Recursion Pharmaceuticals Inc (RXRX) 2025 Q2 法說會逐字稿

Hello. Welcome, everybody, to Recursion's Q2 2025 earnings call.

你好。歡迎大家參加 Recursion 2025 年第二季財報電話會議。

My name is Chris Gibson. I'm the Co-Founder and CEO of Recursion. I'm excited to share with you today some of the latest updates on our company, as we drive forward to decode biology.

我叫克里斯·吉布森。我是 Recursion 的共同創辦人兼執行長。今天我很高興與大家分享我們公司的一些最新動態，我們正在努力破解生物學的奧秘。

We've been talking for the last nine months since the business combination with Exscientia about the Recursion OS 2.0. I want to start there today and tell you a little bit about the way that we're bringing together the incredible components from both Exscientia and Recursion and building new components of the OS in order to drive forward our mission.

自從與 Exscientia 合併以來，我們一直在討論 Recursion OS 2.0 的開發。今天我想從這裡開始，簡單介紹一下我們如何整合 Exscientia 和 Recursion 的優秀元件，並建立新的作業系統元件，以推進我們的使命。

At Recursion, we base everything off of proprietary fit-for-purpose data, whether it's data we generate in-house or data that we pull from partners. We're not just generating data to help discover targets or to help translate programs or to help with clinical trials, we're building a true end-to-end capability, from target discovery all the way through to clinical trial simulation. We're really, really excited about the way all of these pieces fit together and add to each other.

在 Recursion，我們的一切都以專有的適用數據為基礎，無論是我們內部生成的數據還是從合作夥伴那裡獲取的數據。我們不僅僅是產生數據來幫助發現目標、幫助翻譯程序或幫助臨床試驗，我們還在建立真正的端到端能力，從目標發現一直到臨床試驗模擬。我們真的非常興奮於所有這些部分組合在一起並相互補充的方式。

Everything we do at Recursion is based on iterative cycles of learning. Much of our work based on iterative cycles are dry lab predictions and wet lab validations. I want to talk about a few of the pieces of the Recursion OS that we really, really leaned into in the last quarter.

我們在 Recursion 所做的一切都是基於迭代學習週期。我們基於迭代循環的大部分工作都是乾實驗室預測和濕實驗室驗證。我想談談我們在上個季度真正關注的 Recursion OS 的幾個部分。

I'm going to start off with talking about Boltz-2. This was a really exciting partnership with both MIT and Nvidia, where we were able to help lead the field of protein folding; and lead the field of protein ligand binding predictions, with this work that we did with MIT.

我首先要談的是 Boltz-2。這是與麻省理工學院和 Nvidia 的一次非常令人興奮的合作，我們能夠幫助引領蛋白質折疊領域；並透過與麻省理工學院合作的這項工作引領蛋白質配體結合預測領域。

We were able to actually open source this project. To date, there have been almost 200,000 downloads and almost 50,000 unique users. What I think is most exciting -- what's gotten the most traction -- about this work is that we were able to actually make binding predictions that are approaching the level of efficiency and the level of efficacy of free energy perturbation calculations but we're able to do this with about 1,000-fold less compute.

我們實際上能夠開源這個專案。迄今為止，下載量已接近 20 萬次，獨立用戶接近 5 萬名。我認為這項工作最令人興奮的地方——也是最受關注的地方——是我們能夠真正做出具有約束力的預測，其效率和功效水平接近自由能擾動計算，但我們能夠用少約 1,000 倍的計算量做到這一點。

That is really, really, really exciting. It means that a lot of the real bespoke work that was done with physics-based computing could actually be done in a screening format. While there's more work to do in this space by us and many others, we are very excited about the way this tool and tools like this are going to be able to drive the field forward.

這真的真的真的很令人興奮。這意味著許多基於物理的計算完成的真正客製化工作實際上可以以篩選形式完成。雖然我們和其他許多人在這個領域還有很多工作要做，但我們對這個工具和類似的工具能夠推動這個領域的發展感到非常興奮。

What's more? We've already built this technology into the Recursion OS; and even, improvements on this technology into the Recursion OS.

還有什麼？我們已經將這項技術融入 Recursion OS 中；甚至，也將這項技術的改進融入 Recursion OS 中。

Another area we've been talking about for the last year has been our ClinTech platform. This is something that we are now deploying against every single one of our programs at Recursion. There's multiple components to this.

去年我們一直在談論的另一個領域是我們的 ClinTech 平台。我們現在正在針對 Recursion 的每一個程式部署此功能。這其中有多個組成部分。

The first is our causal AI applied to human genomics. This is really exciting. We're taking patient data that we get from Helix and Tempus. We're combining that with our perturbation biology data and algorithms from Recursion to help to connect our platform to patients. This is enabling us to identify targets, to stratify patients, and even to do indication expansion.

首先是我們將因果人工智慧應用於人類基因體學。這真是令人興奮。我們正在獲取來自 Helix 和 Tempus 的患者數據。我們將其與 Recursion 的擾動生物學數據和演算法相結合，以幫助將我們的平台與患者聯繫起來。這使我們能夠確定目標、對患者進行分層，甚至擴大適應症。

We've also started to design and simulate our clinical trials at Recursion using in-house software that we've been building. This is allowing us to potentially improve the optimal dose for 30% more patients. This is really, really exciting. Again, we are now deploying this against our programs at Recursion.

我們也開始使用我們一直在建立的內部軟體來設計和模擬 Recursion 的臨床試驗。這使我們能夠為 30% 以上的患者改善最佳劑量。這真的非常令人興奮。再次，我們現在正在針對 Recursion 的程式部署它。

And third, we're now using our AI, not just to identify patients, not just to design our clinical trials, but actually to recruit and execute. The operations side of our clinical trials is really, really important, as well. With the new software that we've built and the partnerships we've built in this space, we now have the potential for 50% faster enrollment projections at high-quality sites. This means we can activate trials up to two months faster.

第三，我們現在使用人工智慧，不僅是為了識別患者，不僅是為了設計臨床試驗，而且實際上還用於招募和執行。我們的臨床試驗的操作方面也非常非常重要。憑藉我們開發的新軟體以及我們在該領域建立的合作夥伴關係，我們現在有可能將高品質站點的招生預測速度提高 50%。這意味著我們可以提前兩個月啟動試驗。

Again, this is the early days of our ClinTech platform. But what I'm most excited about is that we're already deploying these tools against the programs in our pipeline. We'll be deploying these against new programs in our pipeline soon. Najat is going to be able to tell you more about that in a few minutes.

再次強調，這是我們的 ClinTech 平台的早期階段。但最讓我興奮的是，我們已經針對我們管道中的程式部署了這些工具。我們很快就會針對我們管道中的新程式部署這些功能。幾分鐘後，Najat 將會向您詳細介紹這一點。

We continue to advance a pipeline of both internal programs in oncology and rare disease, as well as a suite of R&D collaborations and programs with our partners at Roche, Sanofi, Bayer, and Merck KgaA. We're really, really excited about all of these programs today.

我們繼續推動腫瘤學和罕見疾病領域的內部項目，以及與羅氏、賽諾菲、拜耳和默克集團等合作夥伴進行的一系列研發合作和項目。我們對今天的所有這些節目感到非常非常興奮。

But what I think is most excited isn't any one of the programs, it's the platform we're building and these leading indicators, where we're demonstrating that we can bring medicines to the clinic faster and at lower cost. Ultimately, these leading indicators are things that we believe, over time, are going to continue to improve. We're going to be able to continue to raise a high bar of quality on our programs and drive them forward at real scale.

但我認為最令人興奮的不是任何一個項目，而是我們正在建立的平台和這些領先指標，我們正在證明我們可以更快、更低成本地將藥物帶到診所。最終，我們相信，隨著時間的推移，這些領先指標將會持續改善。我們將能夠繼續提高我們專案的品質標準，並推動它們在實際規模上向前發展。

To tell you more about the way we're building momentum, let me turn it over to our Chief R&D and Chief Commercial Officer, Najat Khan. Najat?

為了向您詳細介紹我們建立勢頭的方式，請允許我將話題交給我們的首席研發和首席商務官 Najat Khan。納賈特？

Thanks, Chris. Great to be here today. Let's dive into this.

謝謝，克里斯。很高興今天來到這裡。讓我們深入探討一下。

Chris mentioned the suite of partnerships and partner discovery programs and internal programs that we are progressing.

