Recursion Pharmaceuticals Inc (RXRX) 2023 Q4 法說會逐字稿

Hi, everybody. I'm Chris Gibson, Co-Founder and CEO of Recursion, and I am really excited to welcome you to our first ever learnings call here at Recursion. So what is a learnings call and why are we starting this practice now?

大家好。我是 Recursion 的共同創辦人兼執行長 Chris Gibson，我非常高興地歡迎您參加我們在 Recursion 舉辦的第一次學習電話會議。那什麼是學習召喚呢？

A traditional learnings call has a lot of value. But over the years, I think these have become extraordinarily scripted, frankly quite boring in many cases, and hard to access for all of the stakeholders that we want to be able to speak to. Learnings is our interpretation of a traditional earnings call, which we feel is more authentic -- so I will not be scripted today; I'll just be working off of the slides in front of me -- adaptive, and, we hope, easy to access. And please, if you have suggestions on how we can make this better going forward, please send them our way.

傳統的學習電話具有很大的價值。但多年來，我認為這些已經變得非常照本宣科，坦白說，在許多情況下相當無聊，並且對於我們希望能夠與之交談的所有利益相關者來說都很難接觸到。學習是我們對傳統財報電話會議的解釋，我們認為這更真實——所以我今天不會照本宣科；我將根據我面前的幻燈片進行工作——自適應，並且我們希望易於訪問。如果您對我們如何更好地推進這項工作有任何建議，請發送給我們。

What I would also say is that we've chosen to initiate our first learnings call at this moment, at the start of 2024, because as we look ahead at the future of Recursion, the milestones and catalysts coming before us are going to be coming fast and furious. And we want to make sure that we have a robust mechanism to reach out to all of our stakeholders on a quarterly cadence and to be able to share all the incredible work that we're doing here at Recursion with you.

我還想說的是，我們選擇在 2024 年初的此時此刻啟動我們的第一次學習電話會議，因為當我們展望遞歸的未來時，擺在我們面前的里程碑和催化劑即將到來速度與激情。我們希望確保我們有一個強大的機制，以每季一次的節奏與所有利益相關者聯繫，並能夠與您分享我們在 Recursion 所做的所有令人難以置信的工作。

So to frame where we are today, where we've been, and where we're going, I want to start by going back really a decade, going back to the origins of techbio one decade ago. And it was a really interesting time in the early 2010s. You saw technology companies coming into a wide variety of industries and leveraging a pretty straightforward playbook to bring fundamental new advances from how we get around cities, to how we think about our preferences for digital media, to how we even think about what products we want to order.

因此，為了描述我們今天的處境、我們曾經去過的地方以及我們要去的地方，我想先回顧十年，回到十年前科技生物的起源。2010 年代初期那是一段非常有趣的時期。你看到科技公司進入了各種各樣的行業，並利用一個非常簡單的劇本帶來了根本性的新進步，從我們如何遊覽城市，到我們如何看待我們對數位媒體的偏好，甚至我們如何思考我們想要什麼產品訂購。

And what these companies did was quite straightforward. They used technology to capture high-dimensional data to create a digital record of reality. And it's important to note that the data that they collected was rich -- very, very rich and high dimensional. They aggregated and digitized that data, and then leveraged algorithms to make predictions across all of these massive data sets.

這些公司所做的事情非常簡單。他們利用技術來捕獲高維度資料來創建現實的數位記錄。值得注意的是，他們收集的數據非常豐富——非常非常豐富，而且維度很高。他們匯總並數位化這些數據，然後利用演算法對所有這些海量數據集進行預測。

And most important of all, they went back into the real world to test those predictions. So whether that's telling you to turn left instead of right, whether it's telling you to buy product A instead of product B, or to watch TV show X or Y, these algorithms could be tested in their ability to predict the right outcome in a real setting.

最重要的是，他們回到現實世界來測試這些預測。因此，無論是告訴你向左轉而不是右轉，無論是告訴你購買產品 A 而不是產品 B，還是觀看電視節目 X 或 Y，這些演算法都可以測試它們在實際情況中預測正確結果的能力。環境。

But in biology, this has been extraordinarily challenging. There are so many roadblocks to aggregating and generating the right data to be able to map and navigate this complex system of biology and chemistry. There are three primary drivers of that.

但在生物學中，這極具挑戰性。聚合和產生正確的數據以繪製和導航這個複雜的生物和化學系統存在許多障礙。這有三個主要驅動因素。

First, this world is very analog standard. It was more so in the 2010s, but it still is in some ways today. There are still CROs who send you scanned PDFs or printouts with handwritten notes. And in the biopharma industry, there's a tremendous amount of data, hundreds of petabytes of data, but that data was collected in a way that wasn't built for the purpose of machine learning. And so it's often siloed on legacy servers, it's often built without the right kind of high dimensional nature or the right kind of metadata to make it easier to extract the connections across and between all of those different data.

首先，這個世界是非常模擬標準的。2010 年代更是如此，但今天在某些方面仍然如此。仍然有 CRO 向您發送掃描的 PDF 或帶有手寫筆記的列印輸出。在生物製藥行業，存在大量數據，數百 PB 的數據，但這些數據的收集方式並不是為了機器學習的目的而構建的。因此，它通常孤立在遺留伺服器上，通常是在沒有正確類型的高維性質或正確類型的元資料的情況下建立的，以便更輕鬆地提取所有這些不同資料之間的連接。

And then of course, there's the public data sets that we and others use. But as you all know, there's a reproducibility crisis, and there are real challenges. Because just like in the pharma data, there's not enough metadata and not enough relatability of this data across all these different publications and data sources. And so it's very, very challenging in the biopharma industry to aggregate and generate the right data.

當然，還有我們和其他人使用的公共資料集。但眾所周知，存在可重複性危機，並且存在真正的挑戰。因為就像製藥數據一樣，在所有這些不同的出版物和數據來源中，沒有足夠的元數據，也沒有足夠的相關性。因此，在生物製藥行業中，匯總和產生正確的數據非常非常具有挑戰性。

But what we and other companies who are today leading techbio saw in the early 2010s was an opportunity. We saw exponential improvements across five main areas. The first was the cost of storage. So in the early 2010s, we were at the end of a 40-year cycle of precipitous decreases in the cost of storage. And this is important because a company like us at Recursion, today with over 50 petabytes of proprietary data, has to be able to pay to store all of that data.

但我們和其他當今領先 techbio 的公司在 2010 年代初期看到了一個機會。我們在五個主要領域看到了指數級的改進。首先是儲存成本。因此，在 2010 年代初期，我們正處於儲存成本 40 年急劇下降週期的末尾。這很重要，因為像我們這樣的 Recursion 公司如今擁有超過 50 PB 的專有數據，必須能夠付費儲存所有這些數據。

We were seeing a radical increase in the availability of compute. We'll talk more about our supercomputer a little bit later. We were seeing an increase in accessibility and flexibility of automation tools that allowed us to pioneer and industrialize a new kind of omics using robotics. We were seeing a renaissance in new biological tools like CRISPR. And then, of course, the field of AI was making extraordinary strides as we took 20 years of learnings and really invested in billions of dollars across the tech industry to move from expert systems into this neural net modern AI age.

我們看到運算可用性急劇增加。稍後我們將詳細討論我們的超級電腦。我們看到自動化工具的可及性和靈活性不斷提高，這使我們能夠利用機器人技術開創並工業化一種新型組學。我們看到了 CRISPR 等新生物工具的復興。當然，隨著我們經過 20 年的學習，並在整個科技行業投入數十億美元，從專家系統轉向神經網路現代人工智慧時代，人工智慧領域取得了非凡的進步。

And now fast forward to today, where Recursion is right now, leading techbio. We are taking that same formula that was so obvious across the technology companies of the early 2000s and 2010s and deploying it now across the biopharma industry, where, as I said before, the data is so hard to generate and so hard to aggregate. But we are doing it here at Recursion.

現在快進到今天，Recursion 現在正在引領 techbio。我們正在採用 2000 年代初和 2010 年代初的科技公司中非常明顯的相同公式，並將其部署到生物製藥行業，正如我之前所說，該行業的數據很難生成，也很難聚合。但我們是在遞歸中這樣做的。

We've built a massive, automated platform where we can profile biology across human cells, rodent cells, in vivo systems, and even patient data. We can extract that data in high dimensional space, aggregate it, and then train algorithms on our supercomputer and cloud computing resources to make predictions.

我們建立了一個龐大的自動化平台，可以在其中分析人類細胞、囓齒動物細胞、體內系統甚至患者數據的生物學特徵。我們可以在高維空間中提取數據，聚合它，然後在我們的超級電腦和雲端運算資源上訓練演算法來進行預測。

And this is the most important part. More than any other company in this space, I believe we are set up to take the predictions from our algorithm and test them back in the lab and creating that virtuous cycle of learning and iteration is the Recursion OS. It's what we've been building for the last decade, and it's what we see positions us -- the data, the technology together, and this virtuous cycle -- to really define and lead the techbio space in the decade going forward.

這是最重要的部分。與這個領域的任何其他公司相比，我相信我們能夠從我們的演算法中獲取預測，並在實驗室中對其進行測試，並創建學習和迭代的良性循環，這就是遞歸作業系統。這是我們在過去十年中一直在建立的東西，也是我們所看到的使我們定位的東西——數據、技術以及這種良性循環——在未來十年中真正定義和引領科技生物領域。

But we're not just building at one point in the drug discovery and development process. It takes hundreds of steps to discover and develop a drug. And Recursion today is building these virtuous cycles of wet lab and dry lab of learning and iteration at points from how we connect patient data into our targets to how we optimize chemical compounds, how we translate these programs, and now early work in how we even identify the right patient cohorts to drive our programs into the clinic. I think, more than any other company in this space, really building the full vertical techbio solution.

但我們不僅僅在藥物發現和開發過程中的某個時刻進行建構。發現和開發藥物需要數百個步驟。如今，Recursion 正在建立學習和迭代的濕實驗室和乾實驗室的良性循環，從我們如何將患者數據連接到我們的目標，到我們如何優化化合物，我們如何翻譯這些程序，以及現在我們如何甚至在早期工作確定合適的患者群體，將我們的計畫推向臨床。我認為，比這個領域的任何其他公司都更能真正建立完整的垂直技術生物解決方案。

And that means that we are leading techbio in 2024 across three primary areas -- our internal pipeline, our partnerships, and our platform -- Recursion is leading. Our first-generation programs, five Phase 2s, either enrolling or soon to enroll patients, that are really focused in capital-efficient niche areas of biology. And we're excited to have second-generation programs that are leveraging some of the tools that we have built or added to our platform in just the last few months moving to the clinic as well. If we build this platform right, every generation of programs will be better than the last.

這意味著我們將在 2024 年在三個主要領域（我們的內部管道、我們的合作夥伴關係和我們的平台）領先 techbio，而 Recursion 處於領先地位。我們的第一代項目，五個第二階段，要么正在招募患者，要么即將招募患者，真正專注於資本高效的生物學利基。我們很高興第二代計畫也利用了我們在過去幾個月中建立或添加到我們平台上的一些工具，並將其轉移到臨床上。如果我們正確地建立這個平台，每一代程式都會比上一代更好。

But it's not just our internal pipeline. We're also learning from and working with partners across both bio and tech. On the biology side, we're partnered with Roche-Genentech in neuroscience and one oncology indication, and then also partnered with our colleagues at Bayer in precision oncology.

