cost and how you do things, and that's what we are focused on at recursion.

你看，對於我們這些真正從事藥物研發並專注於此的人來說（這是一項令人謙卑的努力），我們都知道，在研發過程中改進一個決策是遠遠不夠的。正是分子層面更明智的決策，以及從生物學到臨床各環節的深入洞察，所產生的累積效應，才真正帶來了改變。這樣才能真正改變結果，以及時間和成本，還有做事的方式，而這正是我們在遞迴中關注的重點。

So, what does that result in?

那麼，這會導致什麼結果呢？

First of all, in our clinical development, we have a diversified portfolio.

首先，在臨床開發方面，我們擁有多元化的產品組合。

We are very encouraged by our first AI enabled clinical proof of concept with FAP, which has the potential to be a first in class for FAP, but we also have additional programs behind that.

我們首次利用人工智慧技術在家族性腺瘤性息肉症（FAP）領域取得臨床概念驗證成果，這令我們倍感鼓舞。這項技術有望成為FAP領域的首創，但我們還有其他計畫在背後支持。

In addition to that, in our discovery portfolio, we also have another diversified set of programs, and specifically, I'll just touch on the partner piece where we have brought in over half a billion in upfront and also milestones, and we'll share some additional updates today. I just want to say every single milestone we achieve, is not just, it improves the economics, but it's also a validation of the platform and a validation that we are learning fast in terms of what works, what doesn't to make our platform ever more intelligent.

除此之外，在我們的研發組合中，我們還有另一套多元化的項目，具體來說，我將重點介紹合作夥伴項目，我們已經獲得了超過 5 億美元的預付款和里程碑付款，今天我們將分享一些其他的最新進展。我想說的是，我們取得的每一個里程碑，不僅改善了經濟效益，也驗證了平台的有效性，驗證了我們在哪些方面有效、哪些方面無效方面學習得很快，從而使我們的平台變得更加智能。

In addition to that, let's just talk a little bit about the platform. I, I'm going to share the slide every time we have an earnings because this is so core to what we do. Number one, being end to end, like I said before, is critical. You have to connect biology, chemistry, to ultimately the patient, which is really where the rubber hits the road. That's where we are going. The other thing I also want to say is it's important to, Innovate, not just on data generation, but also your models. So, we have state of the art, and I'll talk a little bit more about this foundation models, not just in phonomics, but transcriptomics, and pulling those together in emerging virtual cell efforts that we're also focused on. We We are also continuing to innovate on additional frontier models in the chemistry space as well as our newly built clinical development AI platform. Again, it is that integration and how you harness it to unlock value that matters the most.

除此之外，我們再簡單談談這個平台。我打算每次發布財報時都分享這張投影片，因為這對我們的工作至關重要。第一，正如我之前所說，端到端的設計至關重要。你必須將生物學、化學最終與病人聯繫起來，這才是真正考驗醫術的地方。這就是我們要去的地方。我還要補充一點，那就是創新很重要，不僅在數據生成方面要創新，在模型方面也要創新。所以，我們擁有最先進的技術，我將更詳細地談談這些基礎模型，不僅是音系學，還有轉錄組學，並將這些整合到我們正在關注的新興虛擬細胞研究中。我們也將繼續在化學領域的其他前沿模型以及我們新建立的臨床開發人工智慧平台方面進行創新。再次強調，最重要的還是這種整合以及如何利用它來釋放價值。

Next one.

下一個。

So, in terms of our strategic pillars, we have 3 main areas that we're doubling down on in this new chapter. Number 1, tangible proof points. This is so important, both from our clinical portfolio, as well as our partner programs.

因此，就我們的策略支柱而言，在這個新篇章中，我們將在以下三個主要領域加倍投入。第一點，切實的證據。這一點非常重要，無論是對我們的臨床產品組合，還是對我們的合作夥伴專案而言。

Second, like I said before, in parallel, continuing to invest surgically in our platform, grounded in areas that will enable us to have more of those proof points. And third, but certainly not the least, pairing that bold ambition that we have with discipline, execution, how do we do more with less?

其次，正如我之前所說，同時，我們將繼續對我們的平台進行精準投資，重點放在能夠讓我們獲得更多此類證明點的領域。第三，但同樣重要的是，如何將我們所擁有的遠大抱負與紀律和執行力結合起來，如何用更少的資源做更多的事？

So let's go through each of these. If you go to the next slide, one area that's really important for us is we like to track what are our wins and learnings as we go through each of these pillars, so you'll get used to seeing that as well going forward.

那麼，讓我們逐一了解。如果你看下一張投影片，你會發現，對我們來說非常重要的一個方面是，我們喜歡追蹤我們在實現每個支柱的過程中取得的成功和獲得的經驗教訓，所以你以後也會經常看到這些內容。

First, in our first pillar, which is really focused around making progress around clinical pipeline as well as our partner programs.

首先，在我們的第一支柱中，我們真正專注於在臨床研發管線以及我們的合作夥伴計畫方面取得進展。

First, FAP, this is really important data for a disease that has no approved therapies to date, durable and meaningful poly burden reduction.

首先，FAP，對於一種目前尚無核准療法的疾病來說，這是非常重要的數據，它能帶來持久而有意義的多發性腺瘤負擔減輕。

Second, today we'll highlight our Sanofi collaboration, just as a reminder, this is where we're tackling challenging targets in INI and oncology and leveraging our AI component, chemistry component of our platform to design novel compounds.

其次，今天我們將重點放在我們與賽諾菲的合作。提醒一下，我們正在利用我們平台的 AI 組件和化學組件，攻克 INI 和腫瘤學領域的挑戰性目標，設計新型化合物。

And here, we just achieved our 5th milestone to date. We'll do a double click on this, but this is an example of the repeatability of our platforms, especially around using AI to develop chemistry molecules and small molecules.

我們剛剛達成了迄今為止的第五個里程碑。我們將對此進行雙擊，但這正是我們平台可重複性的一個例子，尤其是在使用人工智慧開發化學分子和小分子方面。

Second pillar is really focused on our platform, and I want to highlight two things here.

第二大支柱主要集中在我們的平台上，我想在這裡強調兩點。

As we look across the portfolio, we look at green shoots, as I like to call it, proof points, where we're actually seeing that we can do things better and faster. So, one example is again in our AI enabled chemistry platform. When we look across the portfolio, we're synthesizing 90% fewer compounds. Than what we see in industry, so about 300 versus 2,500 compound synthesizes. This is because we are predicting more and making less. This is where in silica approaches should be guiding us, and we're seeing that happen, and we're doing this 2 times faster. So instead of it taking us, taking, the industry 42 months, we're seeing on average it takes us 17 months. We're going to keep pushing on this. The other area, let's talk about biology. We talk constantly about the. Amount of unknown biology and what we're trying to do is generate, and we have generated first in industry, maps of biology, these huge atlases where we are trying to uncover unknown biology. This is in partnership with our great partners at Roche Genetic, two back to back maps that were just accepted, and now the team is hard at work in translating those maps into novel biological programs.

當我們審視整個投資組合時，我們會看到一些積極的跡象，我喜歡稱之為“證明點”，在這些方面，我們確實看到我們可以做得更好、更快。例如，我們的人工智慧化學平台就是一個例子。當我們縱觀整個產品組合時，我們合成的化合物數量減少了 90%。與工業界所見相比，約有 300 種化合物合成，而工業界則有 2500 種化合物合成。這是因為我們預測的內容越來越多，但實際收益卻越來越少。這就是矽方法應該指導我們的地方，我們正在看到這種情況發生，而且我們的速度提高了 2 倍。所以，以前我們整個產業需要 42 個月的時間，現在平均只需要 17 個月。我們會繼續推進這件事。另一個領域，我們來談談生物學。我們一直在談論這件事。未知的生物學領域還有很多，我們正在努力做的是繪製生物學地圖，我們已經率先在業界繪製了這些巨大的圖譜，我們試圖從中發現未知的生物學。這是我們與羅氏基因公司（Roche Genetic）的優秀合作夥伴共同完成的，我們連續繪製了兩張圖譜，這兩張圖譜剛剛被接受，現在團隊正在努力將這些圖譜轉化為新的生物學程序。

And our third pillar, momentum with discipline. Look, we have a lot of things we want to do, but we have to do it with discipline and good financial stewardship, financially, of course, but also operationally. And we're really excited to share that first of all, we've seen a 35% reduction in pro forma operating expenses year over year. This has come from multiple areas, sharper focus on our portfolio, yes, but then also optimizing our G&A.

我們的第三個支柱是：在保持紀律的同時保持動力。你看，我們有很多想做的事情，但我們必須以自律和良好的財務管理來做，當然，財務方面如此，營運方面也如此。首先，我們非常高興地宣布，我們的預計營運費用將年減了 35%。這源自於多個方面，包括更專注於我們的投資組合，以及優化我們的一般及行政費用。

And improving our platform efficiency, which is an example of it you just heard about in the last slide, in terms of the number of compounds we're synthesizing, our speed, etc. And the other thing that we're excited to share today is extending our runway to early 2028.

