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🔹 AI drug discovery, validated. Recursion recently announced the first clinical validation of its AI-enabled drug discovery platform – positive Phase 1b/2 data for the ongoing TUPELO study evaluating its AI-identified drug, REC-4881, in Familial Adenomatous Polyposis (FAP). This progressive rare disease affects more than 50,000 people in the...

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Recursion

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🚀 How Recursion used AI to identify a novel approach for potentially treating the rare disease Familial Adenomatous Polyposis (FAP). A new video shares the story of Recursion’s investigational drug REC-4881 for the rare disease FAP which affects more than 50,000 people in the US and EU5 and has no approved medicines. Patients with this disease develop hundreds to thousands of polyps in their early teens to 20s that, if left untreated, have a nearly 100% risk of developing into colon cancer. ⬇️ Here’s how we did it: ▪️ Using the Recursion OS platform to analyze cell morphology, we tested thousands of compounds to find drugs that restored FAP-diseased cells to a healthy state. ▪️ This led us to REC-4881 — a drug that blocks a biological pathway called MEK1/2 – a new approach for treating FAP that had not been investigated clinically before. ▪️ Now, we’re using the full Recursion OS 2.0 platform — including our ClinTech real-world evidence – to fully understand disease burden and guide our clinical strategy, allowing us to expand patient eligibility for our trial. 💡 The takeaway: This program is the first clinical validation of the Recursion OS, and demonstrates our ability to translate unbiased phenotypic insights into potentially differentiated treatments for diseases with high unmet need. 👉 Read the full summary data in our December 8, 2025 press release here:
2:23

🚀 How Recursion used AI to identify a novel approach for potentially treating the rare disease Familial Adenomatous Polyposis (FAP). A new video shares the story of Recursion’s investigational drug REC-4881 for the rare disease FAP which affects more than 50,000 people in the US and EU5 and has no approved medicines. Patients with this disease develop hundreds to thousands of polyps in their early teens to 20s that, if left untreated, have a nearly 100% risk of developing into colon cancer. ⬇️ Here’s how we did it: ▪️ Using the Recursion OS platform to analyze cell morphology, we tested thousands of compounds to find drugs that restored FAP-diseased cells to a healthy state. ▪️ This led us to REC-4881 — a drug that blocks a biological pathway called MEK1/2 – a new approach for treating FAP that had not been investigated clinically before. ▪️ Now, we’re using the full Recursion OS 2.0 platform — including our ClinTech real-world evidence – to fully understand disease burden and guide our clinical strategy, allowing us to expand patient eligibility for our trial. 💡 The takeaway: This program is the first clinical validation of the Recursion OS, and demonstrates our ability to translate unbiased phenotypic insights into potentially differentiated treatments for diseases with high unmet need. 👉 Read the full summary data in our December 8, 2025 press release here:

Recursion

11,994 Aufrufe • vor 9 Monaten

🚀 Reshaping drug discovery – and the future of medicine – with AI. Recursion CFO Ben Taylor joined the Opto Sessions podcast from CMC Aureon to share how Recursion uses AI, lab automation, biological data, and machine learning to improve the way new medicines are discovered and developed. 💠 They discuss: ▪️How pairing AI with lab automation across biology, chemistry, and clinical technology allows us to move beyond the ~10–12% of the genome currently addressed by approved or in-trial medicines and explore the vast realm of untapped biology. ▪️How Recursion is advancing REC-4881 for patients with familial adenomatous polyposis (FAP) — a progressive condition with no approved medical therapies — after achieving Phase 2 proof-of-concept and demonstrating meaningful reductions in polyp burden. ▪️How Recursion is compressing timelines and improving efficiency with high-quality, ML-ready data to design better molecules with ~90% fewer experimental chemistries and to run smarter, faster clinical trials. 👉 Check out the full conversation:
0:58

🚀 Reshaping drug discovery – and the future of medicine – with AI. Recursion CFO Ben Taylor joined the Opto Sessions podcast from CMC Aureon to share how Recursion uses AI, lab automation, biological data, and machine learning to improve the way new medicines are discovered and developed. 💠 They discuss: ▪️How pairing AI with lab automation across biology, chemistry, and clinical technology allows us to move beyond the ~10–12% of the genome currently addressed by approved or in-trial medicines and explore the vast realm of untapped biology. ▪️How Recursion is advancing REC-4881 for patients with familial adenomatous polyposis (FAP) — a progressive condition with no approved medical therapies — after achieving Phase 2 proof-of-concept and demonstrating meaningful reductions in polyp burden. ▪️How Recursion is compressing timelines and improving efficiency with high-quality, ML-ready data to design better molecules with ~90% fewer experimental chemistries and to run smarter, faster clinical trials. 👉 Check out the full conversation:

Recursion

98,892 Aufrufe • vor 4 Monaten

Early data suggests Recursion (RXRX)’s AI-native platform is dramatically reducing the time and cost of drug discovery. Will it turn the stock around? Shea Wihlborg shares her thoughts in our March webinar.
0:51

Early data suggests Recursion (RXRX)’s AI-native platform is dramatically reducing the time and cost of drug discovery. Will it turn the stock around? Shea Wihlborg shares her thoughts in our March webinar.

ARK Funds

30,583 Aufrufe • vor 7 Monaten

Today, Recursion reported our 4Q/FY25 business updates and financial results. As CEO and President Najat Khan said, “Recursion has reached an inflection point: moving from proving that AI can participate in drug discovery to demonstrating that an AI-native operating system can generate clinical proof and durable value.” This includes: 🚀 A 5th milestone with Sanofi for a first-in-class Sanofi-partnered oncology program against a historically difficult and novel biological space. This brings the total upfront and progress-based milestones to $134 million to date. Five Recursion discovery program packages have been accepted to date, establishing a growing joint portfolio of novel AI-driven small molecules for immunology and oncology. 🚀 The first AI-enabled clinical validation of the Recursion OS platform in our FAP program, demonstrating translation from AI-driven biological insight to meaningful patient outcomes. We also have multiple clinical and preclinical programs advancing with defined milestones. 🚀 New preclinical efficacy data for REC-7735, a potential best-in-class PI3K⍺ H1047R inhibitor, precision designed with 242 compounds synthesized from first novel hit to REC-7735 in 10 months using the Recursion OS platform. REC-7735 demonstrates >100-fold selectivity for the H1074R mutation over WT PI3K⍺ suggesting potential improved tolerability over current inhibitors and is currently in IND-enabling studies. 🚀 $754 million of cash and cash equivalents. We exceeded our original cost savings guidance and now expect runway into early 2028, without additional financing. 👉 Read the full business updates here: 🔹 Tune in to our Earnings Call today, February 25, 2026 at 8:00 am ET / 6:00 am MT / 1:00 pm GMT, here on X, or on: ▪️ YouTube: ▪️ LinkedIn:
0:24

Today, Recursion reported our 4Q/FY25 business updates and financial results. As CEO and President Najat Khan said, “Recursion has reached an inflection point: moving from proving that AI can participate in drug discovery to demonstrating that an AI-native operating system can generate clinical proof and durable value.” This includes: 🚀 A 5th milestone with Sanofi for a first-in-class Sanofi-partnered oncology program against a historically difficult and novel biological space. This brings the total upfront and progress-based milestones to $134 million to date. Five Recursion discovery program packages have been accepted to date, establishing a growing joint portfolio of novel AI-driven small molecules for immunology and oncology. 🚀 The first AI-enabled clinical validation of the Recursion OS platform in our FAP program, demonstrating translation from AI-driven biological insight to meaningful patient outcomes. We also have multiple clinical and preclinical programs advancing with defined milestones. 🚀 New preclinical efficacy data for REC-7735, a potential best-in-class PI3K⍺ H1047R inhibitor, precision designed with 242 compounds synthesized from first novel hit to REC-7735 in 10 months using the Recursion OS platform. REC-7735 demonstrates >100-fold selectivity for the H1074R mutation over WT PI3K⍺ suggesting potential improved tolerability over current inhibitors and is currently in IND-enabling studies. 🚀 $754 million of cash and cash equivalents. We exceeded our original cost savings guidance and now expect runway into early 2028, without additional financing. 👉 Read the full business updates here: 🔹 Tune in to our Earnings Call today, February 25, 2026 at 8:00 am ET / 6:00 am MT / 1:00 pm GMT, here on X, or on: ▪️ YouTube: ▪️ LinkedIn:

Recursion

11,878 Aufrufe • vor 8 Monaten

.Peptone is a biotech company that uses NVIDIA GPUs on AWS infrastructure for AI-driven drug discovery. They are working towards the goal of developing drugs against not only tumors but also inflammatory diseases and neurodegenerative diseases. AWS cloud operations. The ability to scale from zero to hundreds of GPUs in a matter of minutes. NVIDIA's reliability, availability, and cost of GPUs. All aiding Peptone in AI-driven drug discovery.
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.Peptone is a biotech company that uses NVIDIA GPUs on AWS infrastructure for AI-driven drug discovery. They are working towards the goal of developing drugs against not only tumors but also inflammatory diseases and neurodegenerative diseases. AWS cloud operations. The ability to scale from zero to hundreds of GPUs in a matter of minutes. NVIDIA's reliability, availability, and cost of GPUs. All aiding Peptone in AI-driven drug discovery.