但這不僅僅是我們的內部管道。我們也向生物和技術領域的合作夥伴學習並與之合作。在生物學方面，我們與羅氏基因泰克在神經科學和一種腫瘤學適應症方面進行了合作，然後也與拜耳的同事在精準腫瘤學方面進行了合作。

But unlike many other companies in this space, we not only have the therapeutic partnerships, we also have partnerships across data with companies like Tempus, across compute with companies like NVIDIA, and across chemistry with companies like Enamine. And it is this cross-credentialization of technology partnerships and biology partnerships that we believe sets us apart.

但與該領域的許多其他公司不同的是，我們不僅擁有治療合作夥伴關係，還與Tempus 等公司建立了數據合作夥伴關係，與NVIDIA 等公司建立了計算合作夥伴關係，並與Enamine 等公司建立了化學合作夥伴關係。我們相信，正是這種技術合作夥伴關係和生物學合作夥伴關係的交叉認證使我們與眾不同。

And all of these partnerships and pipeline are based off of the Recursion platform. Today, over 50 petabytes of proprietary biological and chemical data, spanning human cells to rodent cells to model organisms to human patients. And in order to make use of all of that data substrate, at Recursion today, we now own and operate the fastest supercomputer in the biopharma space.

所有這些合作夥伴關係和管道都基於 Recursion 平台。如今，擁有超過 50 PB 的專有生物和化學數據，涵蓋人類細胞、囓齒動物細胞、甚至人類患者的生物體模型。為了利用所有這些數據基礎，在今天的 Recursion，我們現在擁有並運營生物製藥領域最快的超級電腦。

And in order to take the predictions from the algorithms that we generate on this computer and test them in the lab, we have industrialized and automated multiple levels of omics data generation at Recursion. On our phenomics platform, for example, we're able to do more than 2 million experiments in any given week.

為了從我們在這台電腦上產生的演算法中獲取預測並在實驗室中測試它們，我們在 Recursion 中實現了多個層級的組學數據生成的工業化和自動化。例如，在我們的表型組學平台上，我們可以在任何一周內進行超過 200 萬次實驗。

And so before I talk about what we're looking out to in terms of our near-term catalysts and milestones for Recursion, I want to take a moment to just look back at 2023. And I want to do this because I think it was one of our very best years. Amidst the challenging capital market environment, this team delivered on our pipeline, our partnerships, and our platform. And so we're going to go through just a few of the highlights.

因此，在我談論我們對 Recursion 的近期催化劑和里程碑的期望之前，我想花點時間回顧 2023 年。我想這樣做是因為我認為那是我們最好的幾年之一。在充滿挑戰的資本市場環境中，團隊交付了我們的產品線、我們的合作夥伴關係和我們的平台。因此，我們將僅介紹其中的一些亮點。

First, I'm going to start back in May, where we announced simultaneously, on the same day, the dual acquisitions of Cyclica, a digital chemistry company that's based in Toronto, and Valence, a cutting-edge AI laboratory for drug discovery that's based in Montreal. And we were able to fully integrate the Cyclica team in just 90 days. And in a few minutes, I'll share with you some of the output from that acquisition that led to us advancing and improving our programs within just a few months of signing that deal.

首先，我要從 5 月開始，我們在同一天同時宣布了對總部位於多倫多的數位化學公司 Cyclica 和尖端藥物發現人工智慧實驗室 Valence 的雙重收購。我們在短短 90 天內就完全整合了 Cyclica 團隊。幾分鐘後，我將與大家分享該收購的一些成果，這些成果使我們在簽署協議後的短短幾個月內推進和改進了我們的計劃。

On the Valence side, I'll show you LOWE later, which is our large language model workflow orchestration engine, and this has really been driven by the Valence team. And I will set the stage for how we see a new direction for how biopharma is going to access all of these incredible new techbio tools.

在 Valence 方面，我稍後將向您展示 LOWE，這是我們的大型語言模型工作流程編排引擎，這確實是由 Valence 團隊推動的。我將為我們如何看到生物製藥如何使用所有這些令人難以置信的新技術生物工具的新方向奠定基礎。

In June, we announced that our first clinical trial, SYCAMORE, this is a trial for the first therapeutic candidate to be advanced by any industry sponsor into Phase 2 for cerebral cavernous malformation, and I will remind you that is a massive area of unmet need. This is a disease that affects roughly six times the number of patients as cystic fibrosis, and yet we are the first with an opportunity to be first in disease.

六月，我們宣布我們的第一個臨床試驗SYCAMORE，這是第一個由任何行業贊助商推進到腦海綿狀血管瘤第二階段的治療候選藥物的試驗，我要提醒您的是，這是一個巨大的未滿足需求領域。這種疾病影響的患者數量大約是囊性纖維化的六倍，但我們是第一個有機會成為疾病第一人的人。

This program was fully enrolled in June across 62 patients in three arms. And one thing that gives us a lot of confidence about the tolerability of this molecule is that, today, as patients finish their 12 months on therapy, the vast majority continue to opt into our long-term extension study. And so we'll be reading out the top-line Phase 2 data in Q3 of this year. This will be our first real POC readout, and we're really excited about the opportunity, not only to potentially drive forward an exciting medicine for an area of significant unmet need, but also, regardless of the outcome of that study, to learn and put that data back into our platform so that the next generation of molecules can be even better.

該計畫已於 6 月全部入組，共有 3 組的 62 名患者參與。讓我們對該分子的耐受性充滿信心的一件事是，今天，當患者完成 12 個月的治療時，絕大多數人繼續選擇參加我們的長期擴展研究。因此，我們將在今年第三季讀出第二階段的頂線數據。這將是我們的第一個真正的POC 讀數，我們對這個機會感到非常興奮，不僅有可能為未滿足重大需求的領域推動一種令人興奮的藥物，而且無論該研究的結果如何，我們都可以學習並將這些數據放回我們的平台中，以便下一代分子可以變得更好。

Then in July, a month later, we announced our collaboration with NVIDIA. This included a $50 million equity investment. And with our partners at NVIDIA, we're working on advanced computation, so foundation model development; we've got priority access to compute hardware, which I'll talk about later; and the DGX Cloud resources. And we talked with them about the potential for us to put some of our tools into their BioNeMo marketplace. And in fact, just last month, in January, at the J.P. Morgan Healthcare Conference, we released the first third-party tool to exist on NVIDIA's BioNeMo platform. That was our Phenom-Beta foundation model in January of 2024. So very excited about this ongoing collaboration.

一個月後的 7 月，我們宣布與 NVIDIA 合作。其中包括 5000 萬美元的股權投資。我們正在與 NVIDIA 的合作夥伴一起致力於高階運算，從而開發基礎模型；我們可以優先訪問計算硬件，我將在稍後討論；以及 DGX 雲資源。我們與他們討論了將我們的一些工具放入他們的 BioNeMo 市場的潛力。事實上，就在上個月 1 月的摩根大通醫療保健會議上，我們發布了 NVIDIA BioNeMo 平台上的第一個第三方工具。這是我們 2024 年 1 月的 Phenom-Beta 基礎模型。對這種正在進行的合作感到非常興奮。

One month later, in August, we were able to deliver a demonstration of how we leveraged the May acquisition of Cyclica and our brand-new partnership with NVIDIA to drive a real value into our platform. We were able to predict the protein ligand interactions for more than 36 billion compounds from the Enamine REAL Space across about 80,000 predicted binding pockets spanning the human proteome.

一個月後，在 8 月，我們能夠展示如何利用 5 月收購的 Cyclica 以及我們與 NVIDIA 的全新合作夥伴關係為我們的平台帶來真正的價值。我們能夠預測來自 Enamine REAL Space 的超過 360 億種化合物在跨越人類蛋白質組的約 80,000 個預測結合袋中的蛋白質配體相互作用。

And what this did was generate a large in silico data layer for us, a synthetic data layer. So when we find a new target or an initial hit, we can immediately prioritize that target based on a potential mechanism of action, and we have already advanced multiple programs, terminated multiple programs, or changed the course of multiple programs using this exciting new technology. So we really see it as fantastic to have the complementarity of this functional machine learning algorithm alongside our -- or this physical machine learning algorithm alongside of our functional biology-based platform here at Recursion.

它所做的是為我們產生一個大型的電腦資料層，一個合成資料層。因此，當我們發現新的目標或初始命中時，我們可以立即根據潛在的作用機制優先考慮該目標，並且我們已經使用這項令人興奮的新技術推進了多個專案、終止了多個專案或改變了多個項目的進程。因此，我們真的認為這種功能性機器學習演算法與我們的功能性機器學習演算法以及我們的物理機器學習演算法與我們在 Recursion 的基於功能性生物學的平台的互補性是非常棒的。

Then in September, we announced the Phase 1 study results for REC-3964 in C. diff colitis. The molecule was safe and well tolerated at multiple doses up to 900 milligrams. There were no SAEs and no discontinuations that were related to treatment. And along with the favorable PK profile, this gave us the confidence to advance this new chemical entity towards a Phase 2 trial, which we will initiate later in 2024.

然後在 9 月份，我們發表了 REC-3964 治療艱難梭菌結腸炎的 1 期研究結果。該分子在高達 900 毫克的多次劑量下是安全的且耐受性良好。沒有出現嚴重不良事件，也沒有與治療相關的停藥。加上良好的 PK 特性，這讓我們有信心將這種新化學實體推進 2 期試驗，我們將於 2024 年稍後啟動試驗。

Then back to our platform in September, we announced our first foundation model we call PHENOM-1. It's the world's largest phenomic foundation model that we're aware of. And I want to take a moment just to talk a little bit about this because I think it's really exciting, especially given all of the talk around large language models in the background. In a large language model, one trains a neural network to predict the next word in a sentence or in a paragraph.

然後回到我們九月的平台，我們宣布了我們的第一個基礎模型，我們稱之為 PHENOM-1。這是我們所知的世界上最大的表型組基礎模型。我想花點時間談談這個問題，因為我認為這真的很令人興奮，特別是考慮到背景中所有關於大型語言模型的討論。在大型語言模型中，訓練神經網路來預測句子或段落中的下一個單字。

And we've done something similar here. But instead of using written language, we're using the language of images of human cells. And what you can see on the left is an image where we've masked 75% of the cellular image, and we've trained a neural network to predict what the rest of that image would have looked like. That's the middle row here and you can -- or the middle column. And what you can see is that our neural nets got really good at doing this. You can almost not even tell the difference between the PHENOM-1 reconstructions and the original image.

我們在這裡做了類似的事情。但我們不使用書寫語言，而是使用人體細胞圖像的語言。您可以在左側看到一張圖像，其中我們屏蔽了 75% 的細胞圖像，並且我們訓練了一個神經網路來預測該圖像的其餘部分會是什麼樣子。這是這裡的中間行，或是中間的列。你可以看到我們的神經網路非常擅長做到這一點。您幾乎無法區分 PHENOM-1 重建影像和原始影像之間的差異。

But we're not in the business of reconstructing masked images at Recursion. That's just a training task. And like in a large language model, where the ability to predict the next word in a sentence led to these emerging features that almost gave us a sense of rational thought in ChatGPT and other sorts of settings, we're seeing emergent features from these foundation models. So against a wide variety of benchmark tasks in drug discovery, these sorts of models are giving us state-of-the-art performance to rediscover known biology to make predictions about ADME and tox and beyond.

