Recursion Pharmaceuticals Inc (RXRX) 2024 Q4 法說會逐字稿

Welcome, everybody, to Recursion's L(earnings) call. I'm Chris Gibson, Co-Founder and CEO, and I'm excited to take you through Recursions 2024, 2025, and the time ahead.

歡迎大家參加 Recursion 的 L（收益）電話會議。我是共同創辦人兼執行長 Chris Gibson，我很高興帶您了解 Recursions 2024、2025 及未來的發展。

So with that, we'll jump into the slides, and I want to just set the stage, first of all, to share a little bit about the moment in time that we're in right now. Recursion is leading this field of TechBio. We're the frontier of this exciting opportunity to decode biology to change the way that drugs are discovered and developed. And I think what we're seeing in 2024 at Recursion are leading indicators of what this inevitable future may look like. And what we're going to see moving into 2025 now is a cascade of proof points that are going to make it more and more obvious to everyone about what the future of the biopharma industry looks like.

因此，我們將進入幻燈片，首先，我想先介紹我們現在所處的時刻。Recursion 正在引領 TechBio 這一領域。我們處於這一激動人心的機會的前沿，可以透過解密生物學來改變藥物的發現和開發方式。我認為，我們在 2024 年在 Recursion 上看到的是這個不可避免的未來的領先指標。而我們現在將要看到的是，2025 年將會出現一系列的證據，這些證據將讓每個人越來越清楚地了解生物製藥產業的未來。

Now to talk a little bit about how we've gotten to this place in 2024, I want to dive into some of the year-end review, so to speak. And I want to kick that off by talking about our clinical data readouts. Last year, 2024 was the first time that we got to talk about efficacy in our first two clinical programs, REC-617 and REC-994.

現在來談談我們是如何走到 2024 年這一步的，我想深入討論年終回顧。首先我想談談我們的臨床數據讀數。去年，2024年我們第一次討論我們的前兩個臨床項目REC-617和REC-994的療效。

And I'll start with REC-617. This is a potential best-in-class CDK7 inhibitor for advanced solid tumors. And during this sort of dose escalation phase of the study before we would have expected to see efficacy. And in fact, in monotherapy, which also is an exciting opportunity for us. We typically would have expected to see efficacy with combo therapy, we saw not only reasonable safety, but we saw early signals of efficacy.

我將從 REC-617 開始。這是一種針對晚期實體瘤的潛在最佳 CDK7 抑制劑。在研究的這種劑量遞增階段，我們原本預期會看到療效。事實上，單一療法對我們來說也是一個令人興奮的機會。我們通常期望看到聯合療法的療效，我們不僅看到了合理的安全性，而且還看到了療效的早期訊號。

We had one patient who had a reduction in their tumor that was sustained for six months or more and a number of patients who had stable disease. So this is really, really exciting, and we're actually kicking off combo studies here in the very, very near term.

我們有一名患者的腫瘤縮小，這種縮小持續了六個月或更長時間，還有多名患者的病情穩定。所以這真的非常令人興奮，而且我們實際上將在近期啟動組合研究。

REC-994 was another program, one of the ones that helped originate Recursion, an oral superoxide scavenger for the potential first in disease opportunity to treat symptomatic cerebral cavernous malformation. And we demonstrated robust chronic safety treating these patients for a year or more. But also in this signal-finding study, a study designed to help us identify early signals in efficacy across many different potential endpoints to design a future trial.

REC-994 是另一個項目，它是幫助啟動 Recursion 的項目之一，Recursion 是一種口服超氧化物清除劑，有望首次用於治療有症狀的腦海綿狀畸形。並且我們證明了對此類患者進行為期一年或更長時間的治療具有良好的長期安全性。但在這項訊號發現研究中，該研究旨在幫助我們識別許多不同潛在終點的療效早期訊號，以設計未來的試驗。

We saw very encouraging reduction in lesion size based on the subjective MRI measure and also encouraging trends in functional improvement in the modified Rankin score in this Phase II study. So very exciting from our perspective to get to start to turn over these cards of the earliest programs from the Recursion OS.

我們在此 II 期研究中根據主觀 MRI 測量發現病灶尺寸有令人鼓舞的減少，且改良的 Rankin 評分功能也有令人鼓舞的改善趨勢。從我們的角度來看，開始翻看這些來自遞歸作業系統的最早程式的卡片是非常令人興奮的。

What's more? We launched multiple additional trials, including our REC-1245 trial in -- for RBM39 degradation in solid tumors. We are able to launch our familial adenomatous polyposis trial, REK-4881. And then also our C. difficile trial with REC-3964.

還有什麼？我們啟動了多項額外試驗，包括針對實體腫瘤中 RBM39 降解的 REC-1245 試驗。我們可以啟動家族性腺瘤性息肉症試驗 REK-4881。然後我們也對 REC-3964 進行了艱難梭菌 (C. difficile) 試驗。

Beyond that, we continue to advance the next generation of molecules towards the clinic or into the clinic with CTA and IND updates for multiple programs, including the IND clearance for LSD1 in small cell lung cancer, the CTA for MALT-1 and B-cell malignancies, we were able to initiate IND studies in our IPF program, REC-4209 and initiated IND-enabling studies in hypophosphatasia with our REV102 program, which is part of a joint venture with Rallybio.

除此之外，我們繼續推進下一代分子藥物進入臨床或臨床試驗，並為多個項目更新 CTA 和 IND，包括小細胞肺癌中 LSD1 的 IND 許可、MALT-1 和 B 細胞惡性腫瘤的 CTA，我們能夠啟動 IPF 項目 REC-4209 的 IND 研究，並通過我們的 REV102 項目 102 項目啟動低磷酸酯支持症的一部分，Rbio 102 項目啟動的 2512515 月 2012 月。

Even more happened on the pipeline slide. You can see some of the deliveries that we made here in green, you can see that we were delivering across this robust pipeline all throughout 2024 and early 2025, and we're excited to continue that as we move into the coming year.

管道滑坡還發生了更多事故。您可以看到我們在這裡用綠色表示的一些交付，您可以看到我們在整個 2024 年和 2025 年初都在這個強大的管道上進行交付，我們很高興在進入新的一年時繼續這樣做。

But it wasn't just our pipeline. It was also our platform that gave us so many of the early new indications that we're advancing. One of those I want to talk a little bit about today because over the last couple of years, we've started talking mostly about our clinical programs and talking less about some of the early programs in the space. There's just not enough time to go into all the incredible science.

但這不僅僅是我們的管道。也正是我們的平台給了我們許多早期的新跡象表明我們正在前進。今天我想談談其中的一個，因為在過去的幾年裡，我們開始主要談論我們的臨床項目，而較少談論該領域的一些早期項目。我們根本沒有足夠的時間來探究所有這些令人難以置信的科學。

But given the Lilly Scorpion deal we saw a couple of months ago, I want to highlight one of those early programs. This program REC-7735. This is a mutant-selective PI3-kinase inhibitor. And we used the Recursion OS to identify and optimize this molecule and it's hundredfold more selective for the mutant compared to the wild type in our early preclinical models. It's probably 10x fold more selective than other wild-type sparing inhibitors that we've been able to test it against. And most importantly, we've seen this limiting -- limited hyperglycemia when we've tested this.

但考慮到我們幾個月前看到的禮來蝎子交易，我想強調其中一個早期項目。該程式 REC-7735。這是一種突變選擇性 PI3-激酶抑制劑。我們使用 Recursion OS 來識別和優化這種分子，與我們早期的臨床前模型中的野生型相比，它對突變體的選擇性提高了百倍。它可能比我們已經測試過的其他野生型保留抑制劑的選擇性高出 10 倍。最重要的是，我們在測試時看到了這種限制性的——有限的高血糖。

And I'll show you a little bit more of the data here. You can see on the left in vivo in this CDX model, we've taken our molecule, which is in sort of late-stage optimization here, REC-7735, and you see it a variety of increasing doses this tumor regression, which is on par with the SDX compound here. This is the Scorpion compound that garnered so much attraction. So this is really exciting to see.

我將在這裡向你們展示更多一些數據。您可以在左側的體內 CDX 模型中看到，我們採用了處於後期優化階段的分子 REC-7735，您可以看到，隨著劑量的增加，腫瘤逐漸消退，這與此處的 SDX 化合物相當。這就是備受矚目的蝎子大院。看到這一幕真的非常令人興奮。

But what I get even more excited about is if you look at naive wild-type mice, at hyperglycemia. This is one of the major challenges of the PI3 kinase inhibition space is that you can see compared to SDX in these naive mice that were treated for five days with the molecules that the plasma insulin levels are dramatically lower in this REC-7735 molecule even at a dose of 150, that's twice as high as the highest dose you see over on the efficacy side.