克里斯提到了我們正在推進的一系列合作夥伴關係、合作夥伴發現計劃和內部計劃。

A couple of things to note. On the internal side, you can see there are six or so programs that are going through really, really important inflection points, both across oncology and rare diseases. What I'll do today is double click a bit more on a couple of our more late-stage or later-stage oncology programs: CDK7, monotherapy dose escalation, as well as the initiation of our expansion cohort/combination arm and RBM39. We'll share a little bit more around the biomarker enriched, the solid tumors, the patient populations, et cetera; and how we leverage our platform insights in order to hone in on where we go.

有幾件事需要注意。從內部來看，你可以看到大約有六個項目正在經歷非常非常重要的轉折點，涉及腫瘤學和罕見疾病。我今天要做的是進一步介紹我們的幾個更晚期或更晚階段的腫瘤學項目：CDK7、單一療法劑量遞增，以及我們的擴展隊列/組合組和 RBM39 的啟動。我們將分享更多關於生物標記富集、實體腫瘤、患者群體等方面的資訊；以及我們如何利用我們的平台洞察力來磨練我們的發展方向。

On the partnership front -- I get this question a lot so I just wanted to step back for a second and share --across our partnerships, there's two major areas of value creation. The first is really around what Chris mentioned in the beginning: proprietary fit-for-purpose data sets that we're co-developing with our partners. So an example of this is, of course, the phenomap -- the first neuronal phenomap -- IPSC-derived with Roche and Genentech.

在合作方面——我經常被問到這個問題，所以我想稍微回顧一下並分享——在我們的合作中，有兩個主要的價值創造領域。第一個實際上是圍繞著克里斯一開始提到的內容：我們與合作夥伴共同開發的專有適用數據集。當然，一個例子就是表型圖——第一個神經元表型圖——由羅氏和基因泰克共同開發的 IPSC。

The other area of value creation is around partner programs, where we are designing using our AI modules on the chemistry side very challenging first-in-class, best-in-class programs. Just recently, we achieved a fourth milestone in our Sanofi partnership. More to come on that.

價值創造的另一個領域是合作夥伴計劃，我們正在使用化學方面的人工智慧模組設計非常具有挑戰性的一流、一流的計劃。就在最近，我們與賽諾菲的合作取得了第四個里程碑。對此還有更多內容。

Just going to the next slide. I'm just going to take a second to do a quick snapshot on the overall programs that we have in our internal portfolio; and then, I'll go a bit more into CDK7 and RBM39.

直接進入下一張投影片。我將花一點時間快速瀏覽我們內部投資組合中的整體項目；然後，我將更深入地介紹 CDK7 和 RBM39。

Just as a quick reminder, CDK7, really important target. The focus really is leveraging our AI-powered design module in our Recursion OS platform to optimize the therapeutic index. This is a target that has been tried by others before. So that's the area of focus.

簡單提醒一下，CDK7 確實是個重要的目標。我們的重點是利用 Recursion OS 平台中的 AI 設計模組來優化治療指數。這是其他人以前嘗試過的目標。這就是重點關注的領域。

We should have more monotherapy dose escalation data by the end of this year. As I mentioned, combination-initiated. RBM39, this is an example actually identified using our phenomap, where we identified a new MOA with synthetic lethal targeting opportunities in genomically unstable cancers. More on that. First half of 2026, we anticipate some initial data from our monotherapy dose escalation.

到今年年底，我們應該會獲得更多單一療法劑量遞增的數據。正如我所提到的，組合發起。RBM39，這是使用我們的表型圖實際識別的一個例子，我們在其中識別出一種新的 MOA，具有在基因組不穩定的癌症中進行合成致死靶向的機會。更多關於此內容。2026 年上半年，我們預計將獲得單藥治療劑量增加的一些初步數據。

You heard a little bit about the MEK1/2 in our FAP program. I just want to highlight, this is again another phenotypic insight, where we actually derive the fact that there's a connection -- an important relationship -- in an unbiased fashion between MEK1/2 and the relationship with MAP kinase pathway -- signaling pathway -- and APC and WNT signaling pathway; which disease, this is for FAP. So again, we should expect more data beyond the initial cut we shared earlier this year; second half of 2025; end of this year.

您在我們的 FAP 計劃中聽到了一些有關 MEK1/2 的資訊。我只是想強調，這又是另一種表型見解，我們實際上得出了一個事實，即 MEK1/2 與 MAP 激酶通路（信號通路）以及 APC 和 WNT 信號通路之間存在聯繫——一種重要的關係；對於哪種疾病來說，這是 FAP。因此，我們應該期待在今年稍早分享的初步數據之外獲得更多數據；2025 年下半年；今年年底。

MALT1. This is another program where now, we're using and leveraging our AI-powered chemistry design portion of the Recursion OS platform, again, to lower the liability that's associated with UGT1A1 inhibition. That's also in monotherapy dose escalation.

MALT1。這是另一個程序，現在我們正在使用和利用 Recursion OS 平台的 AI 驅動化學設計部分，再次降低與 UGT1A1 抑制相關的責任。這也是單一療法劑量的增加。

To round it out, we also have a couple of preclinical programs here that are going through important inflection points in the development candidate/IND-enabling phase.

總的來說，我們還有幾個臨床前計畫正在經歷開發候選/IND 支持階段的重要轉捩點。

But a lot of these programs -- and we talked about this before -- we're really focused on the earlier versions of the Recursion OS platform. As we iterate and learn and add more components to our Recursion OS platforms, we expect the next wave of programs to be even more high potential and potential to do it in a more efficient way.

但對於許多這樣的程式——我們之前討論過這個問題——我們真正關注的是 Recursion OS 平台的早期版本。隨著我們不斷迭代、學習並向我們的 Recursion OS 平台添加更多元件，我們預計下一波程式將具有更高的潛力，並且有可能以更有效的方式實現。

But I wanted to take you a little bit under the hood of what's actually in the Recursion OS, especially the 2.0 platform, following the integration with Exscientia. If you just look to the left-hand side, we first start with the AI-powered biological insights. This is where we are actually driving novel targets. This is from multi-omic data, whether it be genomic, transcriptomics, et cetera, connecting that early on with the patient.

但我想帶您稍微了解一下 Recursion OS 的實際情況，特別是與 Exscientia 整合後的 2.0 平台。如果你只看左側，我們先從人工智慧驅動的生物洞察開始。這實際上是我們追求新目標的地方。這是來自多組學數據，無論是基因組、轉錄組學等等，儘早將其與患者聯繫起來。

This is the ML-based patient connectivity data layer that's really important to the data sets such as from Tempus, Helix, and others, ensuring that we can actually take these biological insights and deconvolute the MOA; and very early on, do a screening approach around triaging what are some of the binding affinities early on. So this is where approaches such as Boltz-2 that Chris mentioned earlier is already being incorporated into our workflow.

這是基於機器學習的患者連接資料層，對於 Tempus、Helix 等資料集而言非常重要，確保我們能夠真正掌握這些生物學見解並解開 MOA；並且在早期就採取篩選方法，對早期的一些結合親和力進行分類。這就是 Chris 之前提到的 Boltz-2 等方法已經融入我們的工作流程中的地方。

In addition to that, we're also developing proprietary algorithms in-house. So as soon as we put this on a slide, I have to say it gets outdated because there's so much rapid iteration and work that's happening.

除此之外，我們還在內部開發專有演算法。因此，一旦我們將其放在幻燈片上，我不得不說它已經過時了，因為正在發生太多的快速迭代和工作。

In the middle, AI-enabled precision design. This is where we're designing our molecules, really optimizing both for novel scaffolds. This is where we use Generative AI approaches and also, active learning in order to optimize drug-like properties. This also includes using QMMD approaches, which is a 3D protein and animistic models.

中間是人工智慧支援的精準設計。這是我們設計分子的地方，真正針對新型支架進行最佳化。在這裡，我們使用生成式人工智慧方法和主動學習來優化類藥物特性。這也包括使用 QMMD 方法，這是一種 3D 蛋白質和萬物有靈模型。

One important point here is the wet and dry lab integration that we have. This is where aspects around automated chemistry, automated biology, and automated ADMET becomes incredibly important so we can design out certain elements earlier, faster to ensure that we have better molecules out of discovery.

這裡的一個重要點是我們擁有乾濕實驗室整合。這就是自動化化學、自動化生物學和自動化 ADMET 方面變得極其重要的地方，這樣我們就可以更早、更快地設計出某些元素，以確保我們發現更好的分子。

Last but certainly not the least -- and one that's close to my heart -- is ensuring that we do this also in clinical development. Chris touched on this in terms of some of the areas that we're building out. You'll see some of the examples we're using in our current programs already around causal inference on patient stratification and also, smarter trials and faster recruitment.