但我們並不是在遞歸中重建蒙版影像。這只是一個訓練任務。就像在大型語言模型中一樣，預測句子中下一個單字的能力導致了這些新興特徵，這些特徵幾乎給了我們在ChatGPT 和其他類型的設定中理性思考的感覺，我們正在從這些基礎中看到新興特徵模型。因此，針對藥物發現中的各種基準任務，此類模型為我們提供了最先進的性能，可以重新發現已知的生物學，從而對 ADME 和毒素等進行預測。

One of the things that was most interesting about this work, though, was that we were able to demonstrate that the scaling hypothesis holds in the world of biology. We were able to demonstrate that is -- that the bitter lesson holds true and that one must have more data and more compute, all else being equal, in order to build a better model.

然而，這項工作最有趣的事情之一是我們能夠證明縮放假說在生物學世界中成立。我們能夠證明這一點——慘痛的教訓是正確的，在其他條件相同的情況下，我們必須擁有更多的數據和更多的計算，才能建立一個更好的模型。

And so based on that, just two months later, we announced with our partners at NVIDIA that we were expanding our supercomputer, which was already the fastest supercomputer wholly-owned and operated by any biopharma company, with another 504 NVIDIA H100s. And this is a picture of the team just a week or two ago where these H100s have arrived on site. And we believe when this system is up and running, it will not only be the fastest supercomputer in the biopharma space, but it has the potential to be one of the fastest supercomputers privately run in any industry. So we're really, really excited about the potential to get this thing up to speed and humming.

基於此，僅僅兩個月後，我們與NVIDIA 的合作夥伴宣布，我們正在擴展我們的超級計算機，這已經是生物製藥公司全資擁有和運營的速度最快的超級計算機，另外增加了504 台NVIDIA H100。這是一兩週前 H100 抵達現場時團隊的照片。我們相信，當該系統啟動並運行時，它不僅將成為生物製藥領域最快的超級計算機，而且有可能成為任何行業中私人運行的最快的超級計算機之一。因此，我們對讓這個東西加速並運轉的潛力感到非常非常興奮。

But going back to our partnerships, in October, we also announced that Roche had exercised the first program under our collaboration, this program in the context of oncology. And this was fantastic, less than two years after signing that collaboration, to already have a program advancing forward with our partners. And we hope and expect that this is the first of many options to come across this partnership and others.

但回到我們的合作關係，十月份，我們也宣布羅氏已經實施了我們合作的第一個項目，即腫瘤學領域的項目。這真是太棒了，在簽署合作協議後不到兩年，我們就已經有了一個與我們的合作夥伴一起推進的計劃。我們希望並期望這是這種夥伴關係和其他夥伴關係的眾多選擇中的第一個。

In November, we then announced another partnership. This time, instead of just generating data at Recursion, partnering with Tempus, to aggregate what we believe is extraordinarily high-quality patient data into our platform, access to the DNA and RNA sequencing data sets and clinical records for over 100,000 patients that we can now train causal AI models on using the Recursion OS. That gives us now access to over 50 petabytes of proprietary biological and chemical data that we've either generated in-house or partnered with companies like Tempus to bring in place. And I'll talk more in a minute about how we're already leveraging this partnership to drive value in our platform.

11 月，我們宣布了另一項合作夥伴關係。這次，我們不只是在 Recursion 上產生數據，而是與 Tempus 合作，將我們認為非常高品質的患者數據匯總到我們的平台中，訪問超過 100,000 名患者的 DNA 和 RNA 測序數據集以及臨床記錄。現在使用遞歸作業系統訓練因果人工智慧模型。這使我們現在能夠訪問超過 50 PB 的專有生物和化學數據，這些數據要么是我們內部生成的，要么是與 Tempus 等公司合作實施的。稍後我將詳細討論我們如何利用這種合作夥伴關係來推動我們平台的價值。

Also in November, we announced an update to our partnership with Bayer, focusing on precision oncology. And I think it's important to note that with this update, we were able to more than double our per-program milestones, which I think is a strong signal about Bayer's excitement around what we're building. And I know the teams are already hard at work together at Bayer and at Recursion to drive forward some of these initial new oncology programs together.

同樣在 11 月，我們宣布更新與拜耳的合作關係，並專注於精準腫瘤學。我認為值得注意的是，透過這次更新，我們能夠將每個專案的里程碑增加一倍以上，我認為這是拜耳對我們正在建造的產品感到興奮的強烈信號。我知道拜耳和 Recursion 的團隊已經在努力合作，共同推動一些最初的新腫瘤學計畫。

Coming out of that same partnership, before it moved to precision oncology, it was focused in fibrosis. And there was a program that was part of that that we thought was just too good to let go to waste. And so we were able to negotiate with our colleagues at Bayer to in-license this program, which we call Target Epsilon, which we believe is a novel target in the context of fibrosis, and we are driving this program forward very quickly. In fact, we're announcing with today's earnings that this is now in IND-enabling studies at Recursion. So we've already advanced it inside of our own internal pipeline.

出於同樣的合作關係，在轉向精準腫瘤學之前，它專注於纖維化。其中有一個計劃，我們認為它太好了，不能浪費。因此，我們能夠與拜耳的同事協商獲得該專案的許可，我們將其稱為 Target Epsilon，我們認為這是纖維化背景下的一個新目標，並且我們正在快速推動該專案向前發展。事實上，我們透過今天的收益宣布，該研究現已進入 Recursion 的 IND 支持研究中。所以我們已經在我們自己的內部管道中推進了它。

And finally, in December, we also crossed the threshold of having generated over 1 trillion neuronal iPSC cells since 2022. And based on the publicly available data, we believe that this makes us the world's largest producer of high-quality neuronal iPSC cells. And this is but one example of the way our team is working with complex biology, co-culture systems, a wide variety of biology, to drive our platform forward into new, exciting areas like neuroscience.

最後，在 12 月，我們也跨越了自 2022 年以來產生超過 1 兆個神經元 iPSC 細胞的門檻。根據公開數據，我們相信這使我們成為世界上最大的高品質神經元 iPSC 細胞生產商。這只是我們團隊處理複雜生物學、共培養系統和各種生物學的方式的一個例子，以推動我們的平台進入神經科學等新的、令人興奮的領域。

All of this underlying our pipeline, which, as I shared earlier, we believe is the most robust, deepest and broadest in the techbio space. And we are now looking forward at 2024 with this learnings call setting the table for a number of important catalysts that are coming up.

所有這些都是我們的管道的基礎，正如我之前分享的，我們相信這是技術生物領域最強大、最深入和最廣泛的管道。現在，我們展望 2024 年，這項經驗教訓將為即將出現的許多重要催化劑奠定基礎。

First, our Phase 2 top-line readout for CCM in Q3, then a preliminary safety and efficacy readout for NF2 in Q4, and then, in the first half of 2025, a preliminary safety and efficacy readout for FAP, the initiation of our Phase 2 program for C. diff colitis later in 2024, and then another Phase 2 safety and preliminary efficacy readout in the first half of 2025. So Recursion really beginning with this third quarter in 2024, setting the table for what we hope can be roughly quarterly readouts that we hope will help propel the company and the platform forward.

首先，我們在第三季公佈CCM 的第二階段頂線數據，然後在第四季度公佈NF2 的初步安全性和有效性數據，然後在2025 年上半年，公佈FAP 的初步安全性和有效性數據，這是我們第二階段的啟動2024 年晚些時候針對艱難梭菌結腸炎的第 2 期計劃，然後在 2025 年上半年公佈另一個第 2 期安全性和初步療效讀數。因此，Recursion 真正從 2024 年第三季開始，為我們希望的大致季度讀數奠定了基礎，我們希望這將有助於推動公司和平台向前發展。

Beyond these early first-generation programs, we've got our Epsilon project and our RBM39 project, which are the first of our second-generation of programs, making use of some of our newest tools. And we've got more than a dozen discovery and research programs in oncology or with our partners coming behind those.

除了這些早期的第一代程序之外，我們還有 Epsilon 項目和 RBM39 項目，它們是我們第二代程序中的第一個，利用了我們的一些最新工具。我們在腫瘤學領域有十多個發現和研究項目，或者我們的合作夥伴在背後支持這些項目。

Now, before I talk about where we are today and what we see as catalysts in the near term beyond just our pipeline, I want to orient you to the broader trajectory of the space of techbio, at least as we see it. And to do that, I have to go back a ways again, to the early days, back to the 2010s, when companies like Recursion were founded.

現在，在我談論我們今天的處境以及我們認為短期內超越我們的管道的催化劑之前，我想讓您了解科技生物領域更廣泛的軌跡，至少在我們看來是這樣。為了做到這一點，我必須再次回到早期，回到 2010 年代，像 Recursion 這樣的公司成立了。

And all of these companies really made their start with a point solution, and we're no different. We were scaling, industrializing, and pioneering a new kind of omics based on images of human cells to try and understand and explore biology. And since that time, we've actually seen that our work in this space has just continued to grow in complexity.

所有這些公司都是從單點解決方案起步的，我們也不例外。我們正在擴展、工業化並開創一種基於人類細胞影像的新型組學，以嘗試理解和探索生物學。從那時起，我們實際上已經看到我們在這個領域的工作的複雜性持續增長。

Today, we can leverage our automated platform on phenomics to generate more than 2.2 million experiments worth of data every week. We leverage extraordinary foundation models like PHENOM-1 that I talked about earlier to make predictions about the relationships across more than 5 trillion biological and chemical contexts. This is an extraordinary, extraordinary feat, and it's based on broad biology, over 50 human cell types that we've explored, roughly 2 million chemical compounds, whole-genome CRISPR knockouts. This is really, really exciting work that we continue to push the limits of.

如今，我們可以利用我們的表型組學自動化平台每週產生超過 220 萬次實驗的數據。我們利用我之前提到的 PHENOM-1 等非凡的基礎模型來預測超過 5 兆個生物和化學環境中的關係。這是一項非凡的壯舉，它基於廣泛的生物學、我們探索過的 50 多種人類細胞類型、大約 200 萬種化合物、全基因組 CRISPR 敲除。這確實是一項非常非常令人興奮的工作，我們將繼續挑戰極限。

But this is but one step in the Recursion OS today. While we started with phenomics, it is now one of many steps spanning patient connectivity all the way to the clinic. And while I wish we had time to go through each one of these, I'm just going to focus on a few of these areas that I think are important to illustrate some of our focus on building these virtuous cycles.

但這只是當今遞歸作業系統的一個步驟。雖然我們從表型組學開始，但它現在是跨越患者連接一直到診所的眾多步驟之一。雖然我希望我們有時間逐一討論，但我只想重點討論其中的一些領域，我認為這些領域對於說明我們對建立這些良性循環的關注很重要。

And the first of those is DMPK. Our DMPK platform is now up and running at Recursion. This is a highly automated platform that's allowing us to execute three critical assays across both human and rat contexts. We can do nearly 1,000 compounds a week on this automated platform. And this is great because we can profile the molecules that are moving through our internal pipeline or our partnership pipeline. But what's more, we're using the majority of this platform's bandwidth to actually profile many diverse compounds to build the data substrate on which we can train additional state-of-the-art predictive ADME and tox models. And it's this virtuous cycle of learning and iteration of data generation and algorithm improvement that we think will differentiate us, not only in target discovery with phenomics, hit discovery with phenomics, but even in how we advance our molecules towards the clinic.