但讓我更興奮的是如果你觀察天然野生型小鼠的高血糖。這是 PI3 激酶抑制領域面臨的主要挑戰之一，您可以看到，與這些用分子治療五天的幼稚小鼠的 SDX 相比，即使劑量為 150，REC-7735 分子中的血漿胰島素水平也顯著降低，這是您在療效方面看到的最高劑量的兩倍。

We think this is really, really exciting. We've had a lot of interest from potential partners as we first started talking about this in JPMorgan a couple of weeks ago, and we continue to optimize this molecule and advance it. This is just one of the exciting early discovery or advanced discovery programs that our teams are working on here at Recursion.

我們認為這真的非常令人興奮。幾週前我們在摩根大通第一次談論這個問題時，就引起了很多潛在合作夥伴的興趣，我們會繼續優化這個分子並推進它的發展。這只是我們的團隊在 Recursion 開展的令人興奮的早期發現或高級發現項目之一。

Beyond the pipeline, the exciting programs that we're advancing, we're also in these incredible partnerships working with Roche, Genentech, Sanofi, Bayer, and Merck KGaA. I want to talk a little bit about the Roche and Sanofi partnerships. Those are the ones we're spending the largest amount of our time on. On the Roche side, we generated multiple whole genome phenomaps in oncology and neuroscience last year. That led to a $30 million milestone that was received, and I would say just a ton of excitement at the Roche, Genentech side and on the Recursion side as we start to look at novel exciting biology in both oncology and neuroscience.

除了正在推進的令人興奮的項目之外，我們還與羅氏、基因泰克、賽諾菲、拜耳和默克集團建立了令人難以置信的合作夥伴關係。我想談談羅氏和賽諾菲的合作關係。這些是我們花費最多時間的事情。在羅氏方面，我們去年在腫瘤學和神經科學領域產生了多個全基因組表型圖。這導致了 3000 萬美元的里程碑，我想說，羅氏、基因泰克和 Recursion 方面都非常興奮，因為我們開始研究腫瘤學和神經科學領域令人興奮的新生物學。

On the Sanofi side, in 2024, we advanced two programs through initial milestones. Those generated $15 million in aggregate payments. There's a number of additional programs that are advancing towards or through milestones in the near future as well. So we're really excited about our work there and continue to do really exciting work with Bayer and Merck KGaA.

在賽諾菲方面，2024 年，我們推進了兩個項目並實現了初步里程碑。這些活動共產生了 1500 萬美元的支付。還有許多其他項目在不久的將來也正在朝著里程碑邁進或通過里程碑。因此，我們對在那裡的工作感到非常興奮，並將繼續與拜耳和默克合作進行真正令人興奮的工作。

Finally, I want to talk a little bit about the work we did on our platform in 2024. We continue to lead the industry across data, across foundation models, across compute, and I think really, it feels like it was much further ago than just last year, but we actually built and launched BioHive-2 last year with NVIDIA. We believe this is the most powerful supercomputer wholly owned and operated by any single biopharma company.

最後，我想稍微談談我們在 2024 年在平台上所做的工作。我們繼續在數據、基礎模型和計算方面引領行業，我認為實際上，感覺這比去年要早得多，但實際上我們去年就與 NVIDIA 合作構建並推出了 BioHive-2。我們相信這是單一生物製藥公司全資擁有和經營的最強大的超級電腦。

And I know, as I watch the dashboard of utilization over the last year, we have been running this thing hard. We've been leveraging it to build a variety of different foundation models to advance these models and to explore new architectures of our neural nets across a wide variety of questions as we build sort of towards these world models of biology.

我知道，當我觀察過去一年的使用率儀表板時，我們一直在努力運行這個東西。我們一直在利用它來建立各種不同的基礎模型，以推進這些模型，並在建構這些生物學世界模型的過程中，針對各種各樣的問題探索神經網路的新架構。

Our data capabilities continue to grow and expand as well. We were able to map more than 1.4 million active ligands to binding pockets for structure-based drug discovery and target deconvolution last year. We've generated now up to 6.2 million multi-timepoint brightfield images each week on our Phenomics platform. And we produced just under 1 million Transcriptomes last year, putting us at a total of more than 1.6 million since we launched this work in 2023. So really exciting to see these new data capabilities, these multimodal data capabilities advancing at Recursion.

我們的數據能力也在不斷成長和擴大。去年，我們能夠將超過 140 萬個活性配體映射到結合位點，以用於基於結構的藥物發現和標靶反捲積。我們現在每週在 Phenomics 平台上產生多達 620 萬張多時間點明場影像。去年，我們產生了近 100 萬個轉錄組，自 2023 年啟動這項工作以來，我們的轉錄組總數已超過 160 萬個。看到這些新的數據功能、這些多模式數據功能在 Recursion 上不斷進步，真的非常令人興奮。

And of course, what we've been building in the discovery and translational space for many years, 2024 saw the beginning of our work in causal AI models and in ClinTech. So we used AI models on the Tempus data and Helix data to start building out causal understanding of biology from patient data for the first time. I think one of the most exciting projects here was deploying the Tempus data to build a patient stratification framework in small cell lung cancer for one of our programs that we're advancing into the clinic there.

當然，我們多年來一直在發現和轉換領域不斷探索，2024 年將是我們在因果 AI 模型和 ClinTech 領域工作的開始。因此，我們首次在 Tempus 數據和 Helix 數據上使用 AI 模型，開始從患者數據建立對生物學的因果理解。我認為這裡最令人興奮的項目之一是部署 Tempus 數據來為我們正在推進到臨床的計畫之一建立小細胞肺癌患者分層框架。

And we've also started building automated site engagement tools and enrollment tools to accelerate patient matching, to accelerate patient recruitment, and essentially do a lot of the ClinOps work as we start to scale this pipeline of Recursion. So early days there on the ClinTech side, but really, really exciting as we build this full stack platform.

我們也已經開始建立自動化站點參與工具和登記工具，以加速患者匹配，加速患者招募，在我們開始擴展這個 Recursion 管道時，基本上完成了大量的 ClinOps 工作。因此，我們在 ClinTech 方面還處於早期階段，但隨著我們建立這個全端平台，我們真的非常興奮。

So we've talked about our platform before as companies move into the TechBio space, it's really important for us to be able to generate and aggregate data across many different levels of biology and chemistry. And I think Recursion really leading the field in generating this real-world data or aggregating this real-world data. You can take those data, you can learn from them. You can build models, foundation models across all of these different kinds of questions you might ask in biology and leverage those to make hypotheses that you can go back to the laboratory and test. And it's this iterative loop that makes us so excited that we think it's going to be essential for advancing the field.

我們之前討論過我們的平台，隨著公司進入 TechBio 領域，對我們來說，能夠在生物學和化學的不同層面上產生和匯總數據非常重要。我認為遞歸在生成或聚合真實世界資料方面確實處於領先地位。您可以獲得這些數據並從中學習。您可以針對生物學中可能提出的所有不同類型的問題建立模型、基礎模型，並利用這些問題提出假設，然後回到實驗室進行測試。正是這種迭代循環讓我們如此興奮，我們認為它對於該領域的發展至關重要。

And what we're seeing is that just looking backwards here, looking at Recursion and Exscientia alone, we've already been able to demonstrate these leading indicators of being able to quickly validate hypotheses compared to the industry average.

我們看到的是，只要回顧過去，僅看 Recursion 和 Exscientia，我們已經能夠展示這些領先指標，即與行業平均水平相比，能夠快速驗證假設。

So using this incredible set of real-world data and these world models were able to take an early hypothesis, validate it and move to the hit stage faster than the rest of the industry, using -- looking back at the legacy Exscientia tools, we're able to take those compounds, those early hits and design them up to a more optimized stage synthesizing dramatically fewer molecules than the industry average.

因此，使用這組令人難以置信的真實世界數據和這些世界模型，我們能夠提出早期假設，對其進行驗證，並比業內其他公司更快地進入命中階段。回顧傳統的 Exscientia 工具，我們能夠採用這些化合物、這些早期命中產品，並將它們設計到更優化的階段，合成的分子數量遠遠少於行業平均水平。

And what that means is that we're spending less time compared to the industry average and going faster -- we're spending less money and going faster compared to the industry averages. And of course, the most important measure that we want to change is the probability of success of our molecules in the clinic. And we look forward as we start to readout trial after trial in the coming years to being able to benchmark ourselves there and hopefully start to show some movement across the industry average probability of success. But I think these leading indicators are really, really exciting.

這意味著，與行業平均水平相比，我們花費的時間更少，但速度更快——與行業平均水平相比，我們花費的錢更少，但速度更快。當然，我們想要改變的最重要的指標是我們的分子在臨床上成功的機率。我們期望，隨著未來幾年一次又一次的試驗，我們能夠在那裡對自己進行基準測試，並希望開始顯示整個行業平均成功機率的一些變化。但我認為這些領先指標確實非常令人興奮。

And now I want to invite up Lina Nilsson, our SVP, Head of Platform to join me here in Salt Lake City to talk a little bit about how not just looking back taking our Exscientia combination and looking forward and over the last few months since we brought the companies together, Lina, maybe you can talk about how we're seeing these benchmarks start to improve.