AWS Startups

2,166,706 Aufrufe • vor 4 Monaten

Demis Hassabis explains Isomorphic Labs' 10x drug discovery engine, and why solving disease could also create a multi-hundred-billion-dollar business "If you ask me, the number one thing AI can do for humanity, it would be to solve hundreds of terrible diseases. I can't imagine a better use case for AI." "If you could revolutionize the drug discovery process, make it 10x faster and more efficient, and more likely to pass through the clinical trials because you can predict the properties better, that has to also be of enormous commercial value too." "I hope to do both with Isomorphic." "Build a multi-hundred-billion-dollar business. I think it has that potential, as well as being incredibly beneficial for society and humanity." The real health AI prize is not another chatbot. It is compressing the path from a predicted drug property to a clinical trial, then turning that speed into treatments for hundreds of diseases. Demis is making a rare dual bet: the most valuable AI company in biology may also deliver AI's most valuable public benefit. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind and founder of Isomorphic Labs, on Bloomberg TV
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Demis Hassabis explains Isomorphic Labs' 10x drug discovery engine, and why solving disease could also create a multi-hundred-billion-dollar business "If you ask me, the number one thing AI can do for humanity, it would be to solve hundreds of terrible diseases. I can't imagine a better use case for AI." "If you could revolutionize the drug discovery process, make it 10x faster and more efficient, and more likely to pass through the clinical trials because you can predict the properties better, that has to also be of enormous commercial value too." "I hope to do both with Isomorphic." "Build a multi-hundred-billion-dollar business. I think it has that potential, as well as being incredibly beneficial for society and humanity." The real health AI prize is not another chatbot. It is compressing the path from a predicted drug property to a clinical trial, then turning that speed into treatments for hundreds of diseases. Demis is making a rare dual bet: the most valuable AI company in biology may also deliver AI's most valuable public benefit. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind and founder of Isomorphic Labs, on Bloomberg TV

Karl Mehta

47,040 Aufrufe • vor 4 Monaten

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100. This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration. Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.
0:59

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100. This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration. Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.

Ian Miles Cheong

93,975 Aufrufe • vor 7 Monaten

Hello, my dear friends, we have some breaking news today! Our rentosertib (novel target that has never been in clinical trials before we picked it using AI, novel molecule designed using true generative AI with the published broadly used AI platform, targeting fibrosis and biology of aging) entered Phase III trial in Idiopathic Pulmonary Fibrosis. This is a major milestone in the life of every drug hunter - the ultimate test. To my knowledge, in our AI drug discovery industry, no other company took the drug for a novel target with a novel molecule into Phase III before so it is a big milestone for the entire industry. Of course, at Insilico we are no longer dependent on this program - we have 13 INDs and many clinical trials are ongoing internally and run by partners. But this program is very special as we also demonstrated the senomorphic properties of this drug and built the entire pipeline of molecules around this target. Please Wish Me Luck! Luck is the most important superpower in everything we do and if we succeed with this program and also demonstrate the antiaging effect - all of us will benefit. Godspeed, my friends and thanks to everyone who helped us along the way!
2:26

Hello, my dear friends, we have some breaking news today! Our rentosertib (novel target that has never been in clinical trials before we picked it using AI, novel molecule designed using true generative AI with the published broadly used AI platform, targeting fibrosis and biology of aging) entered Phase III trial in Idiopathic Pulmonary Fibrosis. This is a major milestone in the life of every drug hunter - the ultimate test. To my knowledge, in our AI drug discovery industry, no other company took the drug for a novel target with a novel molecule into Phase III before so it is a big milestone for the entire industry. Of course, at Insilico we are no longer dependent on this program - we have 13 INDs and many clinical trials are ongoing internally and run by partners. But this program is very special as we also demonstrated the senomorphic properties of this drug and built the entire pipeline of molecules around this target. Please Wish Me Luck! Luck is the most important superpower in everything we do and if we succeed with this program and also demonstrate the antiaging effect - all of us will benefit. Godspeed, my friends and thanks to everyone who helped us along the way!