第一個是 DMPK。我們的 DMPK 平台現已在 Recursion 上啟動並運行。這是一個高度自動化的平台，使我們能夠在人類和大鼠環境中執行三種關鍵測定。我們每周可以在這個自動化平台上進行近 1,000 種化合物的合成。這很棒，因為我們可以分析透過我們的內部管道或合作夥伴管道移動的分子。但更重要的是，我們使用該平台的大部分頻寬來實際分析許多不同的化合物，以建立資料基礎，我們可以在該資料基礎上訓練其他最先進的預測 ADME 和毒理學模型。我們認為，正是這種學習、數據生成迭代和演算法改進的良性循環將使我們與眾不同，不僅在表型組學的標靶發現、表型組學的命中發現方面，甚至在我們如何將分子推向臨床方面。

And it doesn't just stop in human or rodent cells. We're building these same kind of tools in model organisms. In our vivarium, we have over 1,000 cages with cameras and other sensors that allow us to extract much richer, high-dimensional data from each one of these animals. And this means we can use fewer animals as we drive our programs forward, and it means we can make decisions in real time. We can deprioritize and prioritize molecules based on digital tolerability studies in real time. And this has already made a difference in both accelerating and leading to the faster termination of programs at Recursion.

而且它不僅停留在人類或囓齒動物細胞中。我們正在模型生物體中建構這些相同類型的工具。在我們的動物園裡，我們有 1,000 多個裝有攝影機和其他感測器的籠子，使我們能夠從每隻動物身上提取更豐富、高維度的數據。這意味著我們在推進專案時可以使用更少的動物，也意味著我們可以即時做出決策。我們可以根據即時數位耐受性研究對分子進行取消優先順序和優先排序。這已經在加速和導致遞歸程序更快終止方面產生了影響。

But it's beyond model organisms. It also goes to the ultimate model organism, and that is humans. With our Tempus data, we're able to now aggregate patient data across oncology together with all of the wet lab data we've generated at Recursion. And in just about eight weeks since we've had access to this data, this has already led to our team combining our wet lab data and the patient data. So forward and reverse genetics coming together and allowing us in the context of non-small cell lung cancer to already identify multiple potential drivers of disease that we are predicting are causal, which in many cases have not yet been robustly explored in this space. So Recursion now has a program that has advanced just in the first eight weeks based on this kind of data, and we're really just getting started.

但這超出了模式生物的範圍。它也適用於最終的模式生物，那就是人類。借助 Tempus 數據，我們現在能夠匯總腫瘤學領域的患者數據以及我們在 Recursion 中產生的所有濕實驗室數據。自從我們獲得這些數據以來，在大約八週的時間裡，我們的團隊已經將我們的濕實驗室數據和患者數據結合起來。因此，正向和反向遺傳學結合在一起，使我們能夠在非小細胞肺癌的背景下識別出我們預測是因果關係的多種潛在疾病驅動因素，而在許多情況下，這些驅動因素尚未在該領域已充分探索。因此，Recursion 現在有了一個基於此類數據的程序，該程序在前八週內就取得了進展，而我們實際上才剛剛開始。

But what's happening is that as we continue to build this full stack of technology tools and as each of these tools runs through its virtuous cycle of learning and iteration and is improved rapidly, it's becoming increasingly complicated for anyone to keep up with the latest on each tool, the right way to use each of these tools. And we actually think this is going to be a problem across the industry, as we and many others are building lots of different models and lots of different tools.

但正在發生的事情是，隨著我們繼續建立這一整套技術工具，並且隨著每個工具都經歷學習和迭代的良性循環並迅速改進，任何人想要跟上每個工具的最新動態都變得越來越複雜。事實上，我們認為這將成為整個行業的問題，因為我們和許多其他人正在建立許多不同的模型和許多不同的工具。

And so we wanted to address that together with our colleagues at Valence Labs. And we were able, at the J.P. Morgan Healthcare Conference, both in the conference, we think for the first time doing a live software demo and, also at the event, we co-hosted with NVIDIA to show off our LOWE system. This is a large language model-orchestrated workflow engine.

因此，我們希望與 Valence Labs 的同事一起解決這個問題。我們能夠在摩根大通醫療保健會議上首次進行現場軟體演示，並在活動中與 NVIDIA 共同主辦，展示我們的 LOWE 系統。這是一個大型語言模型編排的工作流引擎。

And what this is allowing you to do -- what our scientists and our partner scientists may be able to do with this technology, this tool, is to use natural language, to not have to be an expert programmer, to be able to access all of the tools, to be able to design experiments the right way, to order experiments and execute them on our platform, to analyze data and visualize data using the latest tools at Recursion. And really, this kind of technology is putting the power of the Recursion OS at the fingertips of all of our scientists and partners.

這讓您能夠做的事情 - 我們的科學家和我們的合作夥伴科學家可以利用這項技術、這個工具做的是使用自然語言，不必成為專家程序員，就能夠訪問所有內容工具，能夠以正確的方式設計實驗，訂購實驗並在我們的平台上執行實驗，使用Recursion 的最新工具分析數據和視覺化數據。事實上，這種技術讓我們所有的科學家和合作夥伴都能輕鬆掌握遞歸作業系統的強大功能。

And we see this trajectory as very similar to the early days, the late '70s and early '80s, in the personal computer space. You had products like the Apple-1 on the left, where you really had to be an expert user. You had to be comfortable with this microprocessor board. You had to be comfortable working at the command line in order to make use of this burgeoning new technology.

我們認為這一軌跡與個人電腦領域早期（即 70 年代末和 80 年代初）非常相似。你有像左邊的 Apple-1 這樣的產品，你必須是專家用戶。您必須熟悉這個微處理器板。為了利用這項新興的新技術，您必須熟悉命令列工作。

And with subsequent Apple models, including Lisa on the right, we moved to a graphical user interface. And this created really a renaissance in the ability of more people to be able to harness the power of compute. And what we're building with LOWE, with the Recursion OS, we believe is akin to this, but it's really a discovery user interface.

對於後續的 Apple 型號（包括右側的 Lisa），我們轉向了圖形使用者介面。這確實讓更多人能夠利用計算的力量。我們用 LOWE 和遞歸作業系統建構的東西，我們相信與此類似，但它實際上是一個發現使用者介面。

And we believe it's going to allow each scientist at Recursion and beyond to make more progress faster. It's going to mean that our teams are doing less of the toil and more of the thinking around our projects. And it also means that these tools are going to be accessible, not just to scientists in biology and chemistry, but to software engineers and data scientists, to BD, and to finance. And we think ultimately that's going to be fantastic for the field, and we believe Recursion is really leading out on this new trajectory for our industry.

我們相信，這將使 Recursion 及其他領域的每位科學家更快地取得更多進展。這意味著我們的團隊將減少工作量，並更多地圍繞專案進行思考。這也意味著這些工具不僅可供生物學和化學領域的科學家使用，還可供軟體工程師和資料科學家、BD 和金融界使用。我們認為最終這對該領域來說將是非常棒的，並且我們相信遞歸確實引領了我們行業的這一新軌跡。

So before I move to questions, I want to just end with our near-term milestones, the things that we believe we're going to hit over the next 12 to 18 months or sooner. And I'll start with additional INDs. We've got both our RBM39 program and our Target Epsilon program that we in-licensed from Bayer moving towards the clinic. We've got more Phase 2 trial starts, AXIN1 or APC and C. diff that we believe will be starting this year. We have multiple Phase 2 readouts that I alluded to earlier. And all of this on top of a healthy balance sheet with nearly $400 million in cash at year-end 2023.

因此，在開始提問之前，我想先以我們的近期里程碑來結束，這些里程碑是我們相信我們將在未來 12 到 18 個月或更短時間內實現的目標。我將從額外的 IND 開始。我們已經從拜耳獲得了 RBM39 專案和 Target Epsilon 專案的許可，並正在走向臨床。我們已經開始了更多的 2 期試驗，AXIN1 或 APC 和 C. diff，我們相信今年開始。我們有多個我之前提到的第二階段讀數。所有這一切都建立在健康的資產負債表之上，截至 2023 年底，公司擁有近 4 億美元的現金。

And what's more, we see the potential for significant runway extending options for our map building initiatives with partners and for additional partnership programs being optioned. And beyond that, we see the strong potential for additional partnerships in large intractable areas of biology like cardiovascular metabolism and immunology, where we expect robust upfront payments that will further extend our runway.

更重要的是，我們看到了我們與合作夥伴的地圖建造計劃以及其他合作夥伴計劃的重要跑道延伸選項的潛力。除此之外，我們看到了在心血管代謝和免疫學等大型棘手生物學領域建立更多合作夥伴關係的巨大潛力，我們預計強勁的預付款將進一步擴展我們的跑道。

And what's more, we have an ATM open, which we are using in a very, very surgical way with the right investors at the right time in order to make sure that the company maintains a robust runway moving forward across all of these exciting catalysts.

更重要的是，我們有一個開放的自動櫃員機，我們正在以一種非常非常外科手術的方式在正確的時間與正確的投資者一起使用它，以確保公司在所有這些令人興奮的催化劑中保持強勁的發展勢頭。

And finally, we've got the potential both on the BioNeMo platform and through our LOWE tool to make some of our data and some of our tools available to biopharma and commercial users. And there's the potential for some of that work to generate additional revenue as well.

最後，我們在 BioNeMo 平台上和透過我們的 LOWE 工具都有潛力向生物製藥和商業用戶提供我們的一些數據和一些工具。其中一些工作也有可能產生額外收入。

So I hope you're as excited about the future of techbio as I am. I hope this has been helpful for you to see the trajectory of the company through 2023 into 2024 and how we see the future of our industry.

所以我希望您和我一樣對 techbio 的未來感到興奮。我希望這對您了解公司從 2023 年到 2024 年的發展軌跡以及我們如何看待產業的未來有所幫助。

And with that, I'm going to stop here and head over to answer some questions. And these are being updated by our team live. If you haven't had a chance to ask a question yet, please log into the Slido tool and do so now.

說到這裡，我將在這裡停下來回答一些問題。我們的團隊正在即時更新這些內容。如果您還沒有機會提問，請立即登入 Slido 工具進行提問。

And it looks like the first question is from Morgan Brennan of CNBC. And Morgan asks, what has the reaction been so far from drug makers and others to LOWE? How big do you think this revenue stream could be for the company?

第一個問題似乎是來自 CNBC 的摩根布倫南 (Morgan Brennan)。摩根問道，到目前為止，製藥商和其他公司對 LOWE 有何反應？您認為該收入來源對公司來說有多大？

Thanks, Morgan. That's a fantastic question. I would say that the response has been really, really robust. We had many R&D heads of large pharma companies at our J.P. Morgan presentation, which we co-hosted with NVIDIA. We had CEOs of large companies there, both tech and bio. And what we heard from people is, how do I get access to something like this?