現在，我想邀請我們的高級副總裁、平台主管 Lina Nilsson 來鹽湖城與我一起談論一下如何不僅回顧我們的 Exscientia 合併，而且展望未來，自從我們將兩家公司合併以來的過去幾個月裡，Lina，也許你可以談談我們如何看到這些基準開始改善。

Yeah. Thanks, Chris. So already as two independent companies, as Chris mentioned, we built two technology platforms that are already realizing great efficiencies in drug discovery and development from two complementary angles.

是的。謝謝，克里斯。因此，正如克里斯所提到的，作為兩家獨立的公司，我們已經建立了兩個技術平台，從兩個互補的角度實現了藥物發現和開發的高度效率。

At Recursion, we were founded around novel insights from complex biology mapping. So essentially building giant data sets, fit-for-purpose for generating industry-leading foundation models of human health and disease. And then on the Exscientia side, technologies, models for compound optimization, so efficiently synthesizing molecules with the right multiparameter properties for potential treatments.

在 Recursion，我們是根據對複雜生物學圖譜的新見解而成立的。因此，本質上就是建立巨型資料集，適合產生業界領先的人類健康和疾病基礎模型。然後在 Exscientia 方面，化合物優化的技術和模型可以有效地合成具有正確多參數特性的分子，以用於潛在的治療。

And now we're bringing these two complementary sites together. And very specifically, at the merger, we created 90-day plans to very rapidly supercharge generating this real value. And we promise to tell you about the outcomes from that, and here I am with some of the first results, and there will be more to come later.

現在我們將這兩個互補的網站合併在一起。具體來說，在合併時，我們制定了 90 天計劃，以非常迅速地增強創造這一實際價值的能力。我們承諾將向你們通報研究結果，這裡我已經公佈了部分初步成果，後續還會公佈更多。

Specifically, as you can see here on the right, we have brought together massive data sets from both companies into a unified platform from ADMET, liabilities, Phenomics, and cellular function, protein ligand binding, put all of this into our Centaur model management platform together with Recursion massive compute that you heard about, in order to generate a new generation of models that are more powerful, more accurate, and more generalizable. And then immediately deploy this using Centaur against our pipeline in the Recursion OS. And within the short three months, able to quantify some of the benefits to the pipeline.

具體來說，正如您在右側看到的，我們將兩家公司的海量資料集整合到一個統一的平台中，包括ADMET、負債、表型組學、細胞功能、蛋白質配體結合，將所有這些與您聽說過的Recursion 海量計算一起放入我們的Centaur模型管理平台中，以產生更強大、更準確、更通用的新一代模型。然後立即使用 Centaur 將其部署到 Recursion OS 中的管道上。並且在短短三個月內就能夠量化管道的一些好處。

So for example, deploying new scientific agents that have resulted in a 60% reduction in the human time needed to get to hit to lead initiation or federation of models based on a new data set of over 1 million compounds for more accurate MOA deconvolution, another set of models since Centaur also new with 2.5-fold increase in efficiency of detecting new bioactive compounds as whole groups of new potential useful drug starting points and a 40% reduction in likely cytotoxic compounds.

舉例來說，部署新的科學藥劑可以將需要花費的人力減少 60%，從而基於超過 100 萬種化合物的新數據集來啟動或聯合模型，以實現更準確的 MOA 反捲積，自 Centaur 以來的另一組模型也使檢測新生物活性化合物作為整組新的潛在有用藥物起點的效率提高了 2.5 倍的細胞毒性。

That's just one example there among 18 new admit applications. So really already now seeing massive progress of building a new joint platform, where the new hole is much greater than the sum of the prior parts.

這只是 18 個新入學申請中的一個例子。因此現在確實已經看到了建立新的聯合平台的巨大進展，其中新的漏洞遠大於先前各部分的總和。

And with that, I'm going to hand it off to Ben Taylor, our CFO, who's going to talk about how we are doing all this new exciting work, while at the same time, save money as a joint entity.

接下來，我將把這個任務交給我們的財務長 Ben Taylor，他將講述我們如何完成所有這些令人興奮的新工作，同時作為聯合實體節省資金。

Thanks, Lina. Yeah, it's been a great year for us, both coming together as companies as well as what we've seen looking forward. So for starters, we had $83 million in revenue as a combined group, that's a pro forma basis in 2024 and had an ending cash balance of over $600 million. Now that gives us enough of a runway to be able to extend into 2027. So continuing on the business model that we have, we feel very comfortable that we've got the runway to be able to execute on a lot of those things that Chris and Lina were just talking about.

謝謝，莉娜。是的，對我們來說這是偉大的一年，我們兩家公司走到了一起，並且對未來充滿期待。首先，我們作為一個合併集團的收入為 8,300 萬美元，這是 2024 年的預測基礎，期末現金餘額超過 6 億美元。現在，這為我們提供了足夠的跑道，可以延伸到 2027 年。因此，繼續我們現有的商業模式，我們感到非常舒服，我們已經有能力執行克里斯和莉娜剛才談到的很多事情。

An important element of that, when we closed the deal, we gave guidance that we expected up to $100 million in synergies, we actually believe we will achieve a majority of those synergies this year and be able to get to a run rate that is beyond that $100 million over time. Now, that is coming both from the more traditional synergies that you would expect from two companies coming together but also on a lot of the operational synergies. So all of those benefits that Lina was just talking about actually translate to economic benefits for us too, as we push forward.

其中一個重要因素是，當我們完成交易時，我們給出了預計高達 1 億美元的協同效應的指導，我們實際上相信我們將在今年實現大部分協同效應，並且能夠隨著時間的推移實現超過 1 億美元的運行率。現在，這不僅來自於兩家公司合併後產生的更傳統的協同效應，也來自於大量的營運綜效。因此，隨著我們不斷前進，莉娜剛才談到的所有這些好處實際上也轉化為我們的經濟利益。

In addition, really exciting important note, we have carved out the Vienna operations into a new company that will continue on, and that both gives the new company a great stand-alone mandate as well as helps to organize some of our operations and really provide us as much focus as we can possibly get. And we're on track to also clean up a lot of the excess office and legacy sites that we had previously when we came together as a combined company.

此外，真正令人興奮的是，我們已將維也納業務分拆成一家將繼續運營的新公司，這既賦予新公司強大的獨立授權，也有助於組織我們的部分業務，並真正讓我們盡可能地集中精力。而且，我們還將按計劃清理合併後公司中大量的過剩辦公室和遺留場地。

So we will give you a much more in-depth understanding of some of these developments in May. What we are doing right now is basically going through all of our operations and going from the bottoms up as well as doing a strategic assessment, and we want to give that to you all at once in a few months.

因此，我們將在五月讓您更深入地了解其中的一些發展。我們現在所做的基本上是徹底檢查我們所有的運營，從底層開始，並進行策略評估，我們希望在幾個月內一次性將結果提供給你們。

Now, as we look forward into the next year, we are really, really excited because not only was 2024, as Chris would like to say, our best year yet but when we look forward to '25, we just see so much coming through the pipeline that really starts to show signal and point that the technologies we're developing are having a material difference.

現在，當我們展望明年時，我們真的非常興奮，因為不僅 2024 年（正如克里斯所說）是我們迄今為止最好的一年，而且當我們展望 2025 年時，我們會看到很多新事物正在醞釀中，這真正開始發出信號並表明我們正在開發的技術正在產生實質性的變化。

Chris brought up CDK7 and CCM and the initial data there. We have a number of additional studies that are getting started and we'll have readouts in the near future that could continue to support that moving forward.

克里斯提出了 CDK7 和 CCM 以及那裡的初始數據。我們正在進行一些額外的研究，不久的將來我們將獲得可以繼續支持該研究進展的讀數。

I think the PI3K is actually a great example as well because that is an example of how we have this deep pipeline that most people don't even know about. We were able to look at a situation that was in the market and move quickly to provide what we believe is a better quality candidate that we can move forward with. And it is in that area that is not normally viewed by the rest of the investor market. So really exciting to be able to talk about more of those pipeline programs coming through in the future as well.

我認為 PI3K 實際上也是一個很好的例子，因為它說明了我們擁有大多數人甚至不知道的深層管道。我們能夠了解市場狀況並迅速採取行動，提供我們認為更優質的候選人，以便我們可以繼續前進。而這個領域通常不被其他投資人市場所關注。因此，能夠談論未來更多這樣的管道計劃真的非常令人興奮。

Partnerships will continue to be an important part of our business model. I really want to highlight that in a part of our revenues, we brought in $45 million in cash payments for achieving milestones from Sanofi and Roche. So that's very different from an upfront payment or a license to our technology that is actually achieving technical goals that were extremely difficult and highly valued by those partners. And so we expect to continue achieving those milestones both generating cash flows, but also validating that the technology is doing what we want it to do.