提高我們平台的效率，就像你在上一張投影片中聽到的一個例子，例如我們合成的化合物數量、速度等等。今天我們很高興地宣布，我們將把生產計劃的實施期限延長至 2028 年初。

All right, so let's dive into each of these pillars a little bit more.

好的，那麼讓我們更深入地了解這些支柱。

Starting with our wholly owned pipeline.

首先從我們全資擁有的管道開始。

Look, when we look at the number of programs here, we have a diversified portfolio. There are different types of differentiation across each of these programs, and I'm going to categorize it in three ways. Number 1, they are programs with novel biological insight from our platform. Number 2, there are programs that have emerging biology, interesting biology, which is unconquered, not validated yet, and we have developed optimized programs. And then the 3rd is really focused around areas that have validated biology, but there's significant unmet need that still exists from a patient perspective.

你看，從我們這裡開設的課程數量來看，我們的課程組合非常多元。這些項目之間存在不同類型的差異，我將從三個方面進行分類。第一，它們是我們平台上具有新穎生物學見解的程式。第二，有些項目涉及新興生物學、有趣的生物學，這些生物學尚未被征服、尚未被驗證，而我們已經開發了優化的程序。第三部分則主要關注那些生物學上已被驗證的領域，但從患者的角度來看，仍然存在著巨大的未滿足需求。

So you've seen this slide before, we always track which components of our platform are we using across our various programs. So let's dive into a little bit more around the three categories, starting with the platform-derived novel biological insight.

所以你以前肯定見過這張投影片，我們總是會追蹤我們在各種專案中使用的平台元件。那麼，讓我們更深入地了解這三個類別，首先從平台衍生的新型生物學見解開始。

All right.

好的。

Two programs that exist in that category, one, FAP 4,881, R 4,881. 1st of all, I don't need to say again, but like, the reason why there's such a significant unmet need, there's nothing approved for these patients. This is a disease that is hallmarked by hundreds of polyps, each and every one of which is precancerous and has a 100% risk of CRC colorectal cancer by the time you're 40.

在該類別中存在兩個項目，一個是 FAP 4,881，另一個是 R 4,881。首先，我不需要再說一遍，但是，之所以存在如此巨大的未滿足需求，是因為目前還沒有任何藥物獲批用於這些患者。這種疾病的特徵是長出數百個息肉，每一個息肉都是癌前病變，到 40 歲時患上大腸直腸癌 (CRC) 的風險為 100%。

More than 50,000 addressable patients in the US and EU.

美國和歐盟有超過 5 萬名潛在患者。

The recursion differentiation is using the Phenomics, the early version of the Phenomics platform to ascertain in an unbiased fashion that MET-1-2 inhibition could actually work in FAP.

遞歸區分是利用表型組學（表型組學平台的早期版本）以公正的方式確定 MET-1-2 抑制是否真的對 FAP 有效。

We have just completed our phase two study. We had a positive, a clinical POOC which we just shared in December, and I'll share a little bit more about the data just to recap for those who might have missed it. And one of our core next steps, and we're on track is to initiate FDA engagement on the registrational path, first half of 2026.

我們剛完成了二期臨床試驗。我們在 12 月分享了一個積極的臨床 POOC 結果，我將再分享一些數據，以便那些可能錯過的人回顧一下。我們接下來的核心步驟之一，也是我們目前進展順利的步驟，是在 2026 年上半年啟動與 FDA 的註冊審批流程。

We also have another program that has similar elements from a differentiation perspective, RBM 39. RBM 39, look, it's going to be potentially important in genomically unstable cancers, and from the patient population, as you can see That impacts a wide patient population. The differentiation for recursion in our platform really came from uncovering this MOA and the connection it has to CDK 12, which is known to be important for DDR modulation for many decades, but challenging to target because of the similar homology with CDK 13. Right now, that program is in phase one monotherapy dose escalation. And we expect to share an early phase one update on safety and PK first half of 2026, so later half of this year.

我們還有另一個從差異化角度來看具有類似要素的項目，即 RBM 39。RBM 39，你看，它在基因組不穩定的癌症中可能非常重要，而且從患者群體來看，正如你所看到的，它影響著廣泛的患者群體。我們平台遞歸的差異化真正源自於發現了這種作用機制及其與 CDK 12 的連結。幾十年來，人們都知道 CDK 12 對 DDR 調製很重要，但由於它與 CDK 13 具有相似的同源性，因此很難對其進行靶向。目前，該計畫正處於單藥治療劑量遞增的第一階段。我們預計將於 2026 年上半年（即今年下半年）分享有關安全性和藥物動力學的早期第一階段更新。

All right, let's go to the next category, emerging biology, that unconquered biology, and where we can optimize program. There we have CDK 7 and ENPP1, and you'll see what we're doing from an optimizing the program perspective is both on the chemistry side and also on the clinical development side. So, let's start with CDK 7.

好的，我們進入下一個類別，新興生物學，這個尚未被征服的生物學領域，以及我們可以優化程序的地方。我們有 CDK 7 和 ENPP1，從優化項目的角度來看，我們所做的工作既包括化學方面，也包括臨床開發方面。那麼，讓我們從 CDK 7 開始。

Look, CDA 7 has been known for a long time to be an important central master regulator, both of cell cycle control, but then also of transcription, which are, with a wide variety of patient population that are addressable, given its centrality in oncology.

眾所周知，CDA 7 長期以來都是一個重要的中心主調節因子，它不僅控制細胞週期，還控制轉錄，鑑於其在腫瘤學中的核心地位，它可以應用於各種各樣的患者群體。

From a recursion differentiation perspective, others have tried this target before, and one of the key challenges has been optimizing the PKPD, optimizing the therapeutic index. That's where we have leveraged the second element of our platform, AI chemistry, in order to optimize the molecules, especially around gut permeability.

從遞歸微分的角度來看，其他人以前也嘗試過這個目標，其中一個關鍵挑戰是優化 PKPD，優化治療指數。正是在這裡，我們利用了我們平台的第二個要素—人工智慧化學，來優化分子，特別是腸道通透性方面的分子。

We also are leveraging our platform in order to figure out which patient population should we go into that could potentially impact the most from CDK 7 inhibition.

我們也在利用我們的平台來確定我們應該針對哪些患者群體，以期從 CDK 7 抑制劑中獲益最大。

Progress right now, we finished our phase one monotherapy dose escalation, maximum dose has been selected, and we are in progress of the combination, study, which is focused on ovarian cancer, second line, platinum resistant, with more data expected first half of 2027. And again, apologies, we're working very hard at recursion, which is why I have lost my voice, but I will TRY to make it through, the rest of this presentation.

目前進展順利，我們完成了第一階段單藥治療劑量遞增試驗，確定了最大劑量，正在進行聯合治療研究，該研究針對二線鉑類抗藥性卵巢癌，預計將於 2027 年上半年獲得更多數據。再次致歉，我們正在努力學習遞歸，所以我的嗓子都啞了，但我會努力完成剩下的演講。

All right. The next program that's also in this category.

好的。下一個也屬於這個類別的節目。

Is focused on ENPP1. ENPP1 loss of a certain mutation leads to challenges with bone mineralization, thereby leading challenges in, fractures, pain, etc. Again, another lifelong disease that starts very early in the in the patient's trajectory, life trajectory.

專注於 ENPP1。ENPP1 的某種突變導致骨骼礦化出現問題，進而導致骨折、疼痛等問題。又一種伴隨終生的疾病，它在患者的生命軌跡中很早就開始發作。

The recursion differentiation here is focusing on a molecule that can actually be oral because what's available today for patients and also some of the efforts in investigational agents is around enzyme replacement therapy that requires a huge burden for patient burden in terms of injections, subcutaneous, sometimes multiple a week. So what we wanted to do is design a molecule for EMPP1, which again, challenging target, especially in this space for hyperphosphatasia, which can be suitable for chronic dosing. IND enabling studies ongoing for this program right now, and we expect to have a go no go decision second half of this year on this program.

這裡遞歸區分的重點是能夠口服的分子，因為目前患者可用的藥物以及一些研究藥物都圍繞著酶替代療法，這給患者帶來了巨大的負擔，需要注射、皮下注射，有時一周要注射多次。因此，我們想要設計的是針對 EMPP1 的分子，這又是一個具有挑戰性的靶點，尤其是在高磷酸酶症領域，它可能適合長期給藥。目前該項目正在進行IND申報研究，我們預計將在今年下半年對該項目做出是否批准的決定。

All right.

好的。

The third category. Look, these are some of the, some of, some targets that have validated biology but have significant unmet need that exists.

第三類。你看，這些是一些生物學上已得到驗證，但仍有重大未滿足需求的標靶。

So let's take MLT1.

那我們來看 MLT1。

MALt 1 is validated from a target perspective in B cell drivers, but some of the challenges really have been around limitations around tolerability. So we again leverage our recursion platform to really design molecules that could design away from some of the UGT1A1 and other targets that have been seen, which are going to become increasingly important with combination with BTK inhibitors and others, which is what will be the ultimate efforts in this space. So, we have phase one monotherapy dose escalation ongoing with early phase one update data again on safety and PK monotherapy expecting first half of 27.