Alex Zhavoronkov, PhD (aka Aleksandrs Zavoronkovs)

50,041 Aufrufe • vor 4 Monaten

NEW episode! Drug development has never been more expensive, in terms of output per dollar spent. This trend, called Eroom’s law, is surprising, considering the incredible technological advances in drug discovery, from genome sequencing to engineering to microscopy. On a new episode of the Works in Progress podcast, Ben Southwood and I talk to Ruxandra Teslo 🧬 about why this has happened and what can be done about it. We discuss how: • AI isn’t a magic bullet for drug discovery. Predictive models lack the physical human data, like individual variation and rare side effects, that can only be generated by actually running real-world clinical trials. • As scientists invent more effective drugs, it becomes harder to discover new treatments that can surpass past successes. This is known as the "Better than the Beatles" problem. • Biotech companies are increasingly moving their "first-in-human" trials to Australia because its simpler regulations allow researchers to test drug safety faster and cheaper than in the US. • Clinical trials can be made more efficient with various reforms including: embracing platform trials, allowing researchers to select from independent ethics boards, expanding the funding and validation of surrogate endpoints, increasing transparency by releasing regulatory correspondence from failed companies, and much more. Timestamps: 00:00:00 Eroom’s law and the paradox of drug development 00:08:03 How clinical trials actually work 00:10:23 The power and controversy of surrogate endpoints 00:14:01 How historical patent laws influenced trial timelines 00:22:46 The Australia advantage and regulatory drag 00:29:08 Institutional review boards (IRBs) and bureaucratic drag 00:32:21 Open science and successful reforms 00:41:49 Our wishlist for clinical trial reforms, and which reforms we *don’t* like 00:53:48 Why AI isn’t a magic bullet for drug discovery
56:13

NEW episode! Drug development has never been more expensive, in terms of output per dollar spent. This trend, called Eroom’s law, is surprising, considering the incredible technological advances in drug discovery, from genome sequencing to engineering to microscopy. On a new episode of the Works in Progress podcast, Ben Southwood and I talk to Ruxandra Teslo 🧬 about why this has happened and what can be done about it. We discuss how: • AI isn’t a magic bullet for drug discovery. Predictive models lack the physical human data, like individual variation and rare side effects, that can only be generated by actually running real-world clinical trials. • As scientists invent more effective drugs, it becomes harder to discover new treatments that can surpass past successes. This is known as the "Better than the Beatles" problem. • Biotech companies are increasingly moving their "first-in-human" trials to Australia because its simpler regulations allow researchers to test drug safety faster and cheaper than in the US. • Clinical trials can be made more efficient with various reforms including: embracing platform trials, allowing researchers to select from independent ethics boards, expanding the funding and validation of surrogate endpoints, increasing transparency by releasing regulatory correspondence from failed companies, and much more. Timestamps: 00:00:00 Eroom’s law and the paradox of drug development 00:08:03 How clinical trials actually work 00:10:23 The power and controversy of surrogate endpoints 00:14:01 How historical patent laws influenced trial timelines 00:22:46 The Australia advantage and regulatory drag 00:29:08 Institutional review boards (IRBs) and bureaucratic drag 00:32:21 Open science and successful reforms 00:41:49 Our wishlist for clinical trial reforms, and which reforms we *don’t* like 00:53:48 Why AI isn’t a magic bullet for drug discovery

Saloni

109,256 Aufrufe • vor 6 Monaten

Demis Hassabis says AI could do for drug discovery what AlphaFold did for proteins The first step is finding potential cures, but the bigger shift comes if AI also speeds up clinical trials "curing all disease within 10 to 20 years is not blocked by any known law of physics"
0:58

Demis Hassabis says AI could do for drug discovery what AlphaFold did for proteins The first step is finding potential cures, but the bigger shift comes if AI also speeds up clinical trials "curing all disease within 10 to 20 years is not blocked by any known law of physics"

Haider.