合作夥伴關係將繼續成為我們商業模式的重要組成部分。我真的想強調的是，在我們的收入中，我們從賽諾菲和羅氏獲得了 4500 萬美元的現金支付，以達到里程碑。因此，這與預付款或技術授權非常不同，後者實際上是在實現那些合作夥伴極其困難且高度重視的技術目標。因此，我們希望繼續實現這些里程碑，既產生現金流，也驗證技術是否能夠實現我們的期望。

And then we will also continue to bring forward more of the data on how the platform is exceeding benchmarks, moving faster, being very efficient in how it does it. And so you can expect that through the course of 2025.

然後，我們也將繼續提供更多數據，展示該平台如何超越基準、運行速度更快、更有效率。因此你可以預計到 2025 年才會實現。

All of that, I think, comes together. We take it very seriously. Our mandate from our investors is not to just be a biotech company. It's actually when we say TechBio, what that means is we want to change the underlying probability of success across the biopharma industry, and that's by creating better quality medicines and doing it in a more efficient way. And so we look at all of these points and try and stay focused on the bigger vision. And I'm going to turn it back over to Chris because he can really talk about how these points come together and where we see it going in the future.

我認為，所有這些都是綜合在一起的。我們對此非常重視。投資者對我們的期望不僅僅是成為一家生技公司。實際上，當我們說 TechBio 時，意味著我們想要改變整個生物製藥行業成功的潛在機率，那就是透過創造更高品質的藥物並以更有效的方式去做。因此，我們會考慮所有這些要點，並盡力專注於更大的願景。現在我將把話題交還給克里斯，因為他可以真正談論這些觀點是如何結合在一起的，以及我們對其未來的發展有何看法。

Thanks, Ben. Yeah, I think we're so excited about 2025, as Ben just shared, so many different potential catalysts upcoming across our pipeline, our partnerships, and our platform. But as we look out a little bit beyond that, sort of to the intermediate or even longer term, we believe that there is just extraordinary value to be unlocked. And we think Recursion is better positioned than almost anyone to be able to do this work.

謝謝，本。是的，我認為我們對 2025 年感到非常興奮，正如 Ben 剛才分享的那樣，我們的管道、合作夥伴關係和平台將會出現許多不同的潛在催化劑。但當我們將目光放遠一些，放眼中期甚至更長期，我們相信還有巨大的價值有待釋放。我們認為 Recursion 比幾乎任何人都更有能力完成這項工作。

And so I want to share a little bit of that vision for you. I want to talk a little bit about how we think about the intermediate term. And of course, Recursion today is leading the field and bringing together the real world and building these world models. This is the idea that we can have a laboratory full of scientists who are generating data. That data can be leveraged in a computational environment to turn it into models. We can learn and hypothesize using those models and go back into the real world.

所以我想與你們分享一點這個願景。我想稍微談談我們對中期的看法。當然，如今遞歸正引領著該領域，它將現實世界結合在一起並建立這些世界模型。這就是說，我們可以擁有一個充滿生成數據的科學家的實驗室。這些數據可以在計算環境中利用並轉換為模型。我們可以使用這些模型來學習和假設，然後回到現實世界。

And this has been what we've been building at Recursion for quite some time. This idea of the real world and the world model, this loop of learning and then hypothesizing, but what we think is coming, what we believe is coming in the near to intermediate future is actually a transposition of the -- the transposition where the world models become so good that they actually start to look more like a virtual cell. They start to actually look like -- they start to look like a virtual cell and that virtual cell is well positioned to help us make predictions about the -- make predictions about biology.

這正是我們在 Recursion 上已經建立了很長一段時間的東西。這種關於現實世界和世界模型的想法，這種學習和假設的循環，但我們所想即將發生的事情，我們所相信的在不久的將來即將發生的事情，實際上是一種轉置——這種轉置使得世界模型變得如此優秀，以至於它們實際上開始看起來更像一個虛擬細胞。它們開始看起來像——它們開始看起來像一個虛擬細胞，而這個虛擬細胞可以幫助我們做出有關生物學的預測。

And so if we are taking this virtual cell and making predictions about biology, instead of taking real-world data to inform algorithms to go back and make predictions, there's this transposition that we think is going to fundamentally shift the way that the field engages. It's going to allow us to explore biology more broadly, chemistry more broadly.

因此，如果我們利用這個虛擬細胞對生物學做出預測，而不是利用現實世界的數據來指導演算法並進行預測，那麼我們認為這種轉變將從根本上改變該領域的參與方式。它將使我們能夠更廣泛地探索生物學、化學。

And we believe that there's a number of different data layers and capabilities that are going to be required to truly build a highly, highly realistic virtual cell where you can simulate biology and chemistry in. At the very macro side of that will be patient model, these are real-world patient data and AI. And Recursion hasn't generated a lot of our own data in that space, although our clinical trials are one opportunity for us to do that, but we've partnered with incredible groups like Tempus and Helix, and they're allowing us to bring in tens of petabytes of data from patients all around the country and beyond. And to leverage those to build causal AI models with our underlying data.

我們相信，要真正建構一個高度逼真的虛擬細胞來模擬生物學和化學，需要許多不同的資料層和功能。最宏觀的一面是患者模型，這些是現實世界的患者數據和人工智慧。儘管我們的臨床試驗是我們這樣做的一個機會，但 Recursion 並沒有在該領域產生大量我們自己的數據，但我們已經與 Tempus 和 Helix 等出色的團體合作，他們允許我們從全國各地及其他地方的患者那裡獲取數十 PB 的數據。並利用這些來利用我們的基礎數據來建立因果 AI 模型。

When we start thinking about the pathway levels of biology, Recursion, I think, more than any other company in the space, this is what we're known for, hundreds of millions of different perturbations of biology in our laboratories where we've generated this fit-for-purpose data set across multiple layers of omics. We think we're going to continue to lead there.

當我們開始思考生物學的途徑水平時，我認為，Recursion 比該領域的任何其他公司都更出名，這就是我們實驗室中數億種不同的生物學擾動，我們在多層組學中生成了適合用途的數據集。我們認為我們將繼續在那裡保持領先。

Of course, the protein model side with AlphaFold and other protein folding models, we think this is so exciting to see. And we think it's going to be relatively commoditized. There are so many groups on the cutting edge here and we haven't yet announced all of these, but we are working with partners in this space to make sure that Recursion has access to some of the most advanced protein folding models in the world.

當然，蛋白質模型與 AlphaFold 和其他蛋白質折疊模型相比，我們認為這是非常令人興奮的。我們認為它將會相對商品化。這裡有很多處於前沿的團隊，我們還沒有宣布所有這些團隊，但我們正在與該領域的合作夥伴合作，以確保 Recursion 能夠使用世界上一些最先進的蛋白質折疊模型。

And one step below that on the true micro side of things is the atomistic models. This is sort of the QM/MD side of things. And some have said that AI will never play a role. This is, Hey, I can't really do physics. And I think we're going to see that, that is dramatically incorrect. What we're seeing at Recursion is that AI brought together with QM/MD is actually putting us in a position with the legacy Exscientia team and all of our compute to potentially lead here.

比事物真正的微觀方面低一級的是原子模型。這有點像是事物的 QM/MD 方面。有人說人工智慧永遠不會發揮作用。這是，嘿，我真的不懂物理。我認為我們將會看到，這是完全錯誤的。我們在 Recursion 上看到的是，AI 與 QM/MD 的結合實際上使我們與傳統的 Exscientia 團隊以及我們所有的計算團隊處於同一水平，有可能在這裡處於領先地位。

And there's a lot of companies that -- and foundations and institutes that are working on this kind of atomistic side. But I think Recursion is going to have some really exciting things to share in the coming quarters.

有許多公司、基金會和研究所正在研究這種原子的研究。但我認為 Recursion 將會在接下來的幾季分享一些真正令人興奮的東西。

But it's when you put these all together, it's this vertical stack of data across atoms, across proteins, across pathways and across patients, where I think Recursion is truly going to create this competitive advantage, integrating all of those data into a virtual cell. And while I don't think that's something we're going to be able to deliver in the next 12 months, I do think it's something that is not in the too distant future, and we're going to keep working very hard to win that particular race.

但當你把這些都放在一起時，它就是跨原子、跨蛋白質、跨途徑和跨患者的垂直數據堆棧，我認為 Recursion 真正會創造這種競爭優勢，將所有這些數據整合到一個虛擬細胞中。雖然我不認為我們能在未來 12 個月內實現這一目標，但我確實認為這不是遙不可及的事情，我們將繼續努力贏得這場特殊的比賽。

So now that we've taken a look at 2024, 2025 and maybe a little bit beyond, I want to go and actually dive into questions. And we've got lots of great questions coming in. I'm going to turn here to our Q&A monitor.