從標靶角度來看，MALt 1 在 B 細胞驅動中得到了驗證，但一些挑戰確實與耐受性方面的限制有關。因此，我們再次利用遞歸平台來真正設計能夠避開某些 UGT1A1 和其他已發現的靶點的分子，這些靶點將隨著與 BTK 抑制劑和其他藥物的聯合使用而變得越來越重要，這將是該領域最終的努力方向。因此，我們正在進行一期單藥治療劑量遞增試驗，預計 2027 年上半年再次公佈一期單藥治療安全性和藥物動力學的早期更新數據。

Another program that has a similar theme is LSD1. LSD one is known to be an epigenetic regulator, really trying to prevent or inhibit some of the differentiation that you see in solid tumors such as small cell lung cancer and also AML with some validated data seen in AML recently.

另一個主題類似的程式是 LSD1。已知 LSD 是一種表觀遺傳調節劑，它試圖阻止或抑制實體瘤（如小細胞肺癌和 AML）中的一些分化，最近在 AML 中也觀察到了一些驗證數據。

And the differentiation again here is, can we design out some of the challenges around tolerability, which has led to some DLTs and not being able to dose up high enough, such as thrombocytopenia. This two phase one monotherapy dose escalation is in startup, and next steps is to have early phase one update on safety and PKA monotherapy expected second half of 2027. Again, we expect to start to understand if some of the tolerability improvements we're trying to do, can we actually see that early on. This is our theme around early go, no go decisions to really understand is the design playing out in the clinic.

這裡的差異在於，我們能否透過設計消除一些與耐受性相關的挑戰，這些挑戰導致了一些劑量限制性毒性（DLT）和無法達到足夠高的劑量，例如血小板減少症。這項為期兩個階段的單藥治療劑量遞增試驗正在啟動中，下一步是爭取在 2027 年下半年獲得早期第一階段安全性和 PKA 單藥治療的更新結果。再次強調，我們希望能夠儘早了解我們正在嘗試的一些耐受性改進措施是否真的能帶來效果。這就是我們圍繞早期決策的主題，即“開始”或“停止”，以便真正了解設計在臨床中的實際應用。

And another program that's in pre-clinical, and, late preclinical is our PI3K 1,047 mutant selected. PI3K and 10, PI3K in general is an important oncogenic mutation linked to resistance and relapse, etc. And I'll walk through a deep dive in terms of some of the latest data we have here, where again, remember, we use our platform to design a molecule that would be much more selective over 100 100x selectivity over wild type PI3K which leads to some of the tolerability challenges that leads to dose interruptions and reductions, and more to come there, but that's an I&D enabling study. Again, go, no go decision, second half of this year expected, before we consider a phase one initiation.

另一個處於臨床前和臨床前後期階段的項目是我們篩選出的 PI3K 1,047 突變體。PI3K 和 10，PI3K 一般而言是重要的致癌突變，與抗藥性和復發等有關。接下來，我將深入探討我們掌握的一些最新數據。請記住，我們利用我們的平台設計了一種分子，其選擇性比野生型 PI3K 高出 100 倍以上。這導致了一些耐受性方面的挑戰，例如劑量中斷和減少，未來還會有更多相關內容。但這是一項研發支持研究。再次重申，在考慮啟動第一階段之前，我們需要在今年下半年做出是否啟動的決定。

So I know that was a around the trip, around our portfolio, but I would love to actually double click on one of our later stage, which is RE 481, and then also one of our earlier stage and potentially in entering our clinical pipeline, which is our PI3K program.

我知道這次旅行和我們的投資組合之間是有一定關聯的，但我很想重點介紹我們後期階段的專案 RE 481，以及我們早期階段的專案 PI3K，該專案可能正在進入我們的臨床研發管線。

So, let's go through the rec4 1.

那麼，讓我們來看看 rec4 1。

I'm just going to do a quick update here, for this program, we had our clinical POC late last year. And a couple of things to note, no approved therapies. What we saw in our phase 23 months on treatment with 4 mg qd of this MEC12 inhibitor, significant polyp burden reduction, about 43% median. Highest, one of the higher polyp burden reductions to date, 75% of the patients responded.

我在這裡簡單報告一下最新情況，關於這個項目，我們去年底完成了臨床概念驗證。還有兩點要注意，目前尚無核准療法。我們在 23 個月的試驗中觀察到，每天服用 4 毫克 MEC12 抑制劑治療後，息肉負荷顯著降低，中位數降低了約 43%。最高療效是迄今為止息肉負擔減輕幅度最大的療效之一，75% 的患者有療效。

In terms of the AEs that we see, very much in line with what you see from MAC 1-2 inhibitors, majority were grade 12, RASH CPK, and no grade 4-5 to date.

就我們所觀察到的不良事件而言，與 MAC 1-2 抑制劑的情況非常一致，大多數為 12 級，即皮疹和 CPK，迄今為止還沒有 4-5 級不良事件。

What we also saw, which was even more encouraging, was when these patients were then off treatment for 3 months, and remember, this is a chronic disease, so the on-off element is going to be really important for us to understand, and we're the first, to actually look at on and off in this disease area, we see continued durable polyp burden reduction in some cases actually deepening and with a significant amount of the patients actually responding.

我們也觀察到一個更令人鼓舞的現象：當這些患者停止治療 3 個月後，要知道，這是一種慢性疾病，所以停藥治療的因素對我們來說非常重要，而我們是第一個真正研究這種疾病領域停藥治療的團隊，我們看到息肉負擔持續持久地減少，在某些情況下甚至有所加重，而且相當一部分患者實際上有反應。

So this is a really important, what I said at the top of the call, like, it's important to not just have insights, but how do you turn those into something that's meaningful for patients and then ultimately new medicines.

所以，正如我在電話會議開始時所說，這非常重要，重要的不僅是擁有見解，更重要的是如何將這些見解轉化為對患者有意義的東西，並最終轉化為新藥。

So I won't recap in terms of the insight to proof point, but I'll focus on what's next. We're on track as we discussed late last year in terms of the FDA engagement initiating that first half of 2026 to really discuss the registrational study design. In addition to that, we've already started the enrollment of 18 and over cohort. And as you remember, some of the data we shared was for 55 and over, so we're already progressing on the 18 and over, and then also advancing dose optimization efforts, really inspired by what we saw with the durability data that I shared in the last slide. So, we expect to have additional clinical data, first half of 2027, as well.

因此，我不會回顧從洞察到證明點的過程，而是專注於接下來的事情。正如我們去年底討論的那樣，我們正按計劃推進與 FDA 的合作，計劃在 2026 年上半年啟動，真正討論註冊研究的設計。除此之外，我們已經開始招收 18 歲及以上的學員。如您所知，我們分享的一些數據是針對 55 歲及以上人群的，因此我們已經在 18 歲及以上人群方面取得了進展，並且還在推進劑量優化工作，這確實受到了我在上一張幻燈片中分享的耐久性數據的啟發。因此，我們預計在 2027 年上半年也將獲得更多臨床數據。

So stay tuned, more to come.

敬請期待，更多精彩內容即將呈現。

Now, let's move to another exciting program that we have in our pipeline. This is our PI3K 1,047 mutant selective. So look, for PI 3K, I'm sure you're thinking, Jacques, there are multiple PI3K. Why are we working on PI3K? First of all, This is a very important target across multiple solid tumors. The current PI3K inhibitors have been constrained, and we have some data that we'll share shortly, hyperglycemia, metabolic toxicity, dose interruptions, dose reductions, limited treatment duration, all of that means, is there an opportunity to do better by patients? There's an unmet need that still exists.

現在，讓我們來看看我們正在籌備的另一個令人興奮的項目。這是我們的 PI3K 1,047 突變體選擇性。所以你看，對於 PI 3K，我肯定你在想，雅克，有好幾個 PI3K 呢。我們為什麼要開發 PI3K？首先，這是多種實體瘤中一個非常重要的標靶。目前的 PI3K 抑制劑存在一些局限性，我們有一些數據（我們很快就會分享），例如高血糖、代謝毒性、劑量中斷、劑量減少、治療持續時間有限等等，所有這些都意味著，我們是否有機會為患者做得更好？仍然存在尚未滿足的需求。

So, what is our differentiation and what is our thesis, is really focusing on the 1,047 mutant selective, which has 100x more selectivity over a wild type, thereby having the potential to minimize risk for AES.

所以，我們的獨特之處和我們的論點是，我們真正關注的是 1,047 突變選擇性，它比野生型具有 100 倍的選擇性，從而有可能最大限度地降低 AES 的風險。

And in order to do that, we designed a molecule that can allow us to have that exquisite selectivity.

為了實現這一目標，我們設計了一種能讓我們獲得這種極佳選擇性的分子。

And with that, let me just actually walk you through something that's very exciting from a platform perspective.

接下來，讓我從平台角度帶您了解一些非常令人興奮的內容。

For this program, we started off with X-ray structures that where we had proprietary structural insight.

在這個專案中，我們首先從我們擁有專有結構見解的 X 射線結構入手。

And that led us to leveraging our MD simulations, and this is where compute becomes really important. Our molecular dynamic simulations revealed a novel pocket.