謝謝，摩根。這是一個很棒的問題。我想說，反應非常非常強烈。我們與 NVIDIA 共同主辦的摩根大通演講中邀請了許多大型製藥公司的研發負責人。我們那裡有大公司的首席執行官，包括科技公司和生物公司。我們從人們那裡聽到的是，我如何獲得這樣的東西？

And we are doing the work now to increase the robustness of the LOWE platform. We're having conversations with potential partners around how we could put these tools in their capable hands in a way that would be helpful to Recursion and to the industry writ large.

我們現在正在努力提高 LOWE 平台的穩健性。我們正在與潛在合作夥伴進行對話，討論如何將這些工具交到他們有能力的手中，從而對 Recursion 和整個行業大有裨益。

As far as guidance around revenue, I don't think we're going to give guidance around revenue in the near term. What I will say is that we see the bigger opportunity in driving these companies towards really significant collaborations like the ones we've done with Bayer and Roche-Genentech, as they see the power of a tool like LOWE. Probably that's the bigger opportunity for us in the near term compared to sort of recurring software revenue. But we certainly will take all the revenue we can get if we're able to identify those questions.

就收入指導而言，我認為我們不會在短期內就收入提供指導。我要說的是，我們看到了推動這些公司進行真正重要合作的更大機會，就像我們與拜耳和羅氏基因泰克所做的那樣，因為他們看到了像 LOWE 這樣的工具的力量。與經常性軟體收入相比，這對我們來說可能是近期更大的機會。但如果我們能夠找出這些問題，我們肯定會獲得所有可以獲得的收入。

All right. Thank you. Next up, we have a question from Alec Stranahan of Bank of America who asks, how do you plan to utilize LOWE either internally or as an external offering? How does this fit into your existing full stack capabilities?

好的。謝謝。接下來，美國銀行的 Alec Stranahan 向我們提問，您計劃如何在內部或外部利用 LOWE？這如何適應您現有的全端功能？

This is actually a fantastic question because I think it highlights something that's really important. LOWE, internally at Recursion, is being used by certain teams on the BD side. And elsewhere, it certainly is something we think pharma could use.

這實際上是一個很棒的問題，因為我認為它強調了一些非常重要的事情。LOWE 在 Recursion 內部被 BD 方面的某些團隊使用。在其他地方，我們認為製藥公司當然可以使用它。

But I'll actually go to a slide from our other deck here to say that, internally, we actually believe there's a step beyond LOWE, where autonomous agents use a tool like LOWE to drive discovery as opposed to individual scientists, and I think this is a great example of this. This is a plot of thousands of targets in human biology. And what I'm showing you here on the Y-axis is how we've used a large language model that is based on public data sets, like the cancer dependency map, open targets, TCGA, et cetera, and we have profiled all of these different targets to assess their relevance in oncology.

但我實際上會從我們的另一張幻燈片上說，在內部，我們實際上相信有超越 LOWE 的一步，自主代理使用像 LOWE 這樣的工具來推動發現，而不是個別科學家，我認為這就是一個很好的例子。這是人類生物學中數千個目標的圖。我在 Y 軸上向您展示的是我們如何使用基於公共數據集的大型語言模型，例如癌症依賴圖、開放目標、TCGA 等，並且我們已經分析了所有這些不同的目標來評估它們在腫瘤學中的相關性。

Whereas on the X-axis, we've used a large language model that's looking only at proprietary data internal to Recursion. And so what you see on the top right is important targets like PIK3CA, BRAF, mTOR, EGFR, et cetera, where we see approved medicines for these targets in oncology. We see that these targets score robustly for oncology relevance based on both the public data and Recursion's proprietary data.

而在 X 軸上，我們使用了大型語言模型，該模型僅查看遞歸內部的專有資料。因此，您在右上角看到的是 PIK3CA、BRAF、mTOR、EGFR 等重要靶標，我們在其中看到針對這些腫瘤標靶的核准藥物。我們看到，根據公共數據和 Recursion 的專有數據，這些目標在腫瘤學相關性方面得分很高。

But we see hundreds of targets in the bottom right, in this blue box, that are now being automatically initiated as new pre-programs at Recursion without almost any human intervention based on our large language model scores. And we see these as targets that have the potential to be totally novel.

但我們在右下角的藍色框中看到數百個目標，這些目標現在作為遞歸的新預程式自動啟動，幾乎不需要基於我們的大型語言模型分數進行任何人工幹預。我們認為這些目標有可能是全新的。

And so at Recursion, our scientists aren't just using LOWE, they're really using robust workflows that are highly automated. And LOWE is more of a tool that we see to collaborate with partners that we see to drive partnership progress through our pipeline.

因此，在 Recursion，我們的科學家不僅使用 LOWE，他們還真正使用高度自動化的強大工作流程。LOWE 更多的是我們認為與合作夥伴合作的工具，我們認為透過我們的管道推動合作夥伴關係的進展。

All right. Next question is from Jesse Brodkin who asks, why did you choose Tempol or REC-994 for your CCM indication when the vitamin D data looked better in our preclinical screens?

好的。下一個問題來自 Jesse Brodkin，他問，當維生素 D 數據在我們的臨床前篩選中看起來更好時，為什麼您選擇 Tempol 或 REC-994 作為 CCM 適應症？

Thanks, Jesse, for that question. There's a circulation paper that you all can read about this work. And what we noticed was that both vitamin D and REC-994 had a robust response in the context of these preclinical models. However, REC-994's response was additive on top of vitamin D. So there was vitamin D in the chow of the mice, and the REC-994 treatment added to the effect that we saw.

謝謝傑西提出這個問題。有一份關於這項工作的流通論文，大家都可以閱讀。我們注意到，維生素 D 和 REC-994 在這些臨床前模型中都有強烈的反應。然而，REC-994 的反應是在維生素 D 的基礎上疊加的。

And given vitamin D is a very safe, widely available molecule that many people take in their every day, you get it when you stand out in the sun as well, we didn't see a lot of added value in us bringing that program forward. Whereas bringing REC-994, which was otherwise inaccessible to people, it was not approved, not available forward, we believe there was the potential for additive benefit. And that's why we've driven that program forward, and we're so excited to read out the data in Q3.

鑑於維生素 D 是一種非常安全、廣泛使用的分子，許多人每天都會攝入，當您站在陽光下時也會得到它，但我們在推進該計劃時並沒有看到很多附加價值。雖然人們無法獲得 REC-994，但它沒有獲得批准，也無法繼續使用，但我們相信它有可能帶來額外的好處。這就是我們推動該計劃向前發展的原因，我們很高興能夠讀出第三季的數據。

All right. It looks like, next, we've got a number of questions around our NVIDIA collaboration. The first from Harry Schoenberg at JPMorgan who asks, what involvement will you have with NVIDIA in the near future and going forward?

好的。接下來，我們似乎對 NVIDIA 合作提出了許多問題。第一個來自摩根大通 (JPMorgan) 的 Harry Schoenberg，他問道，在不久的將來以及未來您將與 NVIDIA 進行哪些合作？

That's a great question. I'll go back to the slide on our NVIDIA collaboration here. And just to reiterate that, with NVIDIA, we are really focused in three areas currently. The first is advanced computation. We've been working with the team there for many years. We think they're incredible, and they're helping us take the algorithms that we're building and help scale them, help tune them. And there's not many people in the world who have a lot of experience training multi-billion parameter models, but there's a great team at NVIDIA that's done just that. And so we are collaborating really closely on some of our larger models.

這是一個很好的問題。我將在這裡回顧我們與 NVIDIA 合作的幻燈片。重申一下，對於 NVIDIA，我們目前真正專注於三個領域。首先是高級計算。我們已經與那裡的團隊合作多年。我們認為它們令人難以置信，它們正在幫助我們採用我們正在建立的演算法並幫助擴展它們，以幫助調整它們。世界上沒有多少人擁有訓練數十億參數模型的豐富經驗，但 NVIDIA 有一個出色的團隊做到了這一點。因此，我們在一些較大的模型上進行了非常密切的合作。

What's more, we've already demonstrated the use of our priority access from NVIDIA in our expansion of our BioHive supercomputer. And of course, there's the potential for us to access the DGX Cloud resources in a priority way as well. And then finally, we see the potential for us to put potentially additional tools on their BioNeMo marketplace as we continue to develop these tools. And who knows? The collaboration with NVIDIA is very, very close, and we know that our teams are constantly coming up with new ideas. And we'll be excited to try some of those out with our colleagues there in the near future.

此外，我們已經在 BioHive 超級電腦的擴展中演示了 NVIDIA 優先存取的使用。當然，我們也有可能優先存取 DGX 雲端資源。最後，隨著我們繼續開發這些工具，我們看到了在 BioNeMo 市場上放置額外工具的潛力。誰知道呢？與 NVIDIA 的合作非常非常密切，我們知道我們的團隊不斷提出新的想法。我們將很高興在不久的將來與我們的同事一起嘗試其中的一些內容。

Next question is from Mark Simmons who asks, describe the relationship and investment with NVIDIA regarding AI and their products. I think we've really hit on this one already, so I will move on.

下一個問題來自 Mark Simmons，他要求描述與 NVIDIA 在 AI 及其產品方面的關係和投資。我認為我們已經真正解決了這個問題，所以我將繼續前進。

Okay. The next question is anonymous. This is a good one. Why have insiders been selling shares each month? Do they not have confidence in the company?

好的。下一個問題是匿名的。這是一件好事。為什麼內部人士每個月都在拋售股票？他們對公司沒有信心嗎？

That's a great question, and I'm glad we're addressing it. So I'll speak for myself because I think most people look to the CEO when it comes to insider buys and sells. And in 2023, I traded a very small -- relatively small number of shares. In fact, it was roughly about 4% of my holdings that were traded.

這是一個很好的問題，我很高興我們能解決這個問題。所以我會為自己說話，因為我認為大多數人在涉及內線交易時都會關注執行長。2023 年，我交易了非常少量的股票。事實上，我所持股份的大約 4% 被交易了。

And so all of these trades were done using 10b5-1 pre-planned sales and purchases. And again, I traded roughly 4% of my holdings. If you were to look at that at the grand scale, just on our volume today, all of the trades I did in 2023 represent roughly 6% or 7% of just the volume Recursion traded today in the market. And so I think while you see many of these sales, many of these purchases across insiders, the reality is that the magnitude of these is relatively small, and we're using these for making sure that we've got the right diversification in place.

因此，所有這些交易都是使用 10b5-1 預先計劃的銷售和採購來完成的。我再次交易了大約 4% 的持股。如果你從宏觀角度來看，就我們今天的交易量而言，我在 2023 年所做的所有交易大約佔 Recursion 今天市場交易量的 6% 或 7%。因此，我認為，雖然你看到許多這樣的銷售，許多內部人士的購買，但現實是這些的規模相對較小，我們正在利用這些來確保我們已經實現了正確的多元化。

This is my first job out of grad school, and so I have the vast majority of my shares that I've had from the beginning, the vast majority of my shares that I had at the IPO, and I intend to keep the vast majority of my shares moving forward because I definitely believe in what we're building here. I've dedicated my life and my career to it.

這是我研究生畢業後的第一份工作，所以我擁有我從一開始就擁有的絕大多數股票，我在 IPO 時擁有的絕大多數股票，而且我打算保留絕大多數股票我的股票繼續前進，因為我絕對相信我們在這裡建立的東西。我為此奉獻了我的一生和我的事業。

Next up, we've got questions in our fibrosis project. So Alec Stranahan asks, fibrosis has been a historically challenging area for development. This is true. How is the asset you in-licensed differentiated? And what are the first disease areas of focus?