最後但同樣重要的一點——也是我最關心的一點——是確保我們在臨床開發中也能做到這一點。克里斯談到了我們正在建立的一些領域。您將看到我們在當前程序中使用的一些示例，這些示例已經圍繞患者分層的因果推斷以及更聰明的試驗和更快的招募。

As I go through each of the programs, I will actually highlight which area of the Recursion OS module and platform we are integrating and actually highlighted insights for our program.

當我瀏覽每個程式時，我實際上會突出顯示我們正在整合的 Recursion OS 模組和平台的哪個區域，並實際上突出顯示我們程式的見解。

Let's start with RBM39. In this program, as I mentioned earlier, the focus was really around leveraging our maps of biology. Just as a reminder for everyone, starting on the left-hand side, we start with these really large maps of biology, whole genome CRISPR knockouts; and then, we profile compounds that are proprietary to us in order to get better understanding of the initial chemical substrates that might actually modulate the biological insight that we have identified.

讓我們從 RBM39 開始。正如我之前提到的，在這個專案中，重點實際上是利用我們的生物學圖譜。提醒一下大家，從左側開始，我們從這些非常大的生物學圖譜、全基因組 CRISPR 敲除開始；然後，我們對專有的化合物進行分析，以便更好地了解可能實際上調節我們已經確定的生物學洞察力的初始化學底物。

The example here is how we identified RBM39, which phenomimics CDK12. CDK12 -- and this is to the panel to your right-hand side -- has been an attractive target in oncology, right, for its role in DDR modulation but generally has suffered from challenges in selectivity because of how homologous CDK13 is.

這裡的例子是我們如何辨識與 CDK12 表型相似的 RBM39。CDK12（這是您右側的面板）因其在 DDR 調節中的作用而成為腫瘤學中一個有吸引力的目標，但是由於 CDK13 的同源性，通常在選擇性方面受到挑戰。

Leveraging our phenomaps, we actually identified that RBM39 is similar -- phenotypically, at least -- to CDK12 and not to CDK13. That was the first insight.

利用我們的表型圖，我們實際上發現 RBM39 至少在表型上與 CDK12 相似，而與 CDK13 不相似。這是第一個見解。

The second insight was the fact that we were able to develop molecular glues and degraders for RBM39, which you'll see in a moment that also phenotypically mimics CDK12. This was our first inkling that RBM39 inhibitors or degraders could potentially provide a druggable potential analog.

第二個見解是，我們能夠開發 RBM39 的分子膠和降解劑，您稍後會看到，它們也在表型上模仿 CDK12。這是我們第一次意識到 RBM39 抑制劑或降解劑可能提供可用藥物的潛在類似物。

And then, I want to say something else that doesn't get talked about enough, which is if you look in the middle panel, we also look not just for CDK12 or CDK13 but we look more extensively across the map to see, is there well-established dependencies that are known of already, biologically; that are also being validated? An example here is a CDK12 and cyclin E -- cyclin K's similar phenomic readout.

然後，我想說一些還沒有被充分討論的事情，那就是如果你看中間的面板，我們不僅會查看 CDK12 或 CDK13，而且我們會更廣泛地查看整個圖譜，看看是否存在已知的、生物學上已經確定的依賴關係；這些依賴關係也正在被驗證？這裡的一個例子是 CDK12 和細胞週期蛋白 E - 細胞週期蛋白 K 的相似表型讀數。

But this is just a small detail in the entirety of the map that we look at. If you go to the next slide, this is another expansion of that same map. What we see here that's actually quite intriguing is in the center in the black box, as what I was referring to in the earlier slide, which is the RBM39 and the degrader itself and some of the associations that we see with CDK12, CDK13, and so forth; but you look broader, you also see associations, mechanistically, in DNA damage repair, epigenetic regulation, cell cycle control, and transcription. This, when you look at it from an MOA perspective, which I'll turn on next, actually intuitively make sense.

但這只是我們所看到的整個地圖的一個小細節。如果您翻到下一張投影片，這是同一張地圖的另一個擴充。我們在這裡看到的實際上非常有趣的東西是在黑框的中心，正如我在之前的幻燈片中提到的那樣，它是 RBM39 和降解劑本身，以及我們看到的與 CDK12、CDK13 等的一些關聯；但如果你看得更廣泛，你還會看到 DNA 損傷修復、表觀遺傳調控、細胞週期控制和轉錄在機制上的關聯。當你從 MOA 角度來看這一點時（我接下來會談到這一點），實際上直觀上是有道理的。

RBM39, if we go to the next slide, is important for splicing fidelity. Degradation of RBM39 leads to splicing defects. Now, if you combine that with tumors that are already genomically unstable, whether it's because of DNA repair pathway vulnerabilities or transcriptional regulation, then that can actually increase the amount of instability, leading to potential apoptosis and cell death. So I just want to share with you how an insight is then triangulated with understanding of mechanism of action.

如果我們看下一張投影片，RBM39 對於剪接保真度很重要。RBM39 的降解會導致剪接缺陷。現在，如果將其與基因組不穩定的腫瘤結合起來，無論是由於 DNA 修復途徑的脆弱性還是轉錄調控，那麼實際上都會增加不穩定性，導致潛在的細胞凋亡和細胞死亡。因此，我只想與你們分享如何將洞察力與作用機制的理解結合起來。

But that's not enough. If we go to the next slide, in addition to that, we also looked at in vitro and in vivo work. Starting with -- look, when we look at the broader patient population, just given the connectivity across the maps that I noted, for replication stress, tumors that suffer from epigenetic dysregulation, cell cycle alterations, or oncogenic drivers are relevant, as well as those tumors that have DDR effects -- so both of those.

但這還不夠。如果我們看下一張投影片，除此之外，我們也研究了體外和體內工作。首先 — — 當我們觀察更廣泛的患者群體時，考慮到我所指出的地圖之間的連通性，對於複製壓力，遭受表觀遺傳失調、細胞週期改變或致癌驅動因素的腫瘤是相關的，以及具有 DDR 效應的腫瘤 — — 所以這兩者都是相關的。

That spans several solid tumors, from colorectal, breast, et cetera; along with some pretty clinically actionable alterations that we'll be studying and looking into more such as MSI high, MYC amplification, et cetera.

這涵蓋了幾種實體腫瘤，包括大腸直腸癌、乳腺癌等；以及一些我們將要研究和調查的具有相當臨床可行性的改變，例如 MSI 高、MYC 擴增等。

But we wanted to look at the in silico understanding and triangulate that with in vitro and in vivo work. If you look at the in vitro cell lines, you clearly see that RBM39 degrader -- so REC-1245, in this case -- there is greater sensitivity in cell lines that have higher replication stress versus cell lines that don't have higher replication stress. This was a good early signal for us. If you go to the next slide, we see a similar trend hold in in vivo as well, where you see a reduction in tumor volume across different tumor types that actually have high replication stress signatures.

但我們想透過電腦模擬來了解情況，並將其與體外和體內研究進行三角測量。如果你觀察體外細胞系，你會清楚地看到 RBM39 降解劑（在本例中為 REC-1245）在具有較高複製壓力的細胞系中比在具有較低複製壓力的細胞系中具有更高的敏感性。這對我們來說是一個很好的早期信號。如果您看下一張投影片，我們會看到體內也存在類似的趨勢，您會看到實際上具有高複製壓力特徵的不同腫瘤類型的腫瘤體積有所減少。

This helps us to two things. Number 1, better understand the importance of RBM39 as a first-in-class target in solid tumors. Second, also give us a better sense in terms of which patient population, tumor segments, et cetera, might be relevant for us to target.

這對我們有兩個幫助。第一，更能理解 RBM39 作為實體瘤一流標靶的重要性。其次，也能讓我們更了解哪些患者族群、腫瘤部位等可能與我們的目標相關。

If you go to the next slide, we went a step further than that. We also wanted to look at the totality of it. You have the Recursion OS inside -- definitely, the preclinical data that I mentioned -- but also looking into mechanistic validation in the middle panel.

如果您翻到下一張投影片，我們更進一步。我們也想看看它的整體狀況。裡面有 Recursion OS——當然，還有我提到的臨床前數據——但也在研究中間面板中的機械驗證。

We see two things here. First, the [d max] is approaching almost 100% in RBM39 degradation, with quite potent D50 numbers as well -- so rapid and potent RBM39 degradation.