因此，我們開始利用分子動力學模擬，而運算能力正是在這裡變得至關重要。我們的分子動力學模擬揭示了一種新型口袋。

We then use our generative 3D modeling efforts and machine learning in order to design molecules, novels scaffolds for this novel pocket, and we were able to use other approaches, our other ML approaches to really rapidly design our cycles, so you get exquisite potency, but then also selectivity. Remember, it is that selectivity that leads to the tolerability challenges we talked about.

然後，我們利用生成式 3D 建模和機器學習技術來設計分子，為這個新型口袋設計新型支架，並且我們能夠使用其他方法，即我們的其他機器學習方法，來真正快速地設計我們的循環，這樣你不僅能獲得極佳的效力，還能獲得選擇性。記住，正是這種選擇性導致了我們之前討論過的耐受性挑戰。

And I want to take a moment, like, it's just look at the lower bar here, in order to design this compound.

我想花點時間，例如，看看這裡的下橫梁，以便設計這個化合物。

We designed 242 compounds, 13 cycles in 10 months. This is what we want to see from a green shoot perspective of the platform. Can you do it better? Can you do it faster?

我們在 10 個月內設計了 242 種化合物，進行了 13 個週期。這是我們希望從平台萌芽階段看到的結果。你能做得更好嗎？你能做得更快嗎？

And this is what we're tracking across our entire portfolio. I can tell you, compared to industry standards, this is fast.

這就是我們在整個投資組合中追蹤的內容。我可以告訴你，與行業標準相比，這速度很快。

And this is what gets us excited, data that we can actually do things better, faster.

而這正是讓我們興奮的地方，數據顯示我們實際上可以做得更好、更快。

But then, the next question is, how does this molecule do? So I'll share some pre-clinical data that we haven't shared before.

但是，下一個問題是，這種分子表現如何？因此，我將分享一些我們之前沒有分享過的臨床前數據。

First, let's look at how it does from a tumor reduction regression perspective. So if you look at the left-hand side over here, what you're looking at here is a dose-dependent tumor regression for a compound, which is in blue. And we actually also looked at some of the compounds that are in the market, such as PICCA and also scorpions compound, just to get a sense of how we're doing. And we see significant tumor regression, not just reduction, but regression with this compound.

首先，讓我們從腫瘤消退的角度來看看它的效果如何。所以，如果你看一下左邊這邊，你看到的是藍色化合物的劑量依賴性腫瘤消退情況。我們實際上也研究了一些市面上的化合物，例如 PICCA 和蝎子化合物，只是為了了解我們目前的進展。我們發現，使用這種化合物後，腫瘤不僅縮小，而且完全消退，出現了明顯的消退現象。

Comparable to what you see with Scorpion and much better than what you see with Pray.

與 Scorpion 的效果相當，比 Pray 的效果好得多。

But given the standard of care, we also wanted to see the performance versus standard of care. So, with surge, with CDK 4-6 inhibitors, which is that com is the standard of care today, and what's exciting to see here is the synergy. Monotherapy, yes, you see reduction and regression without compound, but you actually see synergistic efforts with the standard of care. This is very encouraging. We actually have additional data, we only have so many charts we have space for, but we also looked at other encouraging assets in the space such as cappi.

但考慮到護理標準，我們也想看看其療效與護理標準的比較。所以，隨著 CDK 4-6 抑制劑的出現，這種藥物目前已成為標準療法，而令人興奮的是，它能產生協同作用。單藥治療確實可以減輕病情，無需合併用藥即可緩解症狀，但實際上，單藥治療與標準治療相比，可以產生協同效應。這非常令人鼓舞。我們實際上還有更多數據，只是圖表數量有限，空間有限，但我們也關注了該領域其他一些令人鼓舞的資產，例如 Cappi。

And we saw improved tumor regression with low dose of our acid versus high dose cap.

我們發現，與高劑量組相比，低劑量組的酸能更好地促進腫瘤消退。

So all in all, this is encouraging from an efficacy perspective for this compound.

總而言之，從功效角度來看，這對該化合物來說是令人鼓舞的。

But then we also wanted to look at tolerability.

但我們還想考察一下耐受性。

So, here, what you're seeing is animal models for, from both naive wild type and then also obese diabetic animal models as well. On the left-hand side, you see, we don't see any impact on hyperglycemia markers in naive wild-type mice versus what you see with scorpion and pray as well.

所以，在這裡，你看到的是動物模型，包括未經處理的野生型動物模型和肥胖糖尿病動物模型。在左側，您可以看到，與蝎子和獵物相比，我們沒有看到野生型小鼠的高血糖標記物受到任何影響。

Which is encouraging. This is what we are designing the molecule to do. And then if you go to the right side, a little bit complicated, but we like to share data. Also, in obese diabetic rats, you don't see hyperglycemia or the metabolic liability even at supra efficacious dose for our asset.

這令人鼓舞。這就是我們設計該分子的目的。然後，如果你去右邊看看，會有點複雜，但我們喜歡分享數據。此外，在肥胖糖尿病大鼠中，即使使用我們產品的有效劑量，也不會出現高血糖或代謝紊亂。

Versus scorpion and pray as well. So again, taken together, this is encouraging, but like I always say, the rubber hits the road in the clinic. So what does this mean from a clinic perspective? Look, current PI3K inhibitors, focusing on HR positive breast cancer, they do have tolerability limitations. 65 to 85% experience hyperglycemia.

對抗蝎子並祈禱。所以，總的來說，這令人鼓舞，但就像我常說的，臨床實踐才是檢驗真理的唯一標準。那麼從臨床角度來看，這又意味著什麼呢？目前針對 HR 陽性乳癌的 PI3K 抑制劑確實存在耐受性限制。65%至85%的人會出現高血糖。

Large percent actually also have dose interruptions, dose reductions, some of those driven by the hyperglycemia they're experiencing. And we also did some, real-world analysis as well, given our clinical development AI platform, but really thinking about what the target product profile could look like, and you see the discontinuation about 3 to 6 months. I mean that's not a very long time. So, I think the potential here, and we'll have to see A, how the compound does through IND enabling studies, and that's where we're at today.

事實上，很大一部分患者也出現了劑量中斷、劑量減少的情況，其中一些是由於他們正在經歷的高血糖所致。鑑於我們的臨床開發人工智慧平台，我們也進行了一些現實世界的分析，認真思考目標產品概況可能會是什麼樣子，結果發現停產時間大約在 3 到 6 個月之間。我的意思是，那時間不算長。所以，我認為這裡有潛力，我們還要看看 A，也就是該化合物在 IND 申報研究中的表現，這就是我們目前所處的階段。

I can we expand that patient population in twofold. Number one, in breast cancer, not just in patients that are non-diabetic, but also patients that are pre-diabetic and diabetic, if this trajectory of hyperglycemia markers are not having impact holds. That's about 50, 50% in breast cancer. And then the other is there's also broader patient population such as colorectal and endometrial, we can also explore.

我們可以將患者群體擴大一倍。第一，在乳癌中，不僅對於非糖尿病患者，而且對於糖尿病前期和糖尿病患者，如果高血糖標記的這種軌跡沒有產生影響，那麼就應該繼續下去。大約是50%，乳癌的發生率約為50%。此外，我們還可以探索更廣泛的患者群體，例如大腸癌和子宮內膜癌患者。

And one thing I'd be interested to also look at is can these patients because of the better tolerability, stay on longer treatment duration to really maximize the impact of these therapies. But again, clinical validation and improved tolerability requires, is critical to confirm this expansion thesis. So if you go to the next slide, more to come. But again, we keep looking at these arcs. What was the insight? What did we design the molecule? What are the early proof points so far that you saw with pre, pre-clinical data, and what's next right now is the go no go decision for phase one, which would be the second half of this year. So currently, the study is an ID.

我也想研究一下，由於這些患者耐受性更好，他們能否接受更長時間的治療，從而真正最大限度地發揮這些療法的效果。但是，臨床驗證和提高耐受性對於證實這一擴展論點至關重要。所以，如果你翻到下一張投影片，還有更多內容等著你。但我們仍然要繼續關注這些弧線。你發現了什麼？我們設計了什麼樣的分子？到目前為止，您在臨床前數據中看到了哪些早期證據？接下來是決定是否啟動第一階段試驗，這將在今年下半年進行。所以目前，這項研究是一項ID研究。

All right, that was just our first pillar, double click. We'll also do a little bit more around our partnerships. I'm really excited to share the progress we're making because remember, proof points can come from both your internal portfolio, which is what we just focused on, but then also from our amazing partners that we're working with on actual programs.

好了，這只是我們的第一個支柱，雙擊。我們也會就合作關係方面做一些額外的工作。我非常高興與大家分享我們所取得的進展，因為請記住，證明點既可以來自我們剛才重點討論的內部專案組合，也可以來自我們正在合作進行實際專案的優秀合作夥伴。

To date, we have already achieved over 500 million in total cash inflows from our partnership, both upfronts and milestones, and we've actually laid out some of those recent ones with the momentum that they, we've been achieving recently.

迄今為止，我們已從合作關係中獲得了超過 5 億美元的現金流入，包括預付款和里程碑付款，而且我們已經公佈了其中一些近期的進展情況，以及我們最近取得的進展勢頭。

But I want to emphasize something that sometimes gets lost, each and every one of the programs that we're working on.