現在我們已經展望了 2024 年、2025 年甚至更遠的未來，我想深入探討一些問題。我們收到了很多很好的問題。現在我要轉到我們的問答環節。

It looks like the first question is coming from Alec Stranahan from BofA.

看起來第一個問題來自美國銀行的亞歷克·斯特拉納漢（Alec Stranahan）。

Recursion has spoken about it, supercomputer and data scale, especially on the phenotypic side in the past, but new advances, including DeepSeek or bringing the need for scale into question. I would question that. Do you think this is a risk for TechBio as well? Or is biology just so complex that scale will continue to be essential?

遞歸曾經討論過它、超級電腦和資料規模，特別是過去的表型方面，但包括DeepSeek在內的新進展還是對規模的必要性提出了質疑。我對此表示質疑。您認為這對 TechBio 來說也是一種風險嗎？還是生物學已經如此複雜以至於規模仍然至關重要？

Well, look, Alec, I think it's a great question. I mean biology is extraordinarily complex. And the interaction of biology and chemistry is extraordinarily complex. So I think scale is going to continue to matter. And while DeepSeek did demonstrate that there are ways that you can train and deploy models in a more efficient way, it does not hurt to be able to bring scale to these -- bring scale to the latest generations of neural nets and architectures. I think we're going to be able to do that. And we're going to be able to bring data across all these different data layers together.

嗯，亞歷克，我認為這是一個很好的問題。我的意思是生物學極為複雜。生物學和化學之間的相互作用極為複雜。因此我認為規模仍將很重要。雖然 DeepSeek 確實證明了可以以更有效的方式訓練和部署模型，但能夠將這些模型擴大規模——為最新一代的神經網路和架構擴大規模——並不會有什麼壞處。我認為我們能夠做到這一點。我們將能夠把所有這些不同資料層的資料整合在一起。

And so we don't think that there's a dramatic opportunity for somebody to essentially bootstrap biology and chemistry. We just think that it is fundamentally too complex.

因此，我們認為，對某些人來說，這並不是一個重大的機會來從根本上推動生物學和化學的發展。我們只是認為它從根本上太複雜了。

Next, we'll go to Vikram, one of our analysts at Morgan Stanley.

接下來我們請出摩根士丹利的分析師維克拉姆 (Vikram)。

How is your partnership with NVIDIA progressing? And what are your key focus areas for your foundation model work?

您與NVIDIA的合作進展如何？您基金會模型工作的重點領域是什麼？

Great question, Vikram. So we've been working with the NVIDIA team for many years. We're working on a number of different projects, and they've been helping us to deploy lots of our different models across these very complex supercomputers, BioHive-2 being the one that we have in-house at Recursion. It is not trivial to be able to train on a supercomputer of that scale, not just one or two different models, but many different models across multiple teams and multiple sites.

維克拉姆，你問得好。我們已經與 NVIDIA 團隊合作多年了。我們正在進行許多不同的項目，它們幫助我們在這些非常複雜的超級電腦上部署許多不同的模型，其中 BioHive-2 是我們 Recursion 內部的超級電腦。能夠在如此規模的超級電腦上進行訓練並非易事，不僅僅是一兩個不同的模型，而是跨多個團隊和多個站點的許多不同模型。

And what's more, we're not just using the GPU side of things. We're also using the CPU side of that supercomputer to actually do some of the atomistic work that I talked about just a moment ago. So there's a lot of complexity in just deploying all of these tools and NVIDIA helps us there. There's lots of work that we're excited to continue doing with the NVIDIA team that we haven't talked about yet, and we'll have to wait in future quarters or years to be able to share more of that with you.

而且更重要的是，我們不只是使用 GPU 方面的東西。我們也利用超級電腦的 CPU 端來實際完成我剛才談到的一些原子工作。因此，部署所有這些工具非常複雜，而 NVIDIA 在這方面為我們提供了幫助。我們很高興能與 NVIDIA 團隊繼續合作，但還有很多工作我們還沒有談過，我們必須等到未來的幾個季度或幾年才能與大家分享更多資訊。

Now moving over to Gil at Needham.

現在轉移到尼德姆的吉爾 (Gil)。

Given costs on compute appear to be going down, how much of a moat does owning your own supercomputers still offer?

鑑於運算成本似乎正在下降，擁有自己的超級電腦還能提供多大的護城河？

Yeah, this is a great question. Look, I think there's two things that are necessary and neither sufficient to build these maps of biology and figure out how to advance medicines more quickly. One is data and one is compute. And while the cost of compute is going down, it's not trivial to do compute at the scale that Recursion is doing it.

是的，這是一個很好的問題。我認為，有兩件事對於建立這些生物學圖譜和研究如何更快地推進藥物研發都是必要的，但兩者都不是充分的。一個是數據，一個是計算。雖然計算成本正在下降，但以 Recursion 的規模進行計算並非易事。

So over 2, 5, 10 years, you may see a dramatic reduction, but I think we're going to be moving to virtual cells before we see a 10x reduction in the cost of compute. So we think it will be important for companies operating at the kind of frontier of TechBio to be able to have access to world-class compute. And at least over the next two or three years, we think that's going to be a competitive moat for Recursion.

因此，在 2 年、5 年、10 年後，您可能會看到大幅減少，但我認為，在我們看到計算成本降低 10 倍之前，我們將轉向虛擬單元。因此我們認為，對於處於 TechBio 前沿領域的公司來說，獲得世界一流的運算能力非常重要。至少在未來兩三年內，我們認為這將成為 Recursion 的競爭護城河。

And at the same time, the data side, we think, is the extreme advantage for us. Because it doesn't matter how much money you have or how advanced do we get on the biology side of things, it still takes time for cells to grow. It still takes time for a crisper knockout to mature and create all of the effects downstream in a cell. And biology is so complex that there's sort of this binomial tree of potential possibilities that would take an infinite amount of time to test.

同時，我們認為數據方面對我們來說是極大的優勢。因為無論你有多少錢，或者我們在生物學方面有多先進，細胞生長仍然需要時間。更清晰的敲除仍然需要時間才能成熟並在細胞中產生下游的所有效應。生物學非常複雜，存在著一種表示潛在可能性的二項式樹，需要花費無限的時間來測試。

And so this virtuous cycle of learning and iteration where you can test at some scale, make hypotheses and go back and validate or improve those hypotheses, we think that's going to be key. And we think Recursion is years ahead from almost anyone else in the space in terms of building these data or aggregating these data across all these different levels of biology. So we feel really, really good about that.

因此，這種學習和迭代的良性循環，你可以在某個規模上進行測試，提出假設，然後回過頭來驗證或改進這些假設，我們認為這是關鍵。我們認為，在建立這些數據或跨所有不同生物學層面聚合這些數據方面，Recursion 比該領域的幾乎所有其他人都領先數年。因此我們對此感到非常非常好。

Next, we've got some financial questions. So Ben, I'll turn these over to you. Melissa asks, can you go into more detail about the Q4 revenue drop in 2024 compared to 2023?

接下來，我們有一些財務問題。那麼本，我將把這些交給你。梅麗莎問道，您能否更詳細地介紹一下 2024 年第四季與 2023 年相比收入下降的情況？

Yeah. This is a great question. And it causes a lot of confusion out there because we are, in many ways, a tech company. However, our revenue and earnings don't show up like a traditional tech company. In the sense of we are often in our partnerships, paid a upfront payment that then is recognized as revenue over a longer period of time. So you can't think of this like a subscription or a payment that's coming in quarterly.

是的。這是一個很好的問題。這在很多方面都引起了很多困惑，因為從很多方面來說，我們是一家科技公司。然而，我們的收入和收益並不像傳統的科技公司。從某種意義上說，我們經常在合作中支付預付款，然後在較長一段時間內確認為收入。所以你不能把它想像成一種訂閱或按季度付款。

And so important fact, we've brought in $450 million from our partnerships, a significant amount of that still has not been recognized as revenue. So as we continue to go on and have milestones or enter into new partnerships, those will continue to be cash inflows. They may or may not show up immediately as revenues. So it's really, really hard to track our quarter-to-quarter performance based on our revenue, and we never suggest that people guide to it.

重要的事實是，我們從合作關係中獲得了 4.5 億美元的收入，其中很大一部分尚未確認為收入。因此，隨著我們繼續前進並取得里程碑或建立新的合作夥伴關係，這些將繼續帶來現金流入。它們可能會也可能不會立即表現為收入。因此，根據我們的收入來追蹤我們的季度業績真的非常非常困難，而且我們從不建議人們以此為指導。

A wonderful world of GAAP accounting.

奇妙的 GAAP 會計世界。

All right. Let's go to Dennis Ding from Jefferies, who asks, talk about your expected cash burn for this year and what we should expect at the May 2025 update?