接下來，我們的纖維化項目有一些問題。亞歷克·斯特拉納漢 (Alec Stranahan) 問道，纖維化一直是歷史上具有挑戰性的發展領域。這是真實的。您授權的資產如何與眾不同？首先關注的疾病領域是什麼？

Well, Alec, I really appreciate that question. I'm not going to share the first disease area of focus yet because the novel target we're working on, we think, has the potential to be useful in multiple different areas. And so we're going to probably hold that information back from a competitive standpoint for a while.

好吧，亞歷克，我真的很感激這個問題。我不打算分享第一個重點關注的疾病領域，因為我們認為，我們正在研究的新目標有可能在多個不同領域發揮作用。因此，從競爭的角度來看，我們可能會暫時保留這些資訊。

What I will say is the differentiation here is that we used a very complex assay. We essentially looked for small molecules that were mimicking the effect of Pentraxin-2 in a complex fibrocyte assay. And what we saw was a number of molecules. Since then, we've really optimized one of those molecules, 1169575, and additional molecules that we're advancing as backups. And we think this novel mechanism, and if you knew the mechanism, I could tell you more, but we're not going to share it yet, has a lot of potential to modulate the immune response that could be broadly useful across this space.

我要說的是，這裡的差異在於我們使用了非常複雜的測定法。我們主要尋找在複雜的纖維細胞測定中模擬 Pentraxin-2 效果的小分子。我們看到的是許多分子。從那時起，我們真正優化了其中一個分子，1169575，以及我們作為備份推進的其他分子。我們認為這種新穎的機制，如果你知道這個機制，我可以告訴你更多，但我們不打算分享它，它有很大的潛力來調節免疫反應，在這個領域可能廣泛有用。

So we're aware of the challenging development space. We certainly could imagine partnering this program as we get into the Phase 2 portion of the clinical trials. But we think this one is important and worth advancing because we're unaware of anybody else taking this target or this target class forward in the context of modulating the immune system to drive a reduction in fibrosis.

所以我們意識到充滿挑戰的發展空間。當我們進入臨床試驗的第二階段時，我們當然可以想像與該計畫合作。但我們認為這一目標很重要且值得推進，因為我們不知道有其他人在調節免疫系統以推動減少纖維化的背景下推進這一目標或這一目標類別。

All right. The next question comes from Jesse Brodkin who asked, did Recursion pay Bayer any money to obtain the fibrotic disease lead candidate from the collaboration?

好的。下一個問題來自 Jesse Brodkin，他問道，Recursion 是否向拜耳支付了任何費用來從合作中獲得纖維化疾病的主要候選藥物？

So Jesse, this program was advanced under our original fibrosis collaboration, and specific disclosures around the financial terms can be found in the 10-K. And we'll be filing that 10-K here in the next 48 hours or so. So you can look there.

Jesse，這個計畫是在我們最初的纖維化合作下推進的，有關財務條款的具體披露可以在 10-K 中找到。我們將在接下來的 48 小時左右的時間內在這裡提交 10-K。所以你可以去那裡看看。

But what I will say is we didn't have to pay anything upfront. There's some modest milestones that we think are very attractive as we drive this program forward. And I think both we and the scientific team at Bayer are pretty excited to see what we can do with Target Epsilon.

但我要說的是我們不需要預先支付任何費用。在我們推動這個計劃的過程中，有一些我們認為非常有吸引力的里程碑。我認為我們和拜耳的科學團隊都非常興奮地看到我們可以用 Target Epsilon 做什麼。

All right. The next question, back to Morgan Brennan from CNBC, and Morgan asks, what proof points can you share on AI, ML in medicine? And are AI applications in drug discovery happening as quickly and effectively as you anticipated?

好的。下一個問題，回到 CNBC 的 Morgan Brennan，Morgan 問道，關於醫學領域的人工智慧、機器學習，您能分享哪些證據？人工智慧在藥物發現中的應用是否像您預期的那樣快速有效？

Morgan, it's a great question. So I will share that I'm a founder, and I don't think any founder is ever satisfied with the pace that anything is advancing. So I can say no, things aren't going as fast as I would have liked. But I think if you look back at where Recursion started in 2013, where other companies like us started, and where we are today, we now have developed at Recursion multiple tools that are state of the art in terms of target identification, in terms of making ADME and tox predictions. We have a pipeline of five programs in Phase 2 or nearing Phase 2. I think we can be really proud of the platform we've built, the pipeline we've built, the partnerships we've built.

摩根，這是一個很好的問題。因此，我將分享我是創辦人，我認為沒有任何創辦人會對任何事情的進展速度感到滿意。所以我可以說不，事情進展得沒有我希望的那麼快。但我認為，如果你回顧一下Recursion 在2013 年開始的地方、像我們這樣的其他公司開始的地方以及我們今天的處境，我們現在已經在Recursion 開發了多種工具，這些工具在目標識別方面是最先進的，在進行 ADME 和毒性預測。我們在第二階段或接近第二階段有五個項目。我認為我們可以為我們建立的平台、我們建立的管道、我們建立的合作關係感到非常自豪。

Some of our partnerships are not only -- our Roche-Genentech partnership is not only the largest partnership in techbio today, it's one of the largest partnerships ever disclosed in biopharma in terms of total kind of bio box potential. And so I think that while the next 12 to 24 months is going to feel to all of us like we've kind of underdelivered, we're on this sort of exponential curve where if we look back in five years to 10 years, we're going to be amazed at how far things go.

我們的一些合作關係不僅是——我們的羅氏-基因泰克合作夥伴關係不僅是當今科技生物領域最大的合作夥伴關係，而且就生物盒總潛力而言，它是生物製藥領域迄今為止披露的最大的合作夥伴關係之一。因此，我認為，雖然未來 12 到 24 個月我們所有人都會感覺我們的交付不足，但我們正處於這種指數曲線上，如果我們回顧 5 年到 10 年，我們會發現我們將會驚訝於事情發展到如此程度。

But the reality is, like with any new technology, it takes time. And if we run these virtuous cycles and we get 1% or 2% better each time, but we can compound those efficiencies through many, many cycles, I think over time, we're going to see a fundamental transformation of the biopharma space that over a decade is going to feel much more profound than most people believe today.

但現實是，就像任何新技術一樣，這需要時間。如果我們運行這些良性循環，每次都會提高 1% 或 2%，但我們可以透過很多很多循環來提高效率，我認為隨著時間的推移，我們將看到生物製藥領域發生根本性轉變十年後的感受將比大多數人今天所認為的更加深刻。

All right. Next up, we have a question from Curtis Maxwell who asks, what is the backlog of projects that are in the pipeline for AI analysis? And what is the cost per project and duration typically?

好的。接下來，柯蒂斯·麥克斯韋 (Curtis Maxwell) 提出了一個問題，他問，正在醞釀的人工智慧分析專案積壓了多少？每個項目的成本和持續時間通常是多少？

So here, we can actually look at -- if you're referring, Curtis, to our programs at Recursion, we can share some of these statistics. We believe at Recursion that we're trying to shape this traditional V of the biopharma industry into more of a T, where, one day, we will be able to take all of our prior data and our algorithmic approach and predict the right molecule for each patient and drive it all the way to the market without any attrition.

所以在這裡，我們實際上可以看看——柯蒂斯，如果你指的是我們在 Recursion 的程序，我們可以分享其中一些統計數據。我們相信，在 Recursion，我們正在努力將生物製藥行業的傳統 V 型轉變為 T 型，有一天，我們將能夠利用所有先前的數據和演算法方法，預測正確的分子每位患者並將其一路推向市場，沒有任何損耗。

Now, that T is going to be impossible to actually completely achieve, but we want to move in that direction. And you can see, compared to the industry average, Recursion already starting to shape our internal funnel to look more like that T and less like that V. And what we're able to demonstrate so far across our programs is that our cost to IND and our time to validated lead significantly outperform the industry averages.

現在，實際上完全實現這一目標是不可能的，但我們希望朝這個方向前進。你可以看到，與行業平均水平相比，遞歸已經開始塑造我們的內部漏斗，使其看起來更像 T，而不是 V。到目前為止，我們在整個專案中能夠證明的是，我們的 IND 成本和驗證先導化合物的時間顯著優於產業平均值。

What's next up is that we hope that we're going to be in a position to demonstrate at least meeting the probability of success of the industry averages with a faster time and higher scale for the size of our company, and every generation of future programs we hope will build on that. And one day, we hope to be able to demonstrate to the industry that we can increase the probability of success of our programs.

接下來我們希望能夠以更快的時間和更大的規模來證明我們公司的規模以及每一代未來的計劃至少能夠達到行業平均水平的成功概率我們希望以此為基礎。有一天，我們希望能夠向業界證明，我們可以提高專案成功的可能性。

And we can drive them forward not only in areas of unmet need in rare disease and oncology, where we can be first in disease potentially, but also one day to leverage this platform to fast follow at scale, to be able to take programs at Recursion that we can drive extraordinarily quickly based on the incredible science that's being done elsewhere in the industry. So a lot of good work to come there.

我們不僅可以推動他們在罕見疾病和腫瘤學等未滿足需求的領域前進，我們可以在疾病領域成為第一，而且有一天可以利用這個平台大規模快速跟進，以便能夠在 Recursion 上開展項目基於行業其他地方正在進行的令人難以置信的科學，我們可以以極快的速度行駛。所以有很多好的工作要做。

All right. Looks like we've got another question from one of our analysts here. Given the complexity and layering of data keeps growing on your platform, how would you define a proof of concept in a constantly moving platform?

好的。看來我們的一位分析師又提出了一個問題。鑑於平台上資料的複雜性和分層性不斷增長，您將如何在不斷變化的平台中定義概念驗證？

That's a great question, Gil, and I think this speaks to a difference in mentality across the tech and the bio industries. We believe that these virtuous cycles of learning and iteration must always be running. And that increases the challenge of keeping up with the latest tool -- the latest version of that tool. But we want to make sure that every program at Recursion uses the latest generation of every tool that we're building.

這是一個很好的問題，吉爾，我認為這說明了科技和生物產業的心態差異。我們相信，這些學習和迭代的良性循環必須始終運作。這增加了跟上最新工具（該工具的最新版本）的挑戰。但我們希望確保 Recursion 的每個程式都使用我們正在建立的每個工具的最新一代。

And that's why we talk about the generations of our clinical pipeline, the first-generation programs which are, by and large, focused in rare genetic diseases, before we had a chemistry team. So most of those first-generation programs are actually molecules where we used our ML and AI platform to identify a new opportunity for a known chemical entity. And you'll see in our second generation, you'll start to see the layering in of new chemistry and digital chemistry tools to these programs as we advance them forward.

這就是為什麼我們談論我們的幾代臨床管道，在我們擁有化學團隊之前，第一代計畫總體上專注於罕見遺傳疾病。因此，大多數第一代程式實際上都是分子，我們使用機器學習和人工智慧平台來為已知化學實體識別新的機會。在我們的第二代中，您將看到隨著我們推進這些項目，您將開始看到新的化學和數位化學工具在這些項目中的分層。

And as we run a third generation and a fourth generation in the future, I think you'll see, we hope, that this platform learns, that this platform improves, and that every generation of programs will have, on average, an increasing probability of success and, we hope, increasing impact.