但我想要強調一點，這一點有時會被忽略，那就是我們正在開發的每一個專案。

Has a potential for over 300 million in milestones and tiered royalty per small molecule program. Some of the royalties are up to double-digit royalties. So this is significant economics, and also validation opportunity for recursion.

每個小分子計畫都有可能獲得超過 3 億美元的里程碑付款和分級特許權使用費。部分版稅高達兩位數。所以這具有重要的經濟意義，也是遞迴的驗證機會。

All right.

好的。

We're very excited for the first time today to unveil our joint portfolio with Sanofi. I mean, Sanofi has been a fabulous partner. We learned so much from that exceptional team, both across INI and oncology.

今天，我們非常興奮地首次向大家公佈我們與賽諾菲的聯合產品組合。我的意思是，賽諾菲一直是個非常棒的合作夥伴。我們從這支傑出的團隊身上學到了很多東西，無論是在 INI 還是腫瘤學領域。

And what we're showing here is the multiple programs that we're working on 5 and with multiple early discovery programs as well. And you see, just like our internal pipeline, this is also a diversified pipeline. It's focused on challenging targets in INI and oncology, with molecules that have the potential to be first in class and or best in class.

我們在這裡展示的是我們正在進行的多個項目，以及多個早期發現項目。你看，就像我們的內部管道一樣，這也是一個多元化的管道。它專注於 INI 和腫瘤學領域的挑戰性靶點，其分子有潛力成為同類首創或同類最佳。

With programs that address very specific unmet needs. So thinking with the clinic and in mind.

提供針對特定未滿足需求的方案。所以要考慮到診所的情況。

And to date, we have advanced 5 lead packages that has been delivered by recursion across 5 of these programs, and except by, accepted by Sanofi to date. That's about 34 million in milestones to date, in addition to the 100 million in upfront, so 134 million so far.

到目前為止，我們已經推進了 5 個主要方案，這些方案透過遞歸的方式在 5 個項目中交付，並且迄今為止已被賽諾菲接受。到目前為止，里程碑付款已達約 3,400 萬美元，加上預付款 1 億美元，總計已達 1.34 億美元。

And I just want to say we have a lot of important work ahead of us with later stage discovery milestones over the next 18 months. And look, discovery is probabilistic, we know some will work and some of these programs won't, but it is the repeatability and the ability for Our platform to have multiple shots on goal that's incredibly critical for us that's what you see with our internal portfolio that's what you see as we work humbly with our partners to also advance important programs for patients in areas that are challenging.

我只想說，在接下來的 18 個月裡，我們還有很多重要的工作要做，包括後期發現階段的里程碑。你看，探索是機率性的，我們知道有些專案會成功，有些專案不會成功，但對我們來說，平台的可重複性和多次嘗試的能力至關重要。這就是你從我們的內部專案組合中看到的，也是你從我們與合作夥伴謙遜合作，共同推進在具有挑戰性的領域為患者提供重要專案的實踐中看到的。

So just double clicking on one of these, how do we get there? Remember, these are challenging targets and we are leveraging our platform, and I just want to explain one aspect that I think is really important.

那麼，雙擊其中一個，我們該如何進入那個介面呢？請記住，這些都是具有挑戰性的目標，我們正在利用我們的平台，我只想解釋我認為非常重要的一個方面。

Our platform is not about one data, one model, one asset. It's about the confluence of a suite of them that you use for the problem at hand. So, we start with the problem first, and then you have flexibility and optionality across our models to get to the best outcome. And so, again, our latest program, where we just got a milestone, our 5th milestone that we just achieved in the oncology program, really focused on leveraging A, these are targets that are data poor. So we leverage both our physics-based approaches as well as our machine learning approaches, physics-based to really understand the protein flexibility better, by novel target, novel pockets, and then leverage our machine learning, algorithms in order to rapidly do our design-ma test cycle and find highly potent molecules, that are now progressing to the next stage.

我們的平台並非只關乎單一資料、單一模型或單一資產。關鍵在於將一系列方法結合起來，用於解決當前的問題。因此，我們首先從問題入手，然後我們的各種模型提供靈活性和選擇性，以達到最佳結果。因此，我們最新的項目，我們剛剛取得了里程碑式的進展，我們在腫瘤學項目中實現了第五個里程碑，該項目真正專注於利用 A，這些是數據匱乏的目標。因此，我們結合了基於物理的方法和機器學習方法，利用基於物理的方法，透過新的靶點、新的口袋，更好地了解蛋白質的柔性，然後利用機器學習演算法，快速進行設計測試循環，找到高效的分子，這些分子現在正進入下一階段。

Very exciting progress here and stay tuned. More to come.

這裡取得了令人振奮的進展，敬請期待。更多內容敬請期待。

But this is truly what proof points look like, actually showing value that will matter for the medicines that we are working towards. But look, none of this can happen without a unique and differentiated platform. That is an ever-important work in progress. So, I want to just do a snapshot of the three components of our platform, starting with biology to insight.

但這才是真正的證據點，它確實展現了對我們正在研發的藥物而言重要的價值。但是，如果沒有一個獨特且差異化的平台，這一切都不可能發生。這是一項至關重要的進行中工作。所以，我想簡單介紹一下我們平台的三個組成部分，從生物學到洞察力。

I mentioned about the proprietary data that recursion has been building for a decade, 50/50 petabytes of high-quality multimodal data, and I want to emphasize the multimodal piece, biology is complex and having diversity of data and, having at scale data sets complete to the extent possible, whole genome knockout overexpression, that's the kind. Kind of model data that you need to then build foundation models that are state of the art. We have a fantastic team that's working on this, whether it's in the phenomics Foundation models or the transcriptonic transcriptomic Foundation models and combining those is the fusion of those models that are going to be really important in biology because we all know we need to connect input to output, genetics.

我提到了 Recursion 十年來一直在構建的專有數據，50 PB 的高質量多模態數據，我想強調多模態這一點，生物學很複雜，數據種類繁多，而且大規模地擁有盡可能完整的數據集，例如全基因組敲除過表達，這才是關鍵所在。你需要這類模型資料來建構最先進的基礎模型。我們有一個非常棒的團隊正在研究這個，無論是在表型組學基礎模型還是轉錄組學基礎模型中，將這些模型結合起來，將會對生物學產生非常重要的影響，因為我們都知道我們需要將輸入與輸出（遺傳學）聯繫起來。

Transcriptomic, proteomic, phenomic, patient data, that's the effort that we're focused on, and how do we leverage it? That's the so what is what matters, is creating these novel proprietary data sets. We call them biology maps, and, we have those internally across different therapeutic areas. We also have it in neuroscience and GIOC with Roche Genentech, and that's what those insights is what's fueling our discovery pipeline.

轉錄組學、蛋白質組學、表型組學、患者數據，這就是我們努力的方向，以及我們如何利用這些數據？所以，真正重要的是創建這些全新的專有資料集。我們稱它們為生物學圖譜，我們在不同的治療領域都有這些圖譜。我們在神經科學和GIOC領域也與羅氏基因泰克合作，而正是這些見解推動了我們的研發進程。

The next area is focused on leveraging AI for chemistry, novel small molecules. I can tell you this is harder than it looks. And, we have used our silica approaches to generate over 100 million molecules. One emphasis, one point I want to emphasize is the point around synthetically aware design.

下一個研究領域著重於利用人工智慧進行化學研究，特別是新型小分子的合成。我可以告訴你，這比看起來要難得多。而且，我們已經利用我們的二氧化矽方法生成了超過 1 億個分子。我想強調的一點是關於合成感知設計這一點。

And there's one thing to design molecules that are interesting, but if you cannot make them, Then that limits or if you can make them, but the CMC is very challenging that really limits that end in mind so we always start with that end in mind, that target product profile, what can be a true drug that matters. We do that across our partnerships and our internal portfolio.

設計有趣的分子是一回事，但如果不能製造出來，那就很有限；或即使能製造出來，但CMC（化學、製造和控制）非常具有挑戰性，這也會限制最終目標的實現，所以我們總是從最終目標出發，即目標產品特性，也就是真正重要的藥物。我們在合作夥伴關係和內部投資組合中都這樣做。

And like I said before, 90% of these molecules are generated or prioritized by our plat by our models. One thing that we're doing increasingly, not just leveraging automation, but also agentic orchestration, so we can get things done better, faster, and in a more unbiased approach.

正如我之前所說，這些分子中有 90% 是由我們的平台透過我們的模型產生或優先考慮的。我們正在越來越多地利用自動化以及代理編排，以便我們能夠以更公正的方式，更快更好地完成工作。

And I mentioned the stat before, but I can't.

我之前提到過這個統計數據，但我現在做不到。

Wait to mention it again. Look, we on average across the portfolio. So with PI3K you said 242 compounds, 10 months, but across the portfolio, we like to be transparent around our data. 330 compounds is what we synthesize on average versus 2,500, 5,000 in industry, and we do it in 17 months on average versus 40 months plus for industry. This is, these are the kinds of things that we track, and that's going from target all the way to advanced candidate.