好的。讓我們來聽聽 Jefferies 的 Dennis Ding 的看法，他問道，您今年預計的現金消耗是多少，以及我們對 2025 年 5 月更新的預期是多少？

Sure. We've got another complicated accounting question. So because of when we closed the transaction, most of the financials in our 10-K actually reflect the Recursion -- the legacy Recursion stand-alone financials with a stub period for the legacy Exscientia piece. So what we tried to do was actually put in -- you'll see in the press release and the 10-K the cash burn amount of $184 million. That was the starting year and end of year cash balance for Exscientia, the difference.

當然。我們又遇到了一個複雜的會計問題。因此，由於我們完成交易的時間，我們 10-K 中的大部分財務數據實際上反映了 Recursion——舊版 Recursion 獨立財務數據，以及舊版 Exscientia 部分的存根期間。因此，我們嘗試做的實際上是投入——您會在新聞稿和 10-K 中看到現金消耗額為 1.84 億美元。這就是 Exscientia 年初和年末的現金餘額差額。

As well as if you look at the cash flow from operations and the CapEx from Recursion, you're going to get to a number that's a little over $550 million for the combined entities. That is not a perfect number by any means, to be clear, but to give people a general sense.

如果你看 Recursion 的經營現金流和資本支出，你會發現合併後實體的現金流略高於 5.5 億美元。需要明確的是，這絕不是一個完美的數字，但可以給人們一個整體的認識。

I think what we're comfortable with is we are going to continue to grow, but we will be able to manage our cash burn and be underneath those numbers this year, and we'll give you more detail in May. I don't want to front run ourselves. We want to run a proper budgeting process, but we are very, very focused on cash burn. We are very, very focused on runway. And we'll come back to you with more guidance on that later as well as what makes up it and why.

我認為，我們可以接受的是，我們將繼續成長，但我們將能夠控制我們的現金消耗，並在今年將數字控制在這些數字以下，我們將在五月向您提供更多詳細資訊。我不想搶先一步。我們希望運行一個適當的預算流程，但我們非常非常關注現金消耗。我們非常非常關注跑道。稍後我們會為您提供更多有關該問題的指導以及其組成和原因。

Awesome. Thanks, Ben. Next, it looks like we've got a couple of questions here on our CCM program. This is REC-994 and CCM. So Joe Philips asks, any updates on timing on REC-994? I was wondering if there's more clarification on whether this is going to advance to our commerialization.

驚人的。謝謝，本。接下來，看起來我們對 CCM 程式有幾個問題。這是 REC-994 和 CCM。因此 Joe Philips 詢問，REC-994 的時間有沒有更新？我想知道是否有更多說明表明這是否會促進我們的商業化。

And then Jeff at BioVantage, the primary endpoint of safety for REC-994 was met, but it was negative with regard to efficacy. Can you comment on the status of that program and plans moving forward beyond the recent presentation? What is the rationale for the longer-term treatment that will lead to statistically significant improvements?

然後 BioVantage 的 Jeff 表示，REC-994 的主要安全性終點已經達到，但功效方面卻不佳。您能否評論一下該計劃的現狀以及最近的演示之後的進一步計劃？長期治療能帶來統計上顯著改善的原則是什麼？

So great questions here. So I want to take Jeff's question first, which is, we did see really, really exciting, robust safety across this chronic treatment of a year. But I would challenge this idea that we did not see efficacy in the study. We did a signal-finding study. We're the first company to ever go to any regulatory agency with a CCM clinical trial to look at efficacy.

這裡有很多很棒的問題。因此，我想先回答傑夫的問題，即我們確實看到了在這一年慢性病治療中確實非常令人興奮且強大的安全性。但我對我們在研究中沒有看到療效這一觀點提出質疑。我們做了一個訊號搜尋研究。我們是第一家向監管機構進行 CCM 臨床試驗以檢驗療效的公司。

And so we had to look at a wide variety of different measures, a wide variety of secondary endpoints that could give us an idea of where to go in a subsequent trial. And so in a signal-finding study, you don't necessarily power all of those different end points, you go looking for maybe nearly significant or somewhat significant, but not p-value less than 0.05 findings that you can then parlay into a subsequent study where you narrow down the number of endpoints that you go after.

因此，我們必須研究各種不同的指標、各種次要終點，以便讓我們了解後續試驗的走向。因此，在訊號發現研究中，您不一定要為所有這些不同的終點提供動力，您可以尋找可能幾乎顯著或有點顯著，但 p 值不小於 0.05 的發現，然後您可以將其應用到後續研究中，以縮小要追蹤的終點數量。

What we saw was, I think, nearly significant data with a poorly powered study across this objective measure of MRI. If you look in the brainstem lesions, for example, we see really robust reduction in these particular lesions. And you have to be careful looking at so many different measures, but we've got this long-term extension study that we'll be able to look at soon that will give us insights into whether these trends are continuing.

我認為，我們看到的是，透過這項針對 MRI 的客觀測量，雖然研究力度不夠，但幾乎可以獲得顯著的數據。例如，如果你觀察腦幹病變，我們會發現這些特定病變確實顯著減少。你必須謹慎地看待這麼多不同的指標，但是我們已經進行了這項長期延伸研究，很快就能看到它，它將讓我們了解這些趨勢是否會持續下去。

And then we also saw this trend in modified Rankin Score, which is really, really important because there's a precedence at the FDA from a functional standpoint of looking at neurologic diseases and showing a reduction in this MRS score over time.

然後，我們也看到了改良 Rankin 評分中的這種趨勢，這非常非常重要，因為 FDA 有先例，從功能的角度觀察神經系統疾病，並顯示 MRS 評分隨著時間的推移而下降。

And so these will be key endpoints that we hone in on as we have discussions with the regulators about how to advance this program. And you can imagine if we go forward with a smaller number of endpoints and a larger number of patients, we may be very well positioned if we saw the same quantity and quality of reductions in lesions and improvements in symptoms to actually show statistical significance.

因此，當我們與監管機構討論如何推進該計劃時，這些將是我們關注的關鍵終點。你可以想像，如果我們以較少的終點和更多的患者為研究對象，如果我們看到相同數量和質量的病變減少和症狀改善，我們可能就處於非常有利的位置，從而真正顯示出統計學意義。

Now in terms of the details later this year, we will be able to come back to you post interaction with the FDA and post maybe some exploration of our long-term extension data with some more clear plans on how we plan to advance this program, but I know today, the signals we saw in this signal-finding study were ones that we're excited about and certainly worth exploring for a disease with no other treatment besides surgery.

就今年稍後的細節而言，我們將能夠在與 FDA 互動後再與您聯繫，並發布對我們的長期擴展數據的一些探索，以及有關我們計劃如何推進該計劃的更明確的計劃，但我今天知道，我們在這項信號發現研究中看到的信號令我們興奮，對於除了手術之外沒有其他治療方法的疾病，這些信號絕對值得探索。

All right. And we've got [S. Jane] on the business strategy. So the company's revenue streams suggest a split between a long-term royalty base of revenue and then viable drug candidates and short-term partnership-based deals with existing pharma companies.

好的。我們得到了[S.我向 Jane 介紹了我的商業策略。因此，該公司的收入來源表明，收入分為長期特許權使用費基礎、可行的候選藥物以及與現有製藥公司的短期合作交易。

So there's a two-part question. While we've seen a lot of gains in the partnership revenue, are there any plans to expand and diversify revenue sources beyond the current avenues. So maybe, Ben, actually, I'll send that one over to you and then I'll take the second part in just a minute here.

因此，這個問題由兩個部分組成。雖然我們看到合作收入有了很大的成長，但是否有計劃擴大和多樣化現有管道以外的收入來源？所以也許，本，實際上，我會把那個發給你，然後我會在一分鐘內完成第二部分。

Sure. Well, and it's really interesting. We've certainly done the work internally to look at different revenue streams to come in. What I would say is the economics that we get out of our pharma partnerships are excellent. For example, we're looking at over $300 million in milestone payments per program, high single, low double-digit royalties. And effectively, we have all of our direct cost paid for upfront. And so that's a very economically attractive deal.

當然。嗯，這真的很有趣。我們確實已經在內部做了一些工作，尋找不同的收入來源。我想說的是，我們從製藥合作中獲得的經濟效益非常豐厚。例如，我們預計每個項目的里程碑付款將超過 3 億美元，高個位數的版稅，低兩位數的版稅。實際上，我們所有的直接成本都是預付的。因此，這是一項非常具有經濟吸引力的交易。

For us to diversify into other areas, we actually have to exceed that sort of an ROI threshold. So we do continue to look at it. We do engage with, whether it's partners on the tech side or on the pharma side, different ideas. But I'd say there's a very high bar for us to expand that.