我們在這裡看到兩件事。首先，RBM39 降解的 [d max] 接近 100%，D50 數值也相當強——因此 RBM39 降解迅速且強效。

Now, I wanted to go even a step further, if we go to the next slide, which is -- if you go one slide before, please. Okay. That's okay.

現在，我想更進一步，如果我們轉到下一張投影片，也就是 - 如果您轉到前一張投影片，請。好的。沒關係。

If we go to the next slide, this has actually helped us inform what our dose escalation and our combination arm is going to be. For RBM39, monotherapy dose escalation. But in terms of the cancers that we're looking after or going after, it's endometrial, ovarian, et cetera -- cancers with high genomic instability. We will also be focusing on some of these biomarker-enriched populations such as MSI-high.

如果我們看下一張投影片，這實際上幫助我們了解我們的劑量增加和我們的組合組將會是什麼。對於 RBM39，單一療法劑量增加。但就我們正在治療或追蹤的癌症而言，是子宮內膜癌、卵巢癌等——基因組不穩定性較高的癌症。我們還將重點放在一些富含生物標誌物的人群，例如 MSI-high。

Again, first-patient dose, patients are enrolling in this study. We should have early safety and PK data from this monotherapy trial in the first half of 2026.

再次，首例患者劑量，患者正在參與這項研究。我們應該在 2026 年上半年獲得此單一療法試驗的早期安全性和 PK 數據。

Now, we'll go to CDK7, which is our next program. Here, we actually leverage two components of our Recursion OS platform: first, focused on designing a molecule that can really optimize for the therapeutic index; second, leveraging some of our ClinTech approaches in order to hone in on which patient population and which combination arm we will hone in on.

現在，我們將轉到 CDK7，這是我們的下一個程式。在這裡，我們實際上利用了 Recursion OS 平台的兩個組件：首先，專注於設計一種能夠真正優化治療指數的分子；其次，利用我們的一些 ClinTech 方法來確定我們將針對哪些患者群體和哪種組合方案。

Let's go to the next slide. Okay. Just a quick reminder in terms of how the molecule was designed. A couple of things to note here. CDK7 has been an important target for some time, as well. It is a master regulator, both cell cycle progression as well as transcription.

我們來看下一張投影片。好的。只是快速回顧一下分子是如何設計的。這裡有幾點要注意。一段時間以來，CDK7 也一直是個重要的目標。它是細胞週期進程和轉錄的主要調節因子。

But one of the challenges that other compounds have seen so far is challenges with permeability, efflux, and not rapid absorption. So we want to change that around. We use Generative AI models to actually design new scaffolds and -- I think this part is really important, which is leveraging active learning and experimental ADMET data to quickly learn, iterate, and optimize the molecules to reduce the components that we wanted to design out, such as ensure that there's high permeability, rapid absorption, and low efflux.

但迄今為止其他化合物面臨的挑戰之一是滲透性、外排性和不快速吸收的挑戰。所以我們想改變這種狀況。我們使用生成式人工智慧模型來實際設計新的支架——我認為這部分非常重要，即利用主動學習和實驗性 ADMET 數據來快速學習、迭代和優化分子，以減少我們想要設計的組件，例如確保高滲透性、快速吸收和低流出。

Similar to RBM39 degrader, which was done in a very short amount of time -- 18 months from start to IND-enabling, with about 200 compounds or so synthesized -- in this case, you also see about 136 compound synthesized and getting to candidate ID in less than 12 months.

與 RBM39 降解劑類似，其在很短的時間內完成 - 從開始到 IND 授權僅用了 18 個月，合成了大約 200 種化合物 - 在這種情況下，您還可以看到合成了大約 136 種化合物並在不到 12 個月的時間內獲得了候選 ID。

Now, one of the components for designing high permeability, rapid absorption, and low efflux was to ensure that we would have sufficient exposures. You see that on the right-hand side panel: Both 10-milligram QD, 20-milligram QD clearing the IC80 line. When we actually look at versus some of the peers, it's an order of magnitude higher than the exposure that they're seeing.

現在，設計高滲透性、快速吸收和低流出的要素之一是確保我們有足夠的暴露。您可以在右側面板上看到：10 毫克 QD、20 毫克 QD 均清除 IC80 線。當我們真正與一些同行進行比較時，我們發現它比他們所看到的曝光度高出一個數量級。

As of November/December 2024 data cutoff, the compound showed one confirmed PR in ovarian cancer, as well as multiple cases of stable disease -- so far, with a favorable safety profile and no MTD reached.

截至 2024 年 11 月/12 月數據截止，該化合物在卵巢癌中顯示出一例確診的 PR，以及多例病情穩定的情況——到目前為止，具有良好的安全性，且未達到 MTD。

If we go to the next slide, what we have done since then is to really design which combination arm we will focus on. The one that we're going to focus on that we have announced today is second-line plus platinum-resistant ovarian cancer.

如果我們看下一張投影片，從那時起我們所做的就是真正設計我們將專注於哪個組合臂。我們今天要重點討論的是二線加鉑類抗藥性卵巢癌。

How do we get to that? First, we looked at preclinical data. Cell panels, in vivo you see in ovarian, both of them are sensitive to CDK17 and that there are multiple panels that were done.

我們怎樣才能實現這個目標？首先，我們查看了臨床前數據。細胞面板，在體內您在卵巢中看到的，它們都對 CDK17 敏感，並且已經完成了多個面板。

And then, in addition to that, as part of our ClinTech approach, we also use causal inference using some of this multi-omic and clinical data. This was very important to better understand the cause and effect factors here. What we see is that a higher expression of ovarian cancer based on this data is associated with lower overall or worse overall survival. This was based on about 32,000 patient records.

除此之外，作為我們 ClinTech 方法的一部分，我們也使用一些多組學和臨床數據進行因果推論。這對於更好地理解這裡的因果因素非常重要。根據這些數據，我們發現卵巢癌的表達越高，整體存活率就越低或越差。這是基於大約 32,000 份患者記錄得出的。

This gave the totality of the evidence from preclinical. Also, some of what we see in our early clinical data so far, combined with some of this causal inference work, gave us more confidence in terms of the first indication that we would go after, where there is significant unmet need in second-line plus platinum-resistant ovarian cancer.

這給了臨床前的全部證據。此外，到目前為止，我們在早期臨床數據中看到的一些情況，結合一些因果推論工作，讓我們對我們將要追求的第一個跡象更有信心，即二線加鉑抗藥性卵巢癌存在大量未滿足的需求。

If you go to the next slide, site selection and activation is in progress right now for the combination arm. The standard of care includes single-agent chemotherapy; beva plus chemotherapy; and in some cases, PARP inhibitors. In addition to that, the monotherapy arm is ongoing. We anticipate more data from that later on this year.

如果您翻到下一張投影片，您會發現組合臂的網站選擇和啟動現在正在進行中。標準治療包括單藥化療；貝伐單抗合併化療；在某些情況下還包括 PARP 抑制劑。除此之外，單一療法的研究仍在進行中。我們預計今年稍後將會獲得更多數據。

If we go to the next slide, I'll also share a bit more about some of our partnered discovery programs. Next slide, please. Great.

如果我們進入下一張投影片，我還將分享一些有關我們的一些合作發現計劃的資訊。請看下一張投影片。偉大的。

If you look at Sanofi as an example, I just mentioned that we have our fourth program milestone achieved in the last 18 months. I just want to take a moment to say that some of these programs, both in immunology and oncology, first-in-class, best-in-class, some of the milestones that we're going through include important milestones in discovery (inaudible), lead series, development candidate, and so forth. We have several programs advancing to those milestones, including development candidate in the next 12 to 15 months. This effort leverages what you saw in the Recursion OS platform a lot of the AI-powered chemistry design module.

以賽諾菲為例，我剛才提到，我們在過去 18 個月內實現了第四個項目里程碑。我只想花點時間說一下，這些項目中的一些，無論是在免疫學還是腫瘤學領域，都是一流的、一流的，我們正在經歷的一些里程碑包括發現（聽不清楚）、領先系列、開發候選人等方面的重要里程碑。我們有幾個項目正在向這些里程碑邁進，包括未來 12 到 15 個月的開發候選項目。這項工作充分利用了 Recursion OS 平台中大量基於人工智慧的化學設計模組。

In terms of Roche, 5 phenomaps built, to date. You saw an example for RBM39, how we use some of our phenomaps. These are specific for the neuroscience and GI-Onc space. For the neuroscience, one that we delivered last year, over 1 trillion IPSC-derived cells use, whole genome knockout; and also, other perturbations in terms of overexpression. So you're really getting a very holistic understanding of biological pathways and a lot of work in progress there in order to take those insights and translate them into novel programs. More to come on that.