等會兒再提這件事。你看，我們整個投資組合的平均水準。所以，關於 PI3K，您提到了 242 個化合物，10 個月，但就整個投資組合而言，我們希望在數據方面保持透明。我們平均合成 330 種化合物，而工業界平均合成 2500 到 5000 種；我們平均只需 17 個月就能完成，而工業界則需要 40 個月以上。這就是我們追蹤的內容，涵蓋了從目標候選人到高級候選人的整個過程。

And as a result, we have over 10 development candidates across our internal portfolio and getting to that line with our internal and partner programs as well.

因此，我們內部人才庫中有超過 10 名發展候選人，透過我們的內部和合作夥伴計畫也正在朝著這個目標努力。

And last but certainly not the least, is a, is an area that I get a lot of questions about as well in terms of our newly built emerging clinical development AI platform. What we have done first, and again, just like we did with our biology platform and chemistry, you got to build a really good data foundation, 300 million plus real-world lives, that's, through both some internal work, but then also the great ecosystem, integrated data partnerships. We're very opportunistic around that. So, some of the early results, I mean, you can read the bullets here, but the one that I would point the attention to is enrollment rates. But, we are, to, in order to execute on programs, you have to enroll in a very efficient and intelligent way. And some of our early results for some of the programs, we're starting to see 1.3 to 1.6x improvement. We're also just improving the operational piece that goes underneath it in terms of just starting studies faster. By up to 3 months. All of this accumulates. Remember the point around the compounding impact of decisions across the platform. This is how you do, this is how you define drug discovery and development, leveraging AI. And let me just give you a sneak peek as to how that works on the enrollment front.

最後但同樣重要的是，關於我們新建立的新興臨床開發人工智慧平台，我也常被問到這個問題。我們首先所做的，就像我們之前在生物學平台和化學領域所做的那樣，是建立一個非常好的數據基礎，超過 3 億個真實世界的生命數據，這既是透過一些內部工作實現的，也是透過強大的生態系統和整合的數據合作夥伴關係實現的。我們在這方面非常善於把握機會。所以，一些早期結果，我的意思是，你可以閱讀這裡的要點，但我最想強調的是入學率。但是，為了執行這些計劃，你必須以非常有效率和明智的方式註冊。在一些專案的早期結果中，我們開始看到1.3到1.6倍的改進。我們也在改進其底層操作環節，以便更快啟動研究。最多可延長3個月。所有這些都會累積起來。請記住，平台上的決策會產生累積效應。這就是藥物發現和開發的方法，這就是利用人工智慧來定義藥物發現和開發的方法。讓我先給你們簡單介紹一下招生方面的運作方式。

So we start with the 300 million patient lives.

所以，我們從3億患者的生命開始說起。

Our platform can actually generate a heat map, just like you see for biology or chemistry in different ways, but here, for potential patients across, and we're showing the US here across the country, then we can go into deeper resolution at a state level, and then at a zip code, three-digit zip code level, and then at a site level.

我們的平台實際上可以產生熱圖，就像您在生物學或化學領域看到的那樣，只不過方式不同。但在這裡，我們展示的是整個美國的潛在患者，然後我們可以進一步細化到州一級，再到郵政編碼、三位數郵政編碼級別，最後到站點級別。

And what's really important here is we can also get data around the site's experience with running that trial, and this is, you can probably guess for which program, ovarian cancer, trials, and how many competing trials that exist, that becomes really important. You don't want to fish in the same pond, that can lead to delays.

真正重要的是，我們還可以獲得有關該站點運行該試驗的經驗數據，你可能已經猜到了，對於哪個項目（卵巢癌試驗）以及有多少競爭性試驗存在來說，這一點就變得非常重要了。不要在同一個池塘釣魚，那會造成延誤。

And then beyond that, we can also get what, how many patients do these sites have, and then you can do a filter in terms of your inclusion exclusion and what's relevant for the type of patient that we are looking for in a specific study. That filter does not get happen enough, I can tell you in traditional approaches.

此外，我們還可以了解這些站點有多少患者，然後您可以根據納入排除標準進行篩選，以確定哪些因素與我們在特定研究中尋找的患者類型相關。我可以告訴你，在傳統方法中，這種過濾作用並沒有充分發揮。

I call this, we talk about precision medicine, precision biology, precision chemistry. This is precision operations and starting with the patient in mind.

我稱之為精準醫療、精準生物學、精準化學。這是以病人為中心的精準操作。

With that, thank you for being with me for some time. I want to now hand it over to Ben Taylor, our CFO to actually go through some of our financials.

最後，感謝你們陪伴我這段時間。現在我想把這個任務交給我們的財務長本泰勒，讓他來仔細檢視我們的一些財務數據。

Thanks, Najat Khan.

謝謝，納賈特汗。

So, 2025 was a year of financial transformation for the company.

因此，2025年是該公司財務轉型的一年。

As a part of the integration, we decided to rebuild all of our corporate systems from the ground up. This was really important because we wanted to be able to apply the same level of discipline and rigor to our strategic decision making that we do to all of our scientific decision making.

作為整合的一部分，我們決定從頭開始重建我們所有的企業系統。這非常重要，因為我們希望能夠像對待所有科學決策一樣，對策略決策也採取同樣的嚴謹和自律態度。

And so we looked at how every dollar in the company goes towards a specific quantifiable outcome.

因此，我們研究了公司裡的每一美元是如何用來實現某個具體的、可量化的結果的。

And that's how we were able to achieve the efficiencies that we did over the last year while still advancing up a portfolio of 5 clinical programs, hitting multiple different partner milestones, really investing behind the growth in our, platform as well. And all of that comes back to focus on those investments across our pipeline and technology portfolio that have the best risk return that are going to give us the most impact for the investment that we're making.

正因如此，我們才能在過去一年中取得如此高的效率，同時推進 5 個臨床項目，達成多個不同的合作夥伴里程碑，並真正投資於我們平台的成長。而這一切最終都歸結於關注我們研發管線和技術組合中那些風險回報最佳、能為我們的投入帶來最大影響的投資。

And so that's how we were able to come back and have a 35% year over year reduction from pro forma 24 to 25 and even come in 10% below the guidance that we originally provided in May of last year.

因此，我們得以扭轉局面，實現年減 35%（預計為 24 至 25 美元），甚至比我們去年 5 月最初給出的預期低 10%。

So we ended the year with $754 million in cash. Looking forward, our 2026 cash operating expenses are expected to be under 390 million.

因此，我們在年底時擁有7.54億美元的現金。展望未來，我們預計 2026 年的現金營運支出將低於 3.9 億美元。

Cash operating expenses is a non-GAAP measure that we're going to be using to give you guidance. We have a lot of non-cash, expenses in our P&L and so we wanted to provide something that showed what our cash, profile might look like going forward. And so this is coming directly off of our cash flow statement. If you look at, operational cash flow, and then you add back our inflows from partnership and transaction costs, you'll be able to get directly to this, guidance number that we're using.

現金營運費用是一個非GAAP指標，我們將用它來為您提供指導。我們的損益表中有很多非現金支出，因此我們希望提供一些信息，以顯示我們未來的現金狀況可能會是什麼樣子。所以，這直接來自我們的現金流量表。如果你看一下營運現金流，然後加上我們從合作關係和交易成本中獲得的資金流入，你就可以直接得到我們正在使用的這個指導數字。

In addition, last year, it was really exciting to see, that we crossed the 500 million milestone in cumulative partner inflows. We expect to continue to achieve those, going forward. And in fact, we hit our first milestone, earlier this month already. And so we do include a probability weighting of some of those milestones in our cash flow projections going forward.

此外，去年我們累計合作夥伴資金流入突破 5 億大關，這真的令人興奮。我們期望未來能夠繼續實現這些目標。事實上，我們本月初就已經達到了第一個里程碑。因此，我們在未來的現金流量預測中，確實對其中一些里程碑事件進行了機率加權。

That's actually the really exciting part for me is not only were we able to exceed our efficiency expectations, but that actually means we need to extend out our cash runaway. And so we're updating our guidance to go to early 2028 as of now.

對我來說，真正令人興奮的是，我們不僅能夠超越預期的效率，而且這意味著我們需要延長我們的現金流。因此，我們目前將指導方針更新至 2028 年初。

And with that, I will hand it back over to Najat.

然後，我就把它還給納賈特。

Thank you so much, Ben. We'll wrap it up by just saying, looking ahead, we have a very broad set of catalysts that are coming up and it's going to be a busy next 18 to 24 months. We'll see if I can recover my voice soon. In terms of, this year, like I said, we're on track for our initial engagement with the FDA on RE 481. We're looking forward to that. And also initial data, early safety, and then also PK for RBM 39.

非常感謝你，本。最後我們想說的是，展望未來，我們將迎來一系列非常廣泛的催化劑，未來 18 到 24 個月將會非常忙碌。看看我能否盡快恢復嗓子。就今年而言，正如我所說，我們正按計劃與 FDA 就 RE 481 進行初步接洽。我們很期待。此外，還有 RBM 39 的初步數據、早期安全性和藥物動力學數據。

And go no go decisions for PI3K and ENPP-1, which are both in I&D enabling.