為了實現多元化，我們實際上必須超越這種投資報酬率門檻。因此我們會繼續關注此事。我們確實會與不同的想法接觸，無論是技術方面的合作夥伴還是製藥方面的合作夥伴。但我想說，我們擴大這範圍的門檻非常高。

Thanks, Ben. The second question from S. Jane is how confident is the leadership in our ability to discover and successfully clear clinical trials and get viable drugs on the shelves, potentially tapping into some of the delayed longer-term royalty-based revenue?

謝謝，本。S. Jane 提出的第二個問題是，領導階層對我們發現並成功通過臨床試驗以及將可行藥物上架的能力有多大信心，這有可能利用一些延遲的長期特許權使用費收入？

And I would just say, Look, I think we're very confident here. Discovering and developing medicines is hard, but we've been building a learning system. And my strong belief is that the system we have built has a high probability of being able to generate better molecules and better medicines over time. So whatever level we're at today, on average, I would expect each generation of new molecules to get better.

我只想說，看，我認為我們對這裡非常有信心。發現和開發藥物很難，但我們一直在建立學習系統。我堅信，我們建立的系統很有可能隨著時間的推移產生更好的分子和更好的藥物。因此，無論我們今天處於什麼水平，平均而言，我預計每一代新分子都會變得更好。

Now it's important to note that for any individual program, there are hundreds of ways that it can fail. And some of those can be really, really surprising. And so we can't say with any confidence whether molecule A, B, or C is more or less likely to advance. There are certainly ones where we're investing more to go faster but that it's very hard on an individual asset level to be extraordinarily confident when you're in sort of the preclinical stage, the Phase I stage, the Phase II stage.

現在值得注意的是，對於任何單一程式而言，都有數百種可能失敗的方式。其中一些可能真的非常令人驚訝。因此，我們無法有把握地說分子 A、B 或 C 是​​否有可能前進。當然，我們正在投入更多資金以加快進程，但當你處於臨床前階段、第一階段和第二階段時，在單一資產層面上很難保持特別的信心。

But what I can say is both we, with our own pipeline and our partners with a number of programs that have been moving forward in those partnerships a lot of potential. And what I think is really important about Recursion is that we're not a typical biopharma company that has a handful of assets that create almost a bimodal outcome for the company, where if you're successful in that leading program in that Phase II trial, the company has bought for some really, really exciting number and that molecule advances in someone else's hands.

但我可以說的是，我們都有自己的管道，我們的合作夥伴也有許多專案正在推進，這些合作具有很大的潛力。我認為 Recursion 真正重要的是，我們不是一家典型的生物製藥公司，我們擁有少量資產，這些資產為公司創造了幾乎雙峰的結果，如果你在第二階段試驗的領先項目中取得成功，公司就會以一些非常非常令人興奮的數字購買該分子，而該分子則會在別人的手中取得進展。

We're building a platform that's going to allow us to take many programs forward through our pipeline, many programs through our partnerships, and that starts to remove that kind of bimodal risk. And if one believes our platform is at least as good as the industry average in discovering and developing medicines, but we're doing it at a higher scale and more efficiently I think Recursion can become a really, really important company in the space.

我們正在建立一個平台，使我們能夠透過我們的管道推進許多項目，透過我們的合作夥伴關係推進許多項目，並開始消除這種雙峰風險。如果有人相信我們的平台在發現和開發藥物方面至少與行業平均水平一樣好，但我們在更大的規模和更高的效率上進行操作，我認為 Recursion 可以成為該領域非常非常重要的公司。

If we can do that and over time, start to demonstrate that we're increasing the probability of success. And again, we're not here for trying to build this company and sell it in the next year or two. We're here a decade in with decades more to go. But I think if we can start to demonstrate that improvement in the probability of success, while generating molecules at scale and doing it more efficiently, I think we're a company that has the potential to truly transform this industry. And so those royalty-based revenues would be coming in, in that case. And of course, so with revenues from our own programs that we develop with our internal pipeline.

如果我們能夠做到這一點，並且隨著時間的推移，我們就會開始證明我們正在增加成功的可能性。我再說一遍，我們在這裡並不是為了試圖建立這家公司然後在未來一兩年內將其出售。我們已經走過了十年，未來還有幾十年。但我認為，如果我們能夠開始證明成功機率的提高，同時大規模生產分子並更有效地進行，我認為我們是一家真正有潛力改變這個行業的公司。那麼在這種情況下，那些基於特許權使用費的收入就會進來。當然，我們透過內部管道開發的自己的專案也能帶來收入。

Awesome. Next, let's go to Gil again from Needham.

驚人的。接下來我們再從尼德漢姆去找吉爾。

Can you provide any visibility into milestone payments in 2025? Maybe I'll turn this one over to Ben as well, and maybe I'll add a little something, but Ben take this one.

您能否提供有關 2025 年里程碑付款的任何資訊？也許我也會把這個交給本，也許我會加一點東西，但這個由本來來做。

Sure. The short answer is no. We're not giving any guidance on it right now. But I think what you've seen come out of our partnerships in the past is milestones around either drug programs advancing or delivery of phenomaps. I would say we are doing both of those things still in our partnerships. And so we look to do more of the same and more of it.

當然。簡短的回答是「不」。我們目前不對此提供任何指導。但我認為，您在過去的合作中看到的成果是在藥物專案進展或表型圖交付方面取得的里程碑。我想說我們仍然在合作中做這兩件事。因此，我們希望做更多同樣的事情，並且做得更多。

The other thing that I would note is, and this is alluded to and what Chris was saying, another thing that we are always looking at is when is the right time to either advance a program on our own or to potentially look at partnerships around them as they advance through. And so I think that's something that we continue to look into for our pipeline and that could generate revenues and milestones as well.

我要注意的另一件事是，這是暗示，也是克里斯所說的，我們一直在關注的另一件事是，什麼時候是自行推進我們自己的專案或在專案推進過程中潛在地尋找合作夥伴的正確時機。因此，我認為這是我們將繼續研究的領域，也可以創造收入和里程碑。

Thanks, Ben. Just a couple of things that I would add real quick right there is we've been doing the work of these partnerships for many years and essentially priming the pump on the milestones. And so while we're not giving any formal guidance for this year, I think there's a lot of confidence in where we're going. And as Ben was just alluding to, we're also starting to, I think, get a lot of interest in some of our individual assets that are in our clinical or preclinical pipeline.

謝謝，本。我只想快速補充幾件事，我們多年來一直致力於這些合作工作，並為實現里程碑做好了準備。因此，雖然我們沒有對今年的發展給予任何正式的指導，但我認為我們對我們的發展方向充滿信心。正如本剛才所提到的，我認為我們也開始對我們的一些處於臨床或臨床前研發管線中的個人資產產生濃厚興趣。

And so hopefully, we'll start to be able to demonstrate, again, different ways that Recursion is generating revenue, generating credentialization of the platform and subsidization of the future pipeline that we're going to build.

因此，希望我們能夠再次展示 Recursion 創造收入、為平台提供認證以及為我們將要建立的未來管道提供補貼的不同方式。

Next, so this one from Marcel. I'm actually going to turn over this first question to Lina. So a while ago and by a while ago, Marcel, this is a few weeks ago at the JPMorgan conference -- time flies -- we mentioned the goal of creating virtual cells to enable us to discover and develop medicines at scale, including potentially to be able to simulate clinical trials. Given the known issue of hallucinations in LLM, are there concerns that virtual trials could also be prone to inaccuracies, particularly regarding cell drug interactions?

接下來，這是來自馬塞爾 (Marcel) 的。我實際上要把第一個問題交給莉娜。不久前，馬塞爾，這是幾週前在摩根大通的會議上——時間過得真快——我們提到了創建虛擬細胞的目標，使我們能夠大規模發現和開發藥物，包括可能模擬臨床試驗。鑑於法學碩士 (LLM) 中存在幻覺問題，是否有人擔心虛擬試驗也可能容易出現不準確的情況，特別是在細胞藥物交互作用方面？

And in the rare disease space, where data might be limited, how would you validate the results generated from these virtual trials to ensure their reliability and accuracy?

在罕見疾病領域，數據可能有限，您如何驗證這些虛擬試驗產生的結果以確保其可靠性和準確性？

Yeah, sure. Great question. We rely, of course, on LLMs, in addition to lots of other different model architectures, transformers, deep flow nets, MolE is an architecture unique to Recursion that you might have read about if you are more deeply in the Machine Learning field. And these come both with great power different and -- powers and be able to get accurate predictions. And also sometimes liabilities and limitations and hallucinations and false positives and so on. So we build big branch marking data sets to ensure that our models are performing to the highest capability that we are able to do in our hands with those benchmarking models.