當我們將來運行第三代和第四代時，我想你會看到，我們希望，這個平台能夠學習，這個平台會改進，並且平均而言，每一代程式的機率都會增加我們希望取得成功，並產生越來越大的影響。

All right. Let's go now to some investor and revenue questions. We've got a question here from Eric Joseph at JPMorgan. How should investors generally be thinking about the company's business model at this stage?

好的。現在讓我們來討論一些投資者和收入問題。摩根大通的艾瑞克‧約瑟夫向我們提出了一個問題。現階段投資人一般該如何思考公司的商業模式？

Eric, that's a fantastic question. At the end of the day, in our industry, the currency of impact, the currency of success is assets in the clinic. And I think that's why Recursion has not just focused on building software as a service, not just focused on our partnerships, but has a robust internal pipeline that we're advancing in a small -- in areas of small niche corners of biology with high unmet need and partnerships where we can go after large, intractable areas of biology.

艾瑞克，這是一個很棒的問題。歸根究底，在我們的產業中，影響力、成功的貨幣就是診所的資產。我認為這就是為什麼 Recursion 不僅專注於建立軟體即服務，不僅專注於我們的合作夥伴關係，而且擁有強大的內部管道，我們正在小範圍內推進，在生物學的小利基領域中具有高未滿足的需求和夥伴關係，我們可以在這些領域中探索廣闊而棘手的生物學領域。

We are always doing business experiments at Recursion. LOWE is a business experiment. PHENOM-1 was a business experiment. And we don't yet know how those will drive our business model per se. But what I'm confident in is that Recursion will always be focused in bringing new composition of matter into areas of biology with high unmet need or where we can drive down the costs of expensive molecules that have been advanced into the market. So I think you can count on that being at the core of what we're building at Recursion, but we're going to do all of that with a much more tech-focused mindset than I think many other companies in this space.

我們一直在 Recursion 上做商業實驗。LOWE 是一項商業實驗。PHENOM-1 是一項商業實驗。我們還不知道這些將如何推動我們的商業模式本身。但我相信，Recursion 將始終專注於將新的物質成分帶入需求未滿足的生物學領域，或者我們可以降低已進入市場的昂貴分子的成本。所以我認為你可以相信這是我們在 Recursion 構建的核心，但我們將以比我認為這個領域的許多其他公司更注重技術的心態來完成所有這一切。

All right. Back to Gil, one of our analysts. Do you anticipate that over time, more value will be created from the company's internal pipeline or through its partnerships?

好的。回到我們的分析師之一吉爾。您是否預期隨著時間的推移，公司的內部管道或合作夥伴關係將會創造更多價值？

Well, Gil, if we're talking about long term, I believe Recursion is going to generate much more value from our internal pipeline than our partnerships. We expect to generate significant value in our partnerships today. We signed these partnerships with Roche-Genentech and with Bayer because we saw them as having transformational potential for patients and the potential for extraordinary impact in areas of high unmet need.

好吧，吉爾，如果我們談論長期，我相信遞歸將從我們的內部管道中產生比我們的合作夥伴更多的價值。我們期望今天的合作關係能產生巨大的價值。我們與羅氏基因泰克和拜耳簽署了這些合作關係，因為我們認為它們對患者俱有變革潛力，並且有可能在高度未滿足的需求領域產生非凡影響。

But as each of those partnerships finishes, we expect to have learned what we need to as a company to be able to build our own internal pipeline into those more complex intractable therapeutic areas. And until every disease has a treatment, we won't rest. And so I think you can count on Recursion's internal pipeline being a robust primary driver of our growth if we're to look out over the intermediate and long term.

但隨著每個合作關係的結束，我們希望了解作為一家公司我們需要什麼，以便能夠在那些更複雜、棘手的治療領域建立我們自己的內部管道。在每種疾病都得到治療之前，我們不會休息。因此，我認為，如果我們要關注中長期，您可以依靠 Recursion 的內部管道作為我們成長的強大主要驅動力。

All right. Back to Eric Joseph at JPM. What's envisioned as its earliest and most significant lines of product revenue?

好的。回到摩根大通的艾瑞克·約瑟夫。其最早和最重要的產品收入線是什麼？

I assume that Eric's talking here about some of our software tools like LOWE. Eric, we're having lots of discussions with biopharma companies today about how we might integrate a tool like LOWE and our teams at Recursion with them. I think it's too early to talk about the significance of these lines of product revenue.

我認為 Eric 在這裡談論的是我們的一些軟體工具，例如 LOWE。Eric，今天我們與生物製藥公司進行了大量討論，討論如何將 LOWE 等工具以及我們 Recursion 的團隊與他們整合。我認為現在談論這些產品線收入的重要性還為時過早。

I don't think it's too early to talk about how Recursion leading the field, with tools like LOWE, is helping pull the industry forward, partnering with extraordinary companies like Roche-Genentech and Bayer to help move the entire industry forward. And I think over time, whether it's through the software offerings themselves or whether it's through new chemical entities that we discover with our partners or in our own pipeline, I think we're going to drive a tremendous amount of product revenue leveraging these tools.

我認為現在談論 Recursion 如何利用 LOWE 等工具引領該領域，幫助推動行業向前發展，並與羅氏基因泰克和拜耳等傑出公司合作，幫助推動整個行業向前發展，並不為時過早。我認為隨著時間的推移，無論是透過軟體產品本身，還是透過我們與合作夥伴或在我們自己的管道中發現的新化學實體，我認為我們將利用這些工具帶來大量的產品收入。

All right. Next up, we have a question from Kareem Harrison who asks, when will the company be profitable?

好的。接下來，卡里姆·哈里森 (Kareem Harrison) 提問，公司什麼時候才能獲利？

Well, that's a great question. I think we see the opportunity before us as a multitrillion-dollar opportunity with profound potential impact for patients. There are few industries today where, despite hundreds of thousands of really incredible scientists working really, really hard, on average, our industry still fails 90% of the time in the clinic. And what's more, I think, there are roughly 20 or 25 biotech and biopharma companies today with market caps above $100 billion.

嗯，這是一個很好的問題。我認為我們將擺在我們面前的機會視為一個價值數萬億美元的機會，對患者有深遠的潛在影響。如今，儘管有數十萬真正令人難以置信的科學家非常非常努力地工作，但平均而言，我們的行業仍然有 90% 的時間在臨床上失敗。更重要的是，我認為目前大約有 20 或 25 家生技和生物製藥公司的市值超過 1,000 億美元。

That kind of lack of condensation of these companies, I think, is pretty unique to biopharma. And so we believe that if you look out 10 to 20 years, there will be a much smaller number of biopharma companies, and those companies will look much more like Recursion does today than they will look like a traditional biopharma company, and we hope and expect to be one of those.

我認為，這些公司缺乏凝聚力，這對生物製藥來說是非常獨特的。因此，我們相信，如果你展望 10 到 20 年，生物製藥公司的數量將會少得多，而且這些公司看起來會更像 Recursion 今天的樣子，而不是傳統的生物製藥公司，我們希望期望成為其中之一。

And what that means is that we're going to lean into growth in the coming years. We're going to be good stewards of our capital, but we're going to lean into growth. And so because we see the magnitude of that opportunity, while we hope to decrease with the upcoming milestones and revenue, we hope to decrease the losses on a quarter-by-quarter basis in the intermediate term. In the long term, I don't think we're going to lean into maximizing profitability because we think there's a multitrillion-dollar prize and impact for hundreds of millions or billions of patients on the line over the coming decades.

這意味著我們將在未來幾年內實現成長。我們將成為資本的好管家，但我們將致力於成長。因此，因為我們看到了這個機會的重要性，雖然我們希望隨著即將到來的里程碑和收入而減少，但我們希望在中期逐季度減少損失。從長遠來看，我認為我們不會傾向於最大化獲利能力，因為我們認為在未來幾十年裡，將會有數萬億美元的獎金和對數億或數十億患者的影響。

All right. Next up, we've got a question from Juan Fernandez who asks, what is the company vision? What daily actions are being taken to achieve it?

好的。接下來，胡安費南德斯向我們提出了一個問題，他問公司的願景是什麼？為實現這一目標正在採取哪些日常行動？

Juan, this is a great question. We believe that the biology and chemistry are deterministic that, with the right data and the right technology tools, we will be able one day to predict how any biological and chemical interaction operate, not only in human cells, but in the human organism, and beyond the human organism in any living organism.

胡安，這是一個很好的問題。我們相信生物學和化學是確定性的，憑藉正確的數據和正確的技術工具，有一天我們將能夠預測任何生物和化學相互作用如何運作，不僅在人類細胞中，而且在人體有機體中，並且超越人類有機體中的任何生物體。

And our vision is to be the company that digitizes this space, that moves from wet lab one day entirely to dry lab, where our experiments are done only to validate the predictions we make at scale. And if we can achieve that vision, I think we have the potential to be one of the most impactful companies in the world.

我們的願景是成為一家將這一領域數位化的公司，有一天完全從濕實驗室轉變為乾實驗室，在那裡我們進行實驗只是為了驗證我們大規模做出的預測。如果我們能夠實現這個願景，我認為我們有潛力成為世界上最具影響力的公司之一。

And so how do we manifest this every day? Well, we have a Recursion mindset that we teach our team. We have events like Decoding Recursion. I just got back from one last week, where we bring new and tenured employees together for a couple of days to talk about how we can focus on the experiment we are here to run. We don't want to play the game everybody else is playing because we know what the probable outcome is. We want to play a different game. We want to test this idea that there could be a different way to discover and develop medicines, and so we push that into every person at Recursion.

那我們每天要如何體現這一點呢？嗯，我們教給我們的團隊一種遞歸思維方式。我們有諸如解碼遞歸之類的活動。我剛從上週的活動回來，我們將新員工和終身員工聚集在一起幾天，討論如何專注於我們在這裡進行的實驗。我們不想玩其他人正在玩的遊戲，因為我們知道可能的結果是什麼。我們想玩不同的遊戲。我們想要測試這個想法，即可能存在一種不同的方式來發現和開發藥物，因此我們將這個想法灌輸給 Recursion 的每個人。

We even push that into our partnerships, pushing our partners to adopt new tools, to adopt our workflows. And so it's very front and center at Recursion, and we lean into that vision every day. We still have All Hands every week at Recursion. I'm still a presenter at All Hands as often as I can be. And we bring together people to really lean into that vision, and we're not apologetic about it.

我們甚至將其納入我們的合作夥伴關係中，推動我們的合作夥伴採用新工具，採用我們的工作流程。因此，它是 Recursion 的前沿和中心，我們每天都致力於實現這一願景。我們仍然每週都會在 Recursion 上進行全員參與。我仍然盡可能地在全體會議上擔任主持人。我們將人們聚集在一起，真正致力於實現這個願景，我們對此並不感到抱歉。

We believe that somebody has to be trying to make this space not just a little bit better, but a lot better. And we're thankful not only that Recursion is doing that, but that there are many other techbio companies and many other companies in the biopharma industry who are making big bets on how the future could look extraordinarily different from how it looks today.

我們相信，必須有人努力讓這個空間不僅變得更好一點點，而且要好得多。我們不僅感謝 Recursion 正在這樣做，而且感謝許多其他科技生物公司和生物製藥行業的許多其他公司，他們都在押注未來將如何與今天截然不同。

All right. Next question from Steve Deckert, do you have a rough timeline of when you might submit an IND for Target Epsilon?