就羅氏而言，迄今已建構了 5 個現象圖。您看到了 RBM39 的範例，以了解我們如何使用一些現象圖。這些是針對神經科學和胃腸道腫瘤領域的。對於神經科學，我們去年發布的一項研究使用了超過 1 兆個 IPSC 衍生細胞，全基因組敲除；此外，還有過度表達方面的其他擾動。因此，您實際上對生物途徑有了非常全面的了解，並且正在進行大量工作，以便將這些見解轉化為新穎的程序。對此還有更多內容。

And then, also on the GI-Onc indication, over four maps already developed there; and already, one program that has been auctioned and more work happening.

然後，同樣在 GI-Onc 指示上，已經開發了四張地圖；並且已經拍賣了一個項目，並且還有更多的工作正在進行中。

I think one point to note here, it's a real pleasure and honor to partner with partners such as Roche, Sanofi, Bayer, and Merck KgaA, where we bring the best of our capabilities: the Recursion OS, the Recursion drug hunter expertise, and the platform tech expertise, along with the deep biology expertise and chemistry expertise in Genentech, Sanofi, and others.

我認為這裡有一點需要注意，與羅氏、賽諾菲、拜耳和默克等合作夥伴合作真的非常高興和榮幸，我們將發揮我們最好的能力：Recursion OS、Recursion 藥物獵人專業知識和平台技術專業知識，以及基因泰克、賽諾菲等公司的深厚生物學專業知識和化學專業知識。

And then, when it comes to Bayer and Merck KgaA, similarly, the second area of value creation that I mentioned earlier, which is challenging targets, developing molecules for them using our chemistry platform or actually highlighting and nominating novel or undruggable targets from our maps of biology. With the potential here, a lot of work ongoing for over $100 million in partnership milestones by the end of 2026.

然後，當談到拜耳和默克集團時，同樣，我之前提到的第二個價值創造領域是挑戰目標，使用我們的化學平台為它們開發分子，或者實際上從我們的生物學圖中突出和提名新的或不可治療的目標。由於這裡存在著巨大的潛力，目前正在進行大量工作，力爭在 2026 年底實現超過 1 億美元的合作里程碑。

With that, I'm going to hand it over to Ben Taylor, our CFO and President of UK, to tell us a little bit more about our financial update. Ben?

接下來，我將把時間交給我們的財務長兼英國總裁 Ben Taylor，請他向我們詳細介紹我們的財務更新。本？

Terrific. Thanks, Najat.

了不起。謝謝，納賈特。

We had a good quarter and ended with a strong cash balance, as we go to the next slide, showing $533 million in cash at the end of the quarter. That was based on not only managing our expenses.

我們度過了一個良好的季度，並以強勁的現金餘額結束，正如我們進入下一張幻燈片時所顯示的，季度末的現金餘額為 5.33 億美元。這不僅僅是基於管理我們的開支。

At the time of the merger, we made a commitment to our shareholders that we would not only drive a lot of the growth and the programs and the technology that Chris and Najat talked about, but also manage our expenses. And so you've seen us go from a pro forma burn in 2024 to an expected cash burn in 2026 that's 35% less. That's really our commitment as a management team to making sure that we're doing this as efficiently as possible.

在合併時，我們向股東承諾，我們不僅會推動克里斯和納賈特談到的大量成長、專案和技術，還會管理我們的開支。因此，您已經看到，我們從 2024 年的預期現金消耗轉變為 2026 年的預期現金消耗，減少了 35%。這確實是我們作為管理團隊的承諾，確保我們盡可能有效率地完成這項工作。

We had some great cash inflows over the quarter. In addition to the Sanofi milestone payment, we also had a $29 million R&D tax credit. This is a UK tax credit. We will continue to receive this in the future, although it will be smaller, as the legislation around it has changed.

本季我們獲得了大量現金流入。除了賽諾菲里程碑付款外，我們還獲得了 2900 萬美元的研發稅收抵免。這是英國的稅收抵免。我們將來仍會繼續收到這筆款項，儘管金額會有所減少，因為相關的立法已經發生了變化。

Our guidance has not changed. We continue to project over $100 million in partnership inflows by the end of 2026; and managing our burn below $390 million in 2026 -- so next year. All of that comes together with an expected cash runway through the fourth quarter of 2027.

我們的指導沒有改變。我們繼續預測，到 2026 年底，合作夥伴的資金流入將超過 1 億美元；到 2026 年（也就是明年），我們的資金消耗將控制在 3.9 億美元以下。所有這些都伴隨著預計到 2027 年第四季的現金流。

That cash burn number that I gave you does not include any partner inflows or other financing or inflows that would come in.

我給你的現金消耗數字不包括任何合作夥伴流入或其他融資或流入。

With that, I will turn it back over to Chris.

說完這些，我將把麥克風交還給克里斯。

Thanks, Ben.

謝謝，本。

Yeah. I just want to end by talking a little bit about how we're looking ahead at the future of Recursion. It's been an incredible last nine months, post the business combination with Exscientia. We really feel like we pulled together the best elements of both companies' platforms into the Recursion OS 2.0, as both myself and Najat talked about earlier.

是的。最後，我只想簡單談談我們如何展望遞歸的未來。與 Exscientia 進行業務合併後的過去九個月是令人難以置信的。我們確實覺得我們將兩家公司平台的最佳元素整合到了 Recursion OS 2.0 中，正如我和 Najat 之前談到的那樣。

Going forward, I think you're going to begin to see us, while maintaining an extraordinarily high bar for quality, bringing unique biological insights identified with our multimodal maps across many different cell types.

展望未來，我想您將會看到我們在保持極高的品質標準的同時，透過跨多種不同細胞類型的多模態圖譜帶來獨特的生物學見解。

We're going to see us bring new ideas, new targets, new chemistry. We're going to use our MOA and target deconvolution systems, tools like Boltz-2, our QMMD systems, and even CRISPR screens to help prosecute those exciting targets. And then, we're going to continue to deploy this ClinTech platform to help translate the models and the programs that we're developing at Recursion with real-world evidence into programs that can move towards the clinic.

我們將看到我們帶來新的想法、新的目標、新的化學反應。我們將使用我們的 MOA 和目標反捲積系統、Boltz-2 等工具、我們的 QMMD 系統，甚至 CRISPR 螢幕來幫助實現這些令人興奮的目標。然後，我們將繼續部署這個 ClinTech 平台，幫助將我們在 Recursion 開發的具有真實世界證據的模型和程序轉化為可以進入臨床的程序。

Again, we are focused on differentiated high-quality programs that are going to go where others can't. We're excited for the Recursion 2.0 platform to start to show you some of those programs that are really bringing together all of the elements from target discovery all the way through to ClinTech in the coming quarters and years.

再次強調，我們專注於差異化的高品質項目，這些項目將涵蓋其他人無法涵蓋的領域。我們很高興 Recursion 2.0 平台能夠在未來幾季和幾年內開始向您展示一些真正將從目標發現一直到 ClinTech 的所有元素整合在一起的程式。

But over the next 18 months, we have a catalyst-packed calendar. The second half of this year, looking really exciting: multiple readouts, including FAP and CDK7, as Najat spoke to earlier. In the first half of next year, we'll be talking about our RBM39 program, with early safety and PK from the monotherapy trial. And then, rolling into the second half of next year, we're going to be looking at both MALT1 and initiating our ENPP1 program, which we were able to bring in recently from our JV with Rallybio.

但在接下來的 18 個月裡，我們的日程安排充滿了催化劑。今年下半年看起來真的令人興奮：多個讀數，包括 FAP 和 CDK7，正如 Najat 之前所說的那樣。明年上半年，我們將討論我們的 RBM39 計劃，包括單一療法試驗的早期安全性和 PK。然後，到明年下半年，我們將研究 MALT1 並啟動我們的 ENPP1 計劃，該計劃是我們最近從與 Rallybio 的合資企業引進的。

In addition to what you see here from our internal pipeline, we're going to be delivering across all of our partnerships with the potential for additional phenomap options, the potential for new project initiations, and the potential for programs being optioned by our partners.