是的，當然。好問題。當然，我們依賴 LLM，除了許多其他不同的模型架構、變壓器、深度流網路之外，MolE 是一種遞歸獨有的架構，如果您對機器學習領域有更深入的研究，您可能已經讀過它。這些都具有不同的強大力量，並且能夠獲得準確的預測。有時還會出現責任、限制、幻覺和假陽性等等。因此，我們建立了大型分支標記資料集，以確保我們的模型能夠透過這些基準測試模型發揮最高水準。

In addition, I think a really great important component here is that we have large laboratory setups, in vivo setups, et cetera, where we can validate insights that come out of these models. So at the play that we're doing, harking back to Chris' point, it's not that we're never validating anything in real assays and really experiment is that we're able to focus down our experimental assays to the most promising compounds, the most promising insights so we can be incredibly comprehensive exactly where it matters most and not spend time and money on compounds that never were going to go anywhere in insights that were going to be the right ones. And so we're still doing this incredible important validation along the way before a drug gets put into patients.

此外，我認為這裡一個非常重要的組成部分是我們擁有大型實驗室設置、體內設置等等，我們可以在其中驗證從這些模型中得出的見解。因此，在我們正在進行的工作中，回想起克里斯的觀點，並不是說我們從未在實際分析中驗證任何東西，真正的實驗是，我們能夠將實驗分析重點放在最有希望的化合物和最有希望的見解上，這樣我們就可以在最重要的地方做到非常全面，而不必花時間和金錢在那些永遠不會在正確見解中取得任何進展的化合物。因此，在藥物投入患者體內之前，我們仍在進行這項極為重要的驗證。

And then specifically around rare diseases, this is definitely, of course, a challenge not just for Recursion but for the industry as a whole. And one component to this is building models that are not just focused at predicting that specific disease but models that can understand broad biology so that we can validate and have model performance that are not just about one specific gene, for example, but about that gene in context with everything else going on in the cell and in that patient.

然後具體到罕見疾病，這當然不僅僅是對 Recursion 的挑戰，也是對整個產業的挑戰。其中的一個組成部分是建立不僅僅專注於預測特定疾病的模型，而且還能理解廣泛的生物學，以便我們能夠驗證模型性能，這些模型不僅涉及一個特定的基因，還涉及該基因與細胞和患者體內發生的所有其他事情的關係。

And that is one way to bring confidence in local areas that are quote-on-quote, rare but in context of all the complexity of human health indices including through data at the atomistic and protein level through our massive data sets and Phenomics linking all the way into the Tempus, Helix, and other patient data that Chris mentioned.

這是一種為局部領域帶來信心的方法，這些領域雖然引用了引文，但很少見，但考慮到人類健康指數的所有複雜性，包括透過原子和蛋白質層級的數據，透過我們龐大的數據集和 Phenomics 一直連結到 Tempus、Helix 和 Chris 提到的其他患者數據。

So adding on to that a little bit -- thank you, Lina. Gil from Needham asks, as it relates to creating a virtual cell environment, what would convince you that or us that we've reached something productive?

所以再補充一點——謝謝你，莉娜。來自尼德漢姆的吉爾 (Gil) 問道，既然它與創建虛擬細胞環境有關，什麼能讓您或我們相信我們已經取得了一些成果？

And so this is a great question. And the reality is, I don't think this is going to be us flipping a switch and all of a sudden going from not productive to productive, it's going to be a gradient that we achieve over the next handful of years. And Gil, we're really focused on benchmarking our team at Valence is helping to lead Polaris which is a benchmarking initiative across the industry. We've done a lot of our models against the therapeutic data commons, predictive [ADMET] benchmarks.

這是一個很好的問題。而現實情況是，我不認為我們會突然從按下開關就從低效率變為高效，這將是我們在未來幾年內實現的一個漸進過程。吉爾，我們真正專注的是進行基準測試，我們在 Valence 的團隊正在幫助領導 Polaris，這是一項跨行業的基準測試計劃。我們已經根據治療數據共享、預測 [ADMET] 基準建立了許多模型。

And what I think we'll be looking for is moving from models that give us insights into predictive ADMET or mechanism of action deconvolution or clinical trial simulation towards broader models that have these emergent features that help us ask and answer questions across many different layers of biology.

我認為，我們所尋求的是從能夠讓我們洞察預測性 ADMET 或作用機制反捲積或臨床試驗模擬的模型，轉向具有這些新興特徵的更廣泛的模型，這些模型可以幫助我們提出和回答跨生物學許多不同層面的問題。

If I were to just sort of imagine a place in the future that would give me the sense that we were really on the cusp and starting to feel like we have a virtual cell, it would be when we start to report to all of you a reduction in the scale of data that we're generating. And we're moving from scaled data into really just validating predictions. And that will be the point at which that transposition has happened.

如果我只是想像未來的某個地方，讓我感覺到我們真的處於風口浪尖，並開始覺得我們擁有一個虛擬細胞，那就是當我們開始向大家報告我們產生的數據規模的減少時。我們正在從縮放資料轉向真正的驗證預測。這就是轉換發生的時刻。

So we're still doing up to 2.2 million Phenomics experiments a week. We've done 1.6 million Transcriptomics experiments over the last 1.5 years, where we're building a number of other kind of data modalities. When you start to see those numbers go down purposefully because we're just validating simulated outputs and we're continuing to build the pipeline, that's when you'll know that we have that virtual cell.

因此我們每週仍要進行多達 220 萬次 Phenomics 實驗。在過去的 1.5 年裡，我們已經完成了 160 萬次轉錄組學實驗，同時我們正在建立許多其他類型的資料模式。當您開始看到這些數字有意下降時，因為我們只是在驗證類比輸出，並且我們正在繼續建造管道，那時您就會知道我們有那個虛擬單元。

Perfect. I think there was one more question here from Laura asking about NIH funding and whether changes in NIH funding might impact the direction that Recursion is going or our ability to discover and develop medicines. And what I would say is in the short term, no.

完美的。我認為勞拉還有一個問題，詢問有關 NIH 資金的問題，以及 NIH 資金的變化是否會影響 Recursion 的發展方向或我們發現和開發藥物的能力。我想說的是，短期內不會。

I mean, Recursion in the early days was dependent on NIH funding, both in my graduate school work with my co-founder, Dean, that was RO1-funded research that led to some of the ideas that helped us build Recursion, but also small business innovative research grants that we were able to bring in about $3.5 million in the early years of building Recursion. I do worry about other start-ups in the space who may not be able to access some of those funds and what that means for the environment around us.

我的意思是，Recursion 在早期依賴 NIH 的資助，既包括我和聯合創始人 Dean 在研究生院合作期間進行的 RO1 資助研究，這些研究產生了一些幫助我們創建 Recursion 的想法，也包括小型企業創新研究資助，在創建 Recursion 的早期，我們能夠獲得大約 350 萬美元的資助。我確實擔心該領域的其他新創公司可能無法獲得部分資金，而這對我們周圍的環境意味著什麼。

And so as you may have seen, Laura, we announced that building off of the incubator that is called Altitude Lab that is funded by Recursion, a number of entrepreneurs myself included and other entrepreneurs in the space are anchoring a fun-aimed at bringing really exciting companies that might be impacted by this disruption in NIH funding to Salt Lake City to build and grow in our incubator to perhaps help them bridge that gap.

正如勞拉，你可能已經看到，我們宣布，在由 Recursion 資助的孵化器 Altitude Lab 的基礎上，包括我在內的多位企業家以及該領域的其他企業家正在發起一項有趣的活動，旨在將可能受到 NIH 資金中斷影響的真正令人興奮的公司帶到鹽湖城，在我們的孵化器中創建和發展，以幫助他們彌補這一差距。

Speaking a little bit more broadly, though, I do think it's very concerning. We see a number of institutions that have started deferring graduate student admissions, and while some companies are doing PhD and Postdocs within their walls, all of the companies in our industry are reliant on the incredible grad student and Postdoc group that comes out of academia.

不過，從更廣泛的角度來看，我確實認為這非常令人擔憂。我們看到許多機構已經開始推遲研究生錄取，雖然一些公司正在內部培養博士和博士後，但我們行業的所有公司都依賴來自學術界的優秀研究生和博士後團隊。

And so I am very concerned over 10 or 15 years, if we don't remedy some of these funding cuts, the US could lose, it's really substantial lead. Remember, most of the world wants to come here to train and there's a reason for that. we're going to start to see that shift if we don't remedy some of these cuts on the NIH side pretty quickly.

因此，我非常擔心，如果我們不彌補一些資金削減，那麼在未來 10 到 15 年內，美國可能會失去顯著的領先優勢。請記住，世界上大多數人都想來這裡訓練，這是有原因的。如果我們不盡快彌補 NIH 方面的一些削減，我們就會開始看到這種轉變。

So great with that. I think we're probably going to decide to move on here. Thank you, everybody, for joining us for L(earnings) call. We're really excited to be building towards the future here, decoding biology to radically improve lives and hope you all have an amazing day. Thank you so much.

太棒了。我想我們可能會決定繼續前進。感謝大家參加我們的收益電話會議。我們非常高興能夠在這裡建立未來，破解生物學，從根本上改善生活，並希望大家度過美好的一天。太感謝了。