以及對 PI3K 和 ENPP-1 的是否啟動決定，這兩個計畫都屬於 I&D 賦能領域。

We'll also have additional data for 4,881 early next year, and then combo data expected for our CDK 7 program, as well as more early safety and PK data for MAD1 and LSD1. Recall, for both of those, we designed the assets to be more tolerable, so these are going to be important. And I know the partner catalyst looks like a small box here, but I wish I could physically expand it. Because that's going to be very important. Our partnerships with Sanofi, as we just discussed, in terms of multiple programs as we're progressing into more later-stage development, candidate, and other milestones as well. But in addition to that, these maps where novel biology is really would come from, extracting that into new programs. With Roche, Genentech, etc. So, really important work that continues, and we continue to invest and push the boundaries in terms of our platform, defining what industry and standard really looks like for making medicines using AI and as Ben just mentioned, pairing all of that important work with discipline execution. We've really pivoted towards an outcome-based budget where we test what every dollar would value creation, every dollar can drive, doing more with less. So I'll close by saying thank you so much for the time.

明年年初我們還將獲得 4,881 的更多數據，然後預計會獲得 CDK 7 項目的組合數據，以及 MAD1 和 LSD1 的更多早期安全性和藥物動力學數據。請記住，對於這兩種情況，我們都將資產設計得更容易接受，因此這些資產將非常重要。我知道這裡的催化劑看起來像個小盒子，但我希望我能把它放大。因為那非常重要。正如我們剛才討論的那樣，我們與賽諾菲在多個項目上建立了合作關係，我們正在推進到更後期的開發階段，候選藥物和其他里程碑。但除此之外，這些地圖還能揭示新生物學的真正來源，並將其提取到新的程式中。與羅氏、基因泰克等公司合作。所以，這是一項非常重要的工作，我們將繼續投入資源，不斷拓展平台邊界，定義利用人工智慧製造藥物的行業標準，正如 Ben 剛才提到的，將所有這些重要工作與嚴謹的執行相結合。我們已經真正轉向以結果為導向的預算，我們會測試每一美元能為創造帶來什麼價值，每一美元能推動什麼，用更少的資源做更多的事情。最後，我要衷心感謝您抽出時間。

And also, our focus will always remain on value creation for patients. They're the ones that we ultimately serve, patients are waiting, and also, of course, our shareholders. So, thank you again for listening.

此外，我們將始終專注於為患者創造價值。我們最終服務的是他們，是等待治療的患者，當然還有我們的股東。再次感謝各位的收聽。

And with that, I'm going to pivot to the Q&A section, and I'll also have our CSO Dave Hallett joining us as well, in addition to Ben Taylor, in order to address some of the questions.

接下來，我將進入問答環節，除了 Ben Taylor 之外，我們的首席安全長 Dave Hallett 也將加入我們，回答一些問題。

All right.

好的。

From Sean at Morgan Stanley and Priyanka as well, thank you for the questions from JP Morgan and from Brendan at C. So many people, questions around RC 4,881, understanding what potential registrational pathway may look like upon alignment with the FDA, how we're thinking about providing a regulatory update and updated patient population. It's a long question. I'll break it into pieces. In terms of the regulatory update, as I mentioned, for us, we're on track for that engagement, initial engagement with the FDF first half of 2026. All hands on deck for that. That's going to be really important in terms of discussing the potential design for registrational study, patient.

來自摩根士丹利的肖恩和普里揚卡，感謝摩根大通和C公司的布倫丹提出的問題。許多人對 RC 4,881 有疑問，想了解與 FDA 達成協議後的潛在註冊途徑會是什麼樣子，以及我們正在考慮如何提供監管更新和更新的患者群體資訊。這是一個很長的問題。我會把它拆成碎片。關於監管更新方面，正如我所提到的，我們正按計劃推進與FDF的初步接觸，預計將於2026年上半年開始。全體人員投入行動。這對於討論註冊研究和患者的潛在設計非常重要。

Endpoints, we have a very compelling data set in terms of the durability and then also poll of burden reduction. In addition to that, I didn't cover it today in the interest of time. We also have the natural history data as well. So coupled with that, it's going to be really important for us to have conversations with the FDA. So that's 0.1. 0.2 is also around the updated patient population. So, as I mentioned, 18 and over that arm is already recruiting, as well as we're also looking at dose optimization schedules just given what we saw with our durability data. So more data on that coming, first half of 2027.

端點方面，我們擁有非常有說服力的資料集，包括耐用性以及減輕負擔的調查結果。除此之外，由於時間關係，我今天沒有報道這件事。我們還擁有自然史數據。因此，同時，與美國食品藥物管理局（FDA）進行對話對我們來說就顯得非常重要了。所以是 0.1。0.2 也與更新後的患者群相近。正如我之前提到的，18 歲及以上的受試者已經開始招募，同時，鑑於我們從耐久性數據中看到的情況，我們也在研究劑量優化方案。更多相關數據將於 2027 年上半年公佈。

Look, as we have meaningful updates across both fronts, as you've seen, we've done webinars, ad hoc, we like to be real-time and transparent when we have more meaningful outcomes and updates, we'll absolutely be sharing with the street as well.

你看，正如你所看到的，我們在兩個方面都有重要的進展，我們舉辦了網路研討會，也進行了臨時會議。我們喜歡即時透明地發布訊息，當我們有更有意義的成果和進展時，我們一定會與外界分享。

All right.

好的。

Next question is from Alex from Bank, Alec from Bank of America.

下一個問題來自美國銀行的 Alex，也就是 Alec。

It looks like the cost cutting measures, cost optimization, cost cutting measures really started to take hold in Q4. Any one-offs that helped in the quarter, or are these levels sort of the expectation for the go forward? Ben, do you want to take that?

看來，削減成本、優化成本的措施在第四季真正開始奏效了。本季是否有任何一次性因素起到推動作用，還是說這些水準是未來一段時間的預期水準？本，你想拿嗎？

Sure, happy to. Yeah, thanks, Alex. So, if you think about it, I agree with you that. It's really about efficiency more than cost cutting. So we have hit a point where, we have.

當然可以，我很樂意。謝謝你，Alex。所以，仔細想想，我同意你的看法。其實關鍵在於提高效率，而不是削減成本。所以我們已經到了這樣一個地步。

Gone through all of the integration, I would assume that that is all complete. There's no big one-offs in the system, but what we, really TRY and do is come in with attitude where we want to continue to find ways For every dollar to make more of an impact in the following years and months than it did previously. And so when you come in with that attitude, all of a sudden you start to find ways to do more with less and that's where we'll, we expect to be able to continue growing our pipeline, investing, heavily behind our platform and moving things forward while still hitting those cost targets that we put out there.

完成所有整合工作後，我認為一切都已完成。系統中沒有一次性的重大舉措，但我們真正努力的方向是，希望繼續尋找方法，讓每一美元在未來幾年和幾個月內產生比以前更大的影響。所以，當你抱著這種態度來做事時，你會突然發現如何用更少的資源做更多的事情，而這正是我們期望能夠繼續擴大我們的產品線，大力投資我們的平台，並推動各項工作向前發展，同時還能達到我們設定的成本目標。

Great.

偉大的。

Thank you, Ben. I mean, the only thing I'll add is, Alec, I think it's Piece around rapid go, no go decisions and how we are doing that, just the mentality and the mindset and also understanding and just taking a step back, the variety of areas we're working on and what is the value proposition across the different areas, which is evolved as you generate more data, almost thinking like an investor, I think it's really important, being agile around capital allocation and that's what we will continue to do, of course, being driven by data.

謝謝你，本。我的意思是，我唯一要補充的是，Alec，我認為關鍵在於快速做出是否繼續的決定，以及我們如何做到這一點，關鍵在於心態和思維方式，以及理解和退後一步，審視我們正在進行的各種領域，以及不同領域的價值主張是什麼。隨著數據的積累，價值主張也不斷發展，幾乎要像投資人一樣思考。我認為在資本配置方面保持敏捷非常重要，當然，這也是我們將繼續做的，一切都以數據為驅動。

Great.

偉大的。

Next question. Nvidia, what's the rationale in terms of the divestment? Do you plan to seek other technology partners? Does Nvidia now have proprietary insights from the models you've trained, etc. Etc. Okay.

下一個問題。英偉達，這次資產剝離的理由是什麼？您是否計劃尋找其他技術合作夥伴？Nvidia現在是否掌握了你訓練的模型等的專有資訊？ETC。好的。

I think it's going to be this great question.

我覺得這會是一個很棒的問題。

Thank you so much. It's going to be important to decouple two parts. One is the investment from, Nvidia, and one is our collaboration, our technical collaboration with Nvidia. The technical collaboration with Nvidia continues. I mean, some of you might have just seen. We're going to be highlighted in a lightning round for Nvidia's upcoming GTC presentation with high res, really being the recursion, being a pioneer in how to leverage automation, this wet and dry lab, this is not just words, this is actually in action. This is how we do millions of experiments a week. The other piece is also our collaboration with Nvidia around our Biohive 21 of the fastest supercomputer in life sciences.