除了您從我們的內部管道看到的內容之外，我們還將為所有合作夥伴提供額外的表型圖選項、啟動新專案的可能性以及由我們的合作夥伴選擇的計劃的可能性。

Again, Recursion, continuing to deliver across both our internal and partner pipeline, while also building the future drug discovery platform that we think is going to help to improve the probability of success, the time, the cost, and the potential of the medicines that we're advancing.

再次，Recursion 繼續在我們的內部和合作夥伴管道中提供服務，同時建立未來的藥物發現平台，我們認為這將有助於提高我們正在推進的藥物的成功機率、時間、成本和潛力。

With that, we're going to move over to the Q&A portion.

接下來，我們將進入問答環節。

I'm going to go to the first question, which comes from multiple parties, which is about our Boltz-2 project.

我要回答第一個問題，這個問題來自多方，是關於我們 Boltz-2 專案的。

The question is: Is Boltz-2 the initiative with a major partner on foundational protein structure modeling that I mentioned at J.P. M earlier this year? The answer is yes. This is the partnership that we alluded to at J.P. Morgan.

問題是：Boltz-2 是否是我今年稍早在 J.P. M 提到的與主要合作夥伴共同開展的基礎蛋白質結構建模計劃？答案是肯定的。這就是我們在摩根大通提到的合作關係。

One of the questions here is why open source versus keeping it internal? We believe that discovering and developing medicines is really, really challenging. Biology is really complex. Chemistry is really complex. There are places where we believe we have a very differentiated advantage, such as with our large-scale phenomics platform and our design platform. These are places where we're going to keep those tools internal.

這裡的一個問題是，為什麼要開源而不是將其保留在內部？我們相信，發現和開發藥物確實非常具有挑戰性。生物學確實很複雜。化學確實很複雜。我們相信我們在某些​​方面具有非常差異化的優勢，例如我們的大規模表型組學平台和設計平台。這些是我們將要內部保存這些工具的地方。

There are other places where we need to be on the forefront but we believe there are many competitive partners or groups working in the space. In those areas, rather than try to keep something internal that others have available to them, we actually think it best to help commoditize that particular technology.

在其他地方我們也需要走在前列，但我們相信有很多有競爭力的合作夥伴或團體在這個領域開展工作。在這些領域，我們認為最好的方法是幫助將該特定技術商品化，而不是試圖將某些東西保留在內部供其他人使用。

That's exactly what we're doing with Boltz-2. We're commoditizing our complement, making sure that everyone has access to the kinds of tools that many groups are using; and then, keeping proprietary those tools that we think nobody else really has.

這正是我們對 Boltz-2 所做的事情。我們正在將我們的補充商品化，確保每個人都能使用許多團體正在使用的工具；然後，將我們認為其他人沒有的工具保留為專有的。

The second question is: Are you still building proprietary models? The answer is: absolutely. We were leveraging the Boltz-2 models before they were public. We also have large-scale internal data sets. One could imagine that we could take the same kind of architectures, the same kinds of models that have been built in Boltz-2 and training them across much larger proprietary data sets to give us an internal advantage.

第二個問題是：您還在建立專有模型嗎？答案是：絕對是如此。在 Boltz-2 模型公開之前，我們就已經開始利用它了。我們還有大規模的內部資料集。可以想像，我們可以採用相同類型的架構、在 Boltz-2 中建構的相同類型的模型，並在更大的專有資料集上對其進行訓練，從而為我們帶來內部優勢。

The second question, I'm going to go to Najat. The question is from [Dennis] at Jefferies. Dennis asked for the CDK7 combo expansion cohort in ovarian cancer. What standard of care are you allowing in the trial? Remind us the level of efficacy they showed in terms of OR and PFS?

第二個問題，我要去納賈特。這個問題來自 Jefferies 的 [Dennis]。丹尼斯詢問了卵巢癌中的 CDK7 組合擴展隊列。您在試驗中允許什麼護理標準？提醒我們他們在 OR 和 PFS 方面表現出的療效水準？

And then, Najat, I'll come to the part 2 after you answer the first one.

然後，Najat，在你回答第一個問題之後，我將進入第二部分。

Thanks, Chris. Thanks, Dennis, for the question. Great question.

謝謝，克里斯。謝謝丹尼斯提出這個問題。好問題。

The standard of care, as I mentioned during the presentation, will be single-agent chemo, plus beva as well. The last that I've seen for that combination, the median PFS was about 6.7 months; and then, median OS was about 14 to 22 months.

正如我在演講中提到的那樣，治療標準將是單一藥物化療，加上貝伐單抗。我最後看到的這種組合的中位 PFS 約為 6.7 個月；中位 OS 約為 14 至 22 個月。

Look, for us, for the combination, we definitely want to see meaningful improvement to the standard of care. This is a patient population with very significant unmet need. The team will look through in terms of what other points might be more critical as well. For instance, the proportion of patients that reach a certain scan by a certain period of time and so forth.

對我們來說，對於組合來說，我們絕對希望看到護理標準有顯著的改善。這是一個存在巨大未滿足需求的患者族群。該團隊還將研究其他可能更關鍵的要點。例如，在一定時期內達到特定掃描的患者比例等等。

So a lot of conversations ongoing there but we definitely want to see meaningful improvement from the standard of care for PFS.

因此，目前有許多討論正在進行，但我們確實希望看到 PFS 護理標準有顯著改善。

Thanks, Najat. You hit part 2 there.

謝謝，納賈特。您已到達第二部分。

I'll move on to the next question, which is [Brendan] from Cowen and [Alec] from [BofA], asked: You mentioned the multi-omic profiling that's ongoing for REC-1245 -- that's our RBM39 program -- do you expect the data from this analysis will, in part, dictate which patients you enroll in future studies? What data from this analysis would you be able to leverage when targeting or enrolling future patients? Finally, can you point to the differentiation of RBM39 compared to other CDK-targeting assets?

我將繼續回答下一個問題，即 Cowen 的 [Brendan] 和 BofA 的 [Alec] 提出的問題：您提到了正在進行的 REC-1245 多組學分析 - 這是我們 RBM39 計劃 - 您是否預計這項分析的數據將在一定程度上決定您在未來研究中招募哪些患者？在定位或招募未來患者時，您可以利用此分析中的哪些數據？最後，您能指出 RBM39 與其他 CDK 目標資產的差異嗎？

Great. Lots of questions. Thank you, Brendan. Thank you, Alec.

偉大的。有很多問題。謝謝你，布倫丹。謝謝你，亞歷克。

I'll start with the first couple of questions, which is the data from this analysis dictating the patients that we'll go into; and then, also [future question].

我先從前幾個問題開始，也就是這次分析的數據決定了我們要研究哪些病人；然後，[未來的問題]。

Look, as I mentioned during the presentation today, really, instead of having -- I think the beauty of the mass cell biology, the phenomaps, the multi-omic approach, and so forth is instead of having, like, a single screen in a certain area for a certain target, you saw the holistic nature of how you can see the target being important and interesting across different pathways.

你看，正如我在今天的演講中提到的那樣，實際上，我認為大規模細胞生物學、表型圖、多組學方法等的美妙之處在於，它不是針對某個目標在某個特定區域進行單一篩選，而是讓你看到目標在不同途徑中的重要性和趣味性的整體性。

That was really important for us to understand that, look, for various forms of replication stress, which can be epigenetic, which can be other areas as well; and DNA repair vulnerabilities are both very important for RBM39 as a target. That was step one. That's actually what helped us for our monotherapy dose escalation to select patients in those areas, right, as you saw in our press release this morning.

這對我們來說非常重要，因為要了解各種形式的複製壓力，這些壓力可能是表觀遺傳的，也可能是其他領域的；DNA修復漏洞對於 RBM39 作為目標都非常重要。這是第一步。這實際上幫助我們將單一療法的劑量提高到這些地區的特定患者，正如您在我們今天早上的新聞稿中看到的那樣。

The other thing I'll also say is, look, the monotherapy dose escalation is going to be important. We're going to see patients with certain biomarkers recruited and enrolled and so forth. We'll make more of the honing in of where we go, based on data that we received.

我還要說的是，單一療法的劑量增加將非常重要。我們將會看到具有特定生物標誌物的患者被招募和登記等等。我們將根據收到的數據，進一步明確我們的去向。

But this is a great way of actually using some of this data, not just for a novel target discovery about something I've said before but also, while you're in discovery, to have a better hypothesis of which patients you might actually want to go forward with.

但這是實際使用這些數據的好方法，不僅是為了發現我之前說過的一些新目標，而且在發現過程中，可以更好地假設你可能真正想要繼續治療哪些患者。

100,000 patients-plus. The expansion is because it actually targets a broad -- has a potential, I should say, to target a broad set of tumor types that are genomically unstable.