好的。Steve Deckert 提出的下一個問題是，您是否有一個粗略的時間表來說明何時可以提交 Target Epsilon 的 IND？

Thanks, Steve. We're entering IND-enabling studies at this time. We just advanced that program here in the last week or so. So I think we'll be able to give you a better timeline for that in the coming quarters. But I know the team knows that I'm never satisfied and that speed with quality is what we're aiming for with that program and every other one at Recursion.

謝謝，史蒂夫。我們目前正在進入 IND 支持研究。我們剛剛在上週左右推進了該計劃。因此，我認為我們將能夠在未來幾季為您提供更好的時間表。但我知道團隊知道我永遠不會滿意，速度和品質是我們對該程式以及 Recursion 中所有其他程式的目標。

All right. Now, we have a question from Steven Greenwood who asks, would you consider looking at multiple sclerosis and the issue of remyelination?

好的。現在，史蒂文·格林伍德向我們提問，您會考慮研究多發性硬化症和髓鞘再生問題嗎？

Steven, that's a great question. And certainly, I can't talk about the specific areas of neuroscience that we may collaborate on with Roche-Genentech. But what I will say is that an important limitation of the platform that we built today at Recursion is that it is not yet built, I think, to build models of complex multi-organ systems or tissue systems. It's really built today to understand in a very deep way cell-type autonomous biological mechanisms, and we're working on that. We've got spheroid models and organoid models, both internal at Recursion and potentially through partnerships that we could be working on in the future that think will move us in that direction.

史蒂文，這是一個很好的問題。當然，我無法談論我們可能與羅氏基因泰克合作的神經科學的具體領域。但我要說的是，我們今天在 Recursion 上建立的平台的一個重要限制是，我認為它尚未建立用於建立複雜的多器官系統或組織系統的模型。今天它的真正建立是為了非常深入地理解細胞類型的自主生物機制，我們正在為此努力。我們已經有了球體模型和類器官模型，既有 Recursion 內部的模型，也有可能透過我們未來可能開展的合作夥伴關係，這將推動我們朝這個方向發展。

But if I'm very honest today, I don't think Recursion would be best suited to go after MS or remyelination, though certainly, we'll be working with our partners at Roche and Genentech to take this platform in whatever direction they're most excited to drive it. We certainly know that there's a high degree of unmet need in that space.

但如果我今天很誠實的話，我認為 Recursion 並不是最適合治療多發性硬化症或髓鞘再生，不過當然，我們將與羅氏和基因泰克的合作夥伴合作，將該平台朝他們的任何方向發展。我們最興奮的是駕駛它。我們當然知道該領域存在高度未滿足的需求。

All right. Next up, we have a question from Steven Ma who asks, on causal AI modeling with Tempus data, will it be used for internal drug discovery efforts or partners or both? Any change in BD discussions post-Tempus and any economics?

好的。接下來，Steven Ma 提出了一個問題，他問，關於使用 Tempus 數據進行因果人工智慧建模，它將用於內部藥物發現工作或合作夥伴或兩者兼而有之？Tempus 和經濟學後 BD 討論有什麼變化嗎？

Great. So Steven, the economics are both in the deck here and in our filings. I'll let you take a look at those just for the sake of time because we're almost out of time here. What I do want to say, though, is absolutely, we will be driving our causal AI models for our own internal programs, as well as for closely partnered programs at Recursion.

偉大的。史蒂文，經濟學問題既在我們的報告中，也在我們的文件中。我會讓你看一下這些只是為了節省時間，因為我們在這裡的時間已經不多了。不過，我想說的是，我們絕對會為我們自己的內部程序以及 Recursion 的緊密合作程序驅動因果人工智慧模型。

So for example, in the context of our oncology collaboration with Bayer or our oncology collaboration with Roche-Genentech, we are able to train models on the Tempus data that we deploy for specific programs with those partners. What we're not allowed to do is to resell the data or memorize the data from Tempus and act as a conduit without a real skin in the game partnership. But when we've got a deep, robust partnership like we do with Roche-Genentech or with Bayer, we are absolutely allowed to take those learnings and advance them forward.

例如，在我們與拜耳的腫瘤學合作或與羅氏基因泰克的腫瘤學合作的背景下，我們能夠利用我們為這些合作夥伴的特定項目部署的 Tempus 數據來訓練模型。我們不允許做的是轉售數據或記住來自 Tempus 的數據，並充當沒有真正參與遊戲合作夥伴關係的管道。但是，當我們像與羅氏基因泰克或拜耳那樣建立深入、牢固的合作關係時，我們絕對可以吸收這些經驗並推動它們向前發展。

And one thing I'll say, this year, we acquired two companies, we built exciting new foundation models, we signed the Tempus deal, and we've been very upfront with our partners that we intend, whenever possible, to bring all of those updates into our collaborations as fast as possible because we're incentivized to drive medicines to patients with our partners.

我要說的一件事是，今年，我們收購了兩家公司，我們建立了令人興奮的新基礎模型，我們簽署了Tempus 協議，我們一直非常坦率地與我們的合作夥伴合作，我們打算盡可能將所有盡快將這些更新納入我們的合作中，因為我們有動力與合作夥伴一起向患者提供藥物。

All right, last couple questions here. Here, we are looking at CCM. Gil's asking, what can you guide, if any, on the upcoming CCM readout?

好吧，最後幾個問題在這裡。在這裡，我們關注的是 CCM。Gil 詢問，對於即將發布的 CCM 讀數，您能提供什麼指導（如果有的話）？

Gil, all we're guiding at this time is that we're going to have preliminary, or I should say top-line safety, tolerability, and exploratory efficacy coming out in Q3. And we're excited. We hope, of course, that those data are positive, that they lead us to be able to advance that program forward for this important area of unmet need. But we know that regardless of what those data are, they're going to help us improve our platform and to learn and grow as a company.

吉爾，我們目前的指導是，我們將在第三季推出初步的，或者我應該說最重要的安全性、耐受性和探索性療效。我們很興奮。當然，我們希望這些數據是正面的，它們引導我們能夠針對這個未滿足需求的重要領域推進該計劃。但我們知道，無論這些數據是什麼，它們都將幫助我們改進我們的平台並幫助我們學習和成長。

Next up, from NK, how is Recursion thinking about commercializing its CCM program if the data is very positive?

接下來，NK 表示，如果數據非常積極，Recursion 將如何考慮將其 CCM 專案商業化？

Great question, NK. We've got a broad commercialization strategy here at Recursion. We do believe that some of our early programs, if they are successful, could be robust opportunities for us to outlicense, sell, or otherwise partner those programs so that we can bring money back into the company to create a self-sustaining platform.

好問題，NK。我們在 Recursion 制定了廣泛的商業化策略。我們確實相信，我們的一些早期項目如果成功，可能會成為我們對外許可、出售或以其他方式合作這些項目的絕佳機會，以便我們可以將資金帶回公司以創建一個自我維持的平台。

Because unlike many other biopharma companies who are focused on one or two exciting programs, we believe that, on average, every program at Recursion should be better than the one before it. And if we truly believe that, we should be willing to sell or license our early programs if they're successful in order to subsidize and pay for the next five, the next 10, the next 20 programs that we advance at Recursion. And so CCM could be a good candidate for that.

因為與許多其他專注於一兩個令人興奮的項目的生物製藥公司不同，我們相信，平均而言，Recursion 的每個項目都應該比之前的項目更好。如果我們真的相信這一點，我們應該願意出售或授權我們的早期程序（如果它們成功的話），以便補貼和支付我們在Recursion 中推進的接下來的5 個、接下來的10 個、接下來的20 個程序。因此，CCM 可能是一個很好的候選人。

Now, over the intermediate or longer term, we'll have to see how the rest of the industry moves. We've been generally disappointed with the adoption of some of these technology tools until very, very recently, really until the last 12 to 18 months, where it feels like the industry is finally starting to get really excited about the potential for ML and AI. And so depending on how fast the industry goes, we may decide one day to actually take our programs forward and commercialize them.

現在，從中期或長期來看，我們必須看看行業其他公司的趨勢。我們對其中一些技術工具的採用普遍感到失望，直到最近，直到最近 12 到 18 個月，感覺業界終於開始對 ML 和 AI 的潛力感到非常興奮。因此，根據行業發展的速度，我們可能會決定有一天真正推進我們的專案並將其商業化。

But I can tell you, if we do that, it is very unlikely we will commercialize those programs the way it's done today. I think we see lots of opportunities, and you're starting to see even larger companies like Lilly doing a direct-to-payer, direct-to-consumer kind of play, and I could imagine Recursion focusing on something like a membership model to drive the incentives to physician in favor of the patients and in favor of using all of Recursion 's molecules. But we're really talking about the intermediate to long term there.

但我可以告訴你，如果我們這樣做，我們就不太可能像今天這樣將這些項目商業化。我認為我們看到了很多機會，你開始看到像禮來這樣的更大的公司在做直接面向付款人、直接面向消費者的業務，我可以想像遞歸專注於會員模式之類的東西激勵醫生有利於患者並有利於使用Recursion 的所有分子。但我們真正談論的是中長期。

All right. Looks like the team has sent over a final question due to time. If your company was an animal, what animal would it be? This question comes from [Journey] Gray. We'll end on a funny note.

好的。由於時間原因，團隊似乎已經發送了最後一個問題。如果你的公司是一隻動物，你會是什麼動物？這個問題來自【征途】格雷。我們將以一個有趣的方式結束。

Obviously, our company would be an octopus. And you can see here, when we got our first Phase 2 program, our first patient dosed in our first Phase 2, I promised to the company that I would get a tattoo to mark that milestone, which we hope will be the first of many. And the octopus plays a very important internal role at Recursion, and I think it's the perfect animal for us. So thank you, Journey, for that funny last question.

顯然，我們公司就是一隻章魚。你可以在這裡看到，當我們獲得第一個第二階段計劃時，我們的第一個患者在我們的第一個第二階段中接受了給藥，我向公司承諾我會紋身來標記這個里程碑，我們希望這將是許多里程碑中的第一個。章魚在 Recursion 中扮演著非常重要的內部角色，我認為它對我們來說是完美的動物。謝謝你，旅程，最後一個有趣的問題。

Well, I hope everybody enjoyed this first earnings -- learnings call at Recursion. We intend to do this over the coming quarters. And we got a lot of potential milestones in 2024 and beyond. So I think these are going to be really exciting.

好吧，我希望每個人都喜歡這第一個收益——遞歸的學習電話會議。我們打算在未來幾季做到這一點。我們在 2024 年及以後實現了許多潛在的里程碑。所以我認為這些將會非常令人興奮。

I'm going to have other executives join me on future learning calls. And if you have suggestions, ways we can make this better, we want this to be adaptive. We want this to be accessible, and so please reach out with that feedback on our social media platforms.

我將讓其他高階主管加入我的未來學習電話會議。如果您有建議，或者我們可以做得更好的方法，我們希望它具有適應性。我們希望這一點能夠被訪問，因此請在我們的社交媒體平台上提供回饋。

Thanks everybody for tuning in, and I look forward to seeing you again really, really soon.

感謝大家的收聽，我期待很快能再次見到你們。