太感謝了。將兩個部分解耦至關重要。一是來自英偉達的投資，二是我們與英偉達的技術合作。與英偉達的技術合作仍在持續。我的意思是，你們有些人可能剛剛已經看到了。我們將在英偉達即將舉行的 GTC 大會的閃電演講環節中重點展示高分辨率版本，真正體現遞歸的原理，成為如何利用自動化技術的先驅，這個乾濕實驗室，這不僅僅是說說而已，而是實實在在的行動。我們就是這樣每週進行數百萬次的實驗。另一個成果是我們與英偉達合作開發的 Biohive 21，它是生命科學領域速度最快的超級電腦。

That, I mentioned the examples from PI3K around Sanofi using machine learning, using, molecular dynamics, all of that is underpinned by our supercomputer. Our partnership with Nvidia couldn't be any stronger, so that continues. In terms of the divestment, this really was if you look at the public 13 filings, from Q4 of 2025, is really a shift in Nvidia's investment portfolio to more larger on strategy, supercomputer, data center, etc. Efforts. And so that's really a Investment portfolio shift and we were not the only company. There were other decisions made as well. It's a collective shift from a portfolio to more on strategy investment, large $1 billion dollar plus investments. So those are to be +22 areas to be decoupled. The last thing I'll also say to you also, sorry, there's so many questions, also ask the question, are we seeking other technologies? Partners, we have a strategic partnership with Google as well in terms of cloud compute. We have the partnership, as I mentioned with Nvidia on machine learning and models, etc. But also on-prem, compute, and we will continue, we are, we've always been one of the pioneers in really bridging the world of tech and science, and we'll continue to do that.

我之前提到過 PI3K 在賽諾菲使用機器學習、分子動力學等方面的例子，所有這些都得益於我們的超級電腦。我們與英偉達的合作關係再牢固不過了，因此這種合作關係還會持續下去。就資產剝離而言，如果你查看 2025 年第四季至 2025 年 3 月期間公開的 13 份文件，就會發現這實際上是英偉達投資組合向更大規模的戰略、超級電腦、數據中心等領域的轉變。努力。所以這其實是投資組合的調整，而且我們並不是唯一一家這樣做的公司。此外，也做出了其他一些決定。這是從投資組合轉向更注重策略投資、規模超過 10 億美元的大額投資的集體轉變。所以，這22個區域要脫鉤。最後，我還要對你們說一句，抱歉，問題太多了，也請你們問問，我們是否在尋求其他技術？各位合作夥伴，我們在雲端運算方面也與Google建立了策略合作夥伴關係。正如我之前提到的，我們與英偉達在機器學習和模型等方面建立了合作關係。但我們也關注本地部署、計算，我們將繼續這樣做，我們一直是真正連接科技與科學領域的先驅之一，我們將繼續這樣做。

All right. We'll take one more question here.

好的。我們再回答最後一個問題。

From George's, with the recent positive preliminary efficacy data for RECC 4,881 in FAP and the achievement of your fifth milestone with Sanofi, what specific metrics or historical comparison from your current clinical portfolio best Demonstrate that recursion is improving the probability of clinical success or speed of development compared to traditional discovery methods. I'm going to hand it over, to Dave Hallett to get us started, and, I'm sure we can also add some more comments as well there.

喬治，鑑於 RECC 4,881 在 FAP 中近期取得的積極初步療效數據，以及您與賽諾菲達成的第五個里程碑，您目前的臨床產品組合中哪些具體指標或歷史比較能夠最好地證明，與傳統發現方法相比，遞歸正在提高臨床成功的概率或開發速度。接下來我將把發言權交給戴夫·哈雷特，讓他來為我們開場，我相信我們也可以在那裡補充一些評論。

Thank you, Njat, and kind of good morning and good afternoon to those of us in Europe. .

謝謝你，Njat，也向身處歐洲的我們問好，早安/下午好。。

I think I'll maybe start from the discovery perspective, I think the chat, during the last presentation has highlighted a number of themes, one is about the repeatability of kind of delivery, I think I think that's specifically highlighted, in the in the burgeoning Sanofi kind of pipeline that we're kind of that we're building together, this is a kind of a repeatable platform that's kind of delivering, both best in class and kind of first in class challenging targets, above JP Morgan and again in this presentation I think we've highlighted, the speed of delivery, if you look at the metrics that we're delivering in terms of numbers of novel compounds that we synthesize and test, and the speed that we're getting, to these development candidates, these are, I think, further demonstration that the role kind of technology plays in kind of accelerating that delivery.

我想我或許會從發現的角度開始。我認為上次的演講中，大家討論了很多主題，其中之一是交付的可重複性。我認為這一點在賽諾菲正在蓬勃發展的研發管線中得到了特別強調，我們正在共同建立一個可重複的平台，該平台能夠交付一流的、甚至是首創的具有挑戰性的目標，超越摩根大通。在這次演講中，我們再次強調了交貨速度。如果你看我們交付的指標，例如我們合成和測試的新化合物的數量，以及我們獲得這些候選藥物的速度，我認為這進一步證明了技術在加速交付方面所發揮的作用。

The proof is ultimately in the clinic, and clearly we're very excited, for patients in terms of, FAP.

最終的證明還是在臨床上，顯然我們對此感到非常興奮，這對 FAP 患者來說意義重大。

I think this is the first example from our platform, where we've been able to kind of demonstrate that a compound that came from recursion has shown, clinical proof of concept, and obviously the goal over the coming months and years is to show repeatability in that frame as well.

我認為這是我們平台上的第一個例子，我們已經能夠證明，透過遞歸合成的化合物已經展現出臨床概念驗證，顯然，未來幾個月和幾年的目標是證明該框架的可重複性。

Thank you, Dave. And just to maybe add a little bit of a broader perspective, looking at recursion, 5 plus clinical programs, a diversified portfolio on the clinic side, a diversified portfolio on the discovery side, and in the time and effort it takes to build a platform, I mean, these data sets didn't exist, the models didn't exist, all of that. I just think taking a big step back, we are not a 12 asset biotech, and we are a tech bio for a reason, which is the piece that Dave just mentioned really well, which is what we're really trying to focus on is the repeatability, the scalability, making all of this much more engineeringfoed using Whether it's genetic agents or automations to do things better, faster, taking toil out of the system so we can supercharge our scientists more and more to do the hard work. I just want to emphasize the hard work of drug discovery and development. Drug discovery and development inherently is probabilistic. Most things don't work. We have a 90% failure rate. So we know that multiple shots on goals are, it's going to be important. So that's the kind of fortitude and resilience that's needed in the space and we're adding an area two worlds coming together in tech and bio that haven't really come together before and not just building models that are interesting, but actually applying models that unlock value. And so just to tie it together, we are constantly looking at metrics and stats. The team knows I call it green shoots, whether it is the number of compounds we synthesize, just 90% less than industry, the speed with which the cost of our INDs, we do the same thing in the biology platform, we do the same thing with the clinical development as you saw me share, where we're seeing improvement in enrollment and so forth. We are, there's so much work to be done, but this is what quite frankly gets us excited. It is hard, but incredibly challenging and rewarding work. So thank you all for your support to our partners. To our shareholders, but most importantly, to patients that are willing to take a bet on us in our programs and that are waiting, and we are working as hard as possible to really, forge a new era of how medicines are made for patients that are waiting.

謝謝你，戴夫。再從更廣闊的視角來看，考慮到遞歸、5 個以上的臨床項目、臨床方面的多元化投資組合、發現方面的多元化投資組合，以及構建平台所需的時間和精力，我的意思是，這些數據集並不存在，這些模型也不存在，所有這些。我認為，退一步講，我們不是一家擁有 12 項資產的生物技術公司，我們是一家技術生物公司是有原因的，正如 Dave 剛才提到的，我們真正想要關注的是可重複性、可擴展性，利用基因製劑或自動化技術使所有這些工作更加工程化，從而做得更好、更快，減少系統中的繁瑣工作，以便我們能夠艱苦地激勵我們的科學家。我只想強調藥物發現和研發過程中所付出的艱辛努力。藥物發現和開發本質上是機率性的。大多數事情都行不通。我們的失敗率高達90%。所以我們知道多次射門得分非常重要。所以，這就是這個領域需要的堅韌和韌性，我們正在創造一個領域，將科技和生物這兩個以前從未真正融合在一起的領域結合起來，不僅要建立有趣的模型，還要實際應用能夠釋放價值的模型。因此，總而言之，我們一直在關注各項指標和統計數據。團隊知道我稱之為“萌芽”，無論是我們合成的化合物數量（比行業內少 90%），還是 IND 成本的降低速度，我們在生物平台上也做了同樣的事情，正如你看到我分享的那樣，我們在臨床開發方面也做了同樣的事情，我們看到了入組人數等方面的改善。的確，還有很多工作要做，但坦白說，正是這一點讓我們感到興奮。這份工作很辛苦，但極具挑戰性，也很有成就感。感謝大家對我們合作夥伴的支持。致我們的股東，但最重要的是，致那些願意相信我們、支持我們計畫並正在等待的患者，我們正在竭盡全力，真正為等待的患者開創藥物研發的新時代。

Thank you again for joining us today, and we look forward to sharing more updates in the coming months.

再次感謝您今天的參與，我們期待在接下來的幾個月與您分享更多最新消息。