超過10萬名患者。擴展是因為它實際上針對的是廣泛的——我應該說，它有潛力針對基因組不穩定的多種腫瘤類型。

Thanks, Najat.

謝謝，納賈特。

And then, the (multiple speakers) --

然後，（多位發言者）——

Oh. Go ahead.

哦。繼續吧。

I just wanted to make sure I answer that. The point of differentiation in RBM39 and CDK7.

我只是想確保我能回答這個問題。RBM39 和 CDK7 中的區分點。

RBM39 is not a kinase, right? A lot of the kinases -- for instance, as I mentioned, CDK12 -- has always been, for a long time, an important oncogenic target. But the homology with CDK13 just makes it challenging to really get that selectivity that you're looking for.

RBM39 不是激酶，對嗎？許多激酶——例如我提到的 CDK12——長期以來一直是重要的致癌標靶。但與 CDK13 的同源性使得獲得您所尋求的選擇性變得非常困難。

So for us, it was born out of that inspiration of selectivity for a target that's important for DDR modulation but went beyond much more when we looked at the broader map.

因此，對我們來說，它源於對目標的選擇性啟發，這對 DDR 調製很重要，但當我們看更廣闊的地圖時，它的意義遠不止於此。

Trust me, the map I even showed you today just for DDR pathways, it's a big, beautiful map. It's much broader than that. So at some point, I'd love to be able to show you more and what we see there.

相信我，我今天向您展示的只是 DDR 路徑的地圖，這是一張大而漂亮的地圖。它的範圍比這要廣泛得多。因此，在某個時候，我很樂意向你們展示更多我們在那裡看到的東西。

Thanks, Najat. Okay.

謝謝，納賈特。好的。

Next question. Brendan from Cowen and [Sean] from Morgan Stanley asked: For the upcoming FAP data, where do you see the threshold for success in that readout that would give you confidence in the path forward? Given the high unmet need in FAP, do you think the magnitude of polyp production you've seen to date would support approval and uptake in this patient population, if replicated in Phase 3?

下一個問題。Cowen 的 Brendan 和摩根士丹利的 [Sean] 問道：對於即將發布的 FAP 數據，您認為該讀數的成功門檻在哪裡，可以讓您對未來的道路充滿信心？鑑於 FAP 中未滿足的需求很高，如果在第 3 階段複製，您是否認為迄今為止所見的息肉產量規模能夠支持該患者群體的批准和吸收？

Thank you, Chris. Thank you, Brendan; and Sean from Morgan Stanley for the question.

謝謝你，克里斯。謝謝布倫丹和摩根士丹利的肖恩提出這個問題。

Yes, look, for FAP, the standard of care -- well, there is no therapeutic that's been approved for FAP. So let me just back up by saying that. Celecoxib and others are used off-label, polyburden reduction about 20% to 30% or so forth.

是的，對於 FAP 來說，護理標準——嗯，目前還沒有被批准用於治療 FAP 的治療方法。因此，讓我再重複一遍。塞來昔布等藥物超說明書使用，多負荷減少約20%至30%左右。

So that -- we are definitely looking for a meaningful improvement in the polyburden reduction. Some of the initial data has been promising. However, what's going to be really important for us is to look at the data later on this year, where we will have a broader patient population.

因此——我們肯定希望在多負擔減少方面取得有意義的進步。一些初步數據令人鼓舞。然而，對我們來說真正重要的是查看今年稍後的數據，屆時我們將擁有更廣泛的患者群體。

The second question around support for approval and uptake, following the data that we see later this year, of course, it's going to be important to have conversations with regulators. Once we do, happy to follow up and share more in terms of what's going to take from an approval perspective.

第二個問題是關於批准和採用的支持，根據我們今年稍後看到的數據，當然，與監管機構進行對話非常重要。一旦我們這樣做了，我們很樂意跟進並分享更多從批准角度需要做的事情。

Thanks, Najat. Next, we have partnership questions coming from [Gil] at Needham and Sean at Morgan Stanley.

謝謝，納賈特。接下來，我們要回答 Needham 的 [Gil] 和摩根士丹利的 Sean 提出的合作問題。

Najat, I'm going to send the first one over to you, which is, for the $7 million milestone achieved under the Sanofi collaboration -- one of the latest in many milestones we've earned from that collaboration -- can you go into more detail as to what exactly was achieved to merit this milestone?

納賈特，我要把第一個問題發給你，即與賽諾菲合作取得的 700 萬美元里程碑——這是我們從該合作中獲得的眾多里程碑中的最新一個——你能更詳細地說明一下究竟取得了哪些成就才值得取得這一里程碑嗎？

Great. The programs that we have -- and again, up to 15 programs as part of this partnership -- I can't disclose exactly, of course, the target but I can say that this was a challenging target in the immunology space. What we do see is the milestone is focused on lead series, right; actually being able to successfully accomplish that. Next upcoming milestones would be development candidate.

偉大的。我們擁有的項目——作為此次合作的一部分，共有多達 15 個項目——當然，我無法透露確切的目標，但我可以說這是免疫學領域的一個具有挑戰性的目標。我們確實看到的是里程碑集中在領先系列上，對的；實際上能夠成功實現這一點。下一個即將到來的里程碑將是發展候選人。

I think the point that's important to note is, look, these are all very, very challenging first-in-class, best-in-class targets and to design them is hard. It's not how you do it traditionally. The fact that we've been able to get four out of four so far -- knock on wood, somewhere -- I think is an important testament to how new approaches can help us and augment what we could do before. But more to come over the next 12 to 15 months.

我認為需要注意的一點是，這些都是非常非常具有挑戰性的一流、一流的目標，設計它們很困難。這不是傳統的做法。事實上，到目前為止，我們已經能夠實現四個目標——敲敲木頭，在某個地方——我認為這是新方法如何幫助我們並增強我們以前可以做的事情的重要證明。但未來 12 到 15 個月還會有更多。

I think this is one of the interesting things about the tech bio space, Najat and Dennis -- or I should say Gil and Sean, is that a lot of the companies in this space that are partnering with large pharma are working on some of the very hardest targets that were not amenable to more traditional approaches. So progress by us and others on these milestones is pretty exciting.

我認為這是科技生物領域的有趣之處之一，Najat 和 Dennis——或者我應該說 Gil 和 Sean，這個領域的許多公司都在與大型製藥公司合作，致力於解決一些最困難的目標，而這些目標無法透過更傳統的方法實現。因此，我們和其他人在實現這些里程碑方面取得的進展是相當令人興奮的。

Ben, I'm going to turn it over to you now. What visibility, if any, do you have on the potential $100 million in milestones by 2026? Are any assumed in the cash runway calculations? Again, this comes from Gil at Needham and Sean at Morgan Stanley.

本，我現在就把它交給你。如果有的話，您對 2026 年實現 1 億美元里程碑有何展望？現金流計算中是否有任何假設？再次，這來自 Needham 的 Gil 和摩根士丹利的 Sean。

Sure. Thanks, Gil and Sean. In a way, we have a lot of visibility in the sense of that guidance was only based on existing partnerships and existing programs in those partnerships. Now, of course, we don't have certainty that those milestones will be accomplished.

當然。謝謝，吉爾和肖恩。從某種程度上來說，我們具有很大的可預見性，因為指導僅基於現有的合作夥伴關係以及這些合作關係中的現有計劃。當然，現在我們還不確定這些里程碑是否能夠實現。

And so what we do is we actually look at all of the programs that we know and we probability weight them. And so this is a probability-weighted number, not the full amount. If we were to take the absolute number, it would be higher than this. And we don't include any potential new business development or additional expansion on programs that are not yet identified.

因此，我們實際上所做的就是查看我們所知道的所有程式並對它們進行機率加權。所以這是一個機率加權數字，而不是全部金額。如果我們取絕對數字，它會比這個更高。我們不包括任何潛在的新業務發展或尚未確定的計劃的額外擴展。

So those are two areas where we could grow potential milestones in the future but this is our best estimate that we felt safe in, given the existing business.

因此，這兩個領域是我們未來可以實現潛在里程碑的，但考慮到現有業務，這是我們感到安全的最佳估計。

Thank you, Ben. Next, we're going to go to Dennis from Jefferies and [Mani] from Leerink, who are both asking questions about our cash runway and how we get to our guidance of Q4 2027 cash outlook.

謝謝你，本。接下來，我們將採訪 Jefferies 的 Dennis 和 Leerink 的 [Mani]，他們都在詢問我們的現金流以及我們如何獲得 2027 年第四季度現金前景的指導。

Sure, absolutely. A couple of important notes here.

當然，絕對是如此。這裡有幾個重要的注意事項。

One, it's really important to always focus on the cash flows, when you're thinking about cash runway. So if you look at our P&L statement, our operating expenses or our net income actually include a lot of non-cash expenses in it. So it's really important to go to that cash flow statement and look down at what is flowing through there.

首先，當你考慮現金流時，始終關注現金流非常重要。因此，如果你查看我們的損益表，我們的營運費用或淨收入實際上包含了許多非現金費用。因此，查看現金流量表並了解其中的資金流動非常重要。

Secondly, all of our guidance that we gave, the $450 million this year, the $390 million next year is cash-based operating expense and CapEx, not including any partner inflows or new business development or financing.

其次，我們給出的所有指導，今年的 4.5 億美元，明年的 3.9 億美元都是基於現金的營運費用和資本支出，不包括任何合作夥伴流入或新業務開發或融資。

And so what we do is we then look, what are all the scenarios that could take us forward and get us to 2027? Actually, what we found is, there are many different ways that we get to the fourth-quarter 2027.

因此，我們要做的就是觀察，有哪些情景可以推動我們前進並邁向 2027 年？實際上，我們發現，實現 2027 年第四季目標的方法有很多種。

What we felt comfortable with is even just looking at our existing partnerships -- like I was just talking about with the milestones -- we felt comfortable that operating in a smart way that we are right now and trying to be as efficient as possible with our expenses, trying to really execute on our existing partnerships and following the same strategy that we have on other cash inflows -- including financing -- we felt very comfortable we can get to the fourth-quarter '27. And so we will continue to move forward.

我們感到滿意的是，即使只是看看我們現有的合作夥伴關係 - 就像我剛才談到的里程碑一樣 - 我們感到滿意的是，我們現在以明智的方式運營，並試圖盡可能高效地管理我們的開支，試圖真正執行我們現有的合作夥伴關係並遵循與我們在其他現金流入（包括融資）上相同的策略 - 我們感到非常有信心我們可以進入 27 年第四季度。因此我們將繼續前進。

As time goes forward, we'll look to optimize as best we can around those different variables.

隨著時間的推移，我們將盡可能地優化這些不同的變數。

Thanks, Ben. Final question here from [John], who asks or says, we've seen companies like XAI making bold moves, such as investing heavily in compute with millions of chips to accelerate their vision, can you share how RXRX is similarly tripling down? What ambitious or transformative initiatives are you planning to reflect your next level of thinking?

謝謝，本。最後一個問題來自 [John]，他問或說，我們已經看到像 XAI 這樣的公司做出大膽的舉措，例如在計算方面投入巨資，使用數百萬塊芯片來加速他們的願景，您能否分享一下 RXRX 如何同樣實現三倍增長？您計劃採取哪些雄心勃勃或變革性的措施來體現您的下一個思維層次？

John, thanks. Great question, I think, to end it.

約翰，謝謝。我認為這個問題問得非常好，可以結束了。

First, I'd just say, if you looked at the State of AI Report that Nathan Benaich puts out, you'll actually see that Recursion is, I believe, one of the only biopharma companies that's actually listed as the top 20 private or public companies in the world -- non-governmental companies -- in terms of the scale of our compute.

首先，我想說的是，如果你看過 Nathan Benaich 發布的《人工智慧現狀報告》，你會發現，我相信，就計算規模而言，Recursion 是唯一一家真正躋身全球前 20 名私營或上市公司（非政府公司）的生物製藥公司之一。

Now, we're nowhere near Tesla, XAI, or any of those companies but we really are driving one of the most sophisticated large-scale compute initiatives in the whole of biopharma. I think that speaks to the ambition that we have for how technology is going to drive this field forward.

現在，我們遠不及特斯拉、XAI 或任何這些公司，但我們確實正在推動整個生物製藥領域最複雜的大規模計算計劃之一。我認為這體現了我們對科技如何推動這一領域發展的雄心。

But in terms of other initiatives, I've spoken at prior events, including J.P. Morgan, about our belief in this field racing towards what we call a virtual cell. This is essentially a computational model of cellular biology that would allow you to predict what would happen to a cell, many different kinds of cells, if you acted on them in any way: you add a protein, you change the effect of a gene or the expression level of a gene, you add a small molecule or multiple small molecules.

但就其他舉措而言，我在包括摩根大通在內的之前的活動中都談到了我們對這一領域向所謂的虛擬細胞邁進的信念。這本質上是一個細胞生物學的計算模型，它可以讓你預測如果你以任何方式對一個細胞（許多不同類型的細胞）採取行動，會發生什麼：你添加一種蛋白質，你改變一個基因的作用或一個基因的表達水平，你添加一個或多個小分子。

We believe that building a reliable and robust virtual cell is going to require not just having really good protein folding data, not just having really good atomistic and physics modeling, and not just having good patient data or pathway data, it's going to require having all of those different data layers and being on the frontier of all of those.

我們相信，建立一個可靠且強大的虛擬細胞不僅需要擁有非常好的蛋白質折疊數據，不僅需要擁有非常好的原子和物理模型，不僅需要擁有良好的患者數據或通路數據，還需要擁有所有這些不同的數據層並處於所有這些數據的前沿。

I think Recursion, today, through our partnerships with companies like Tempus and Helix really driving the patient layer through our own work at Recursion; building the pathway data with genome scale knockout maps across more than a dozen different human cell types; and then, as you heard today with our Boltz modeling and some of our QMMD modeling, we're able to really work at the protein folding of the animistic modeling layer.

我認為，Recursion 今天透過與 Tempus 和 Helix 等公司的合作，真正透過我們在 Recursion 的工作推動患者層面的發展；利用十幾種不同人類細胞類型的基因組規模敲除圖來構建通路數據；然後，正如您今天聽到的，透過我們的 Boltz 模型和一些 QMMD 模型，我們能夠真正在萬物有靈模型的蛋白質方面開展工作層。

I think being able to operate across all those layers is going to be a real advantage, as we race towards the virtual cell and deploy early versions of that internally.

我認為，當我們競相實現虛擬單元並在內部部署其早期版本時，能夠在所有這些層進行操作將是一個真正的優勢。

What's more? We have a team at Recursion called the Frontier Research Group. The Frontier Research Group is a dedicated group of folks who are working at the very frontier in high-risk but high-reward areas. While this virtual cell is a part of the work that group is doing, some of the work you heard about today, including the causal AI modeling using Tempus data, actually started in this Frontier Research Group and now has gone into production across the Recursion OS.

還有什麼？我們在 Recursion 有一個名為 Frontier Research Group 的團隊。前沿研究小組是一群致力於在高風險但高回報領域的前沿工作的人。雖然這個虛擬單元是該小組正在進行的工作的一部分，但您今天聽到的一些工作，包括使用 Tempus 數據的因果 AI 建模，實際上是在這個前沿研究小組中開始的，現在已經在 Recursion OS 中投入生產。

These are the bets we make in high-risk/high-reward areas that then get deployed. In some cases, just six or nine months later.

這些是我們在高風險/高回報領域所做的賭注，然後加以部署。在某些情況下，僅僅六個月或九個月後。

I can't tell you about all the things we're doing in that group but I will say, one of the areas we think is super interesting -- we're watching very closely -- is the use of agents to automate the way we discover things and to automate the way we might discover medicines. That's certainly an area that we're working to stay really close to, as well.

我無法告訴你我們在這個小組中所做的所有事情，但我要說的是，我們認為非常有趣的領域之一——我們正在密切關注——是使用代理來自動化我們發現事物的方式以及自動化我們發現藥物的方式。這當然也是我們正在努力保持密切關注的領域。

So lots of exciting work happening at Recursion and across the whole field. It feels like a very, very exciting area to watch for the next half decade or so.

因此，Recursion 和整個領域都發生了很多令人興奮的工作。看起來，這是一個在未來五年左右值得關注的非常非常令人興奮的領域。

I want to thank everybody for joining us today. We really appreciate having you; really appreciate the questions. We look forward to seeing you at the next earnings call or perhaps, sometime before then.

我要感謝大家今天的出席。我們非常感謝您，也非常感謝您提出的問題。我們期待在下次財報電話會議或在此之前的某個時間見到您。

Thanks, everybody.

謝謝大家。