10,241 Aufrufe • vor 5 Monaten

Demis Hassabis, the Nobel Prize winner who runs Google DeepMind just described the most consequential project on earth, and most people have no idea it exists. The project is called Isomorphic Labs and the goal is to end the way drugs have been developed for the last century. Here is the problem it is trying to solve. Developing a single drug today takes an average of 10 years, costs billions of dollars, and fails 90 percent of the time before it ever reaches a patient. Of every 10 drugs that enter clinical trials, only one makes it through. The other nine years of work, the other billions of dollars, the other scientific careers, gone. Hassabis believes AI can collapse that entire process from identifying a disease target to designing a compound that binds to it, predicts how it behaves in the body, and minimizes side effects , end to end, on a computer, before a single experiment is run. The foundation is AlphaFold, the AI system that solved one of biology's hardest problems predicting the 3D structure of every protein in the human body and won him the Nobel Prize in Chemistry in 2024. But knowing a protein's shape is only one part of designing a drug. Isomorphic is building what Hassabis describes as adjacent systems , AlphaFold 3, AlphaFold 4, and now a unified model called IsoDDE , that take the next steps. From designing the actual chemical compound that binds to the protein, predicting its binding strength, identifying new pockets to target that no one has ever found before. IsoDDE more than doubles the accuracy of AlphaFold 3 on the hardest protein-ligand prediction benchmarks that exist. Isomorphic is already running 18 to 19 live drug programs, cardiovascular disease, cancer, immunology in partnership with Eli Lilly, Novartis, and Johnson and Johnson. The first human clinical trial of a fully AI-designed drug is expected by the end of 2026. If that trial succeeds, it will be the first time in history that a drug put into a human body was designed not by a team of chemists working for a decade but by an AI working for months. Hassabis's long-term vision is even more direct, one day you describe a disease, click a button, and a drug blueprint comes out the other side. AI will solve almost all diseases within 10 years.
1:20

Demis Hassabis, the Nobel Prize winner who runs Google DeepMind just described the most consequential project on earth, and most people have no idea it exists. The project is called Isomorphic Labs and the goal is to end the way drugs have been developed for the last century. Here is the problem it is trying to solve. Developing a single drug today takes an average of 10 years, costs billions of dollars, and fails 90 percent of the time before it ever reaches a patient. Of every 10 drugs that enter clinical trials, only one makes it through. The other nine years of work, the other billions of dollars, the other scientific careers, gone. Hassabis believes AI can collapse that entire process from identifying a disease target to designing a compound that binds to it, predicts how it behaves in the body, and minimizes side effects , end to end, on a computer, before a single experiment is run. The foundation is AlphaFold, the AI system that solved one of biology's hardest problems predicting the 3D structure of every protein in the human body and won him the Nobel Prize in Chemistry in 2024. But knowing a protein's shape is only one part of designing a drug. Isomorphic is building what Hassabis describes as adjacent systems , AlphaFold 3, AlphaFold 4, and now a unified model called IsoDDE , that take the next steps. From designing the actual chemical compound that binds to the protein, predicting its binding strength, identifying new pockets to target that no one has ever found before. IsoDDE more than doubles the accuracy of AlphaFold 3 on the hardest protein-ligand prediction benchmarks that exist. Isomorphic is already running 18 to 19 live drug programs, cardiovascular disease, cancer, immunology in partnership with Eli Lilly, Novartis, and Johnson and Johnson. The first human clinical trial of a fully AI-designed drug is expected by the end of 2026. If that trial succeeds, it will be the first time in history that a drug put into a human body was designed not by a team of chemists working for a decade but by an AI working for months. Hassabis's long-term vision is even more direct, one day you describe a disease, click a button, and a drug blueprint comes out the other side. AI will solve almost all diseases within 10 years.

Milk Road AI

36,062 Aufrufe • vor 6 Monaten

Recursion Announces New Clinical Data Across Two Potential First-in-Class Programs, Sanofi Partnership Milestone, and Oversubscribed $200M Offering Today, we announced important progress across our clinical pipeline, partnerships, and capital position—reflecting growing evidence of what our integrated AI-enabled drug discovery platform can deliver. 🚀 REC-4881 | Deep, durable polyp reductions in FAP In an expanded adult population (18+), 87% of patients achieved polyp burden reductions after 12 weeks (47% median), with 85% remaining below baseline after 12 weeks off treatment. In the upper GI—the area of greatest unmet need—92% achieved reductions (55% median), including two patients with near-complete reductions (≥98%). Of those with reductions, 92% remained below baseline after 12 weeks off treatment. With no approved pharmacologic therapies for FAP, we are targeting registrational trial initiation in 2H 2027, pending FDA discussions. Full data will be presented November 2 at CGA-IGC. 🚀 REC-1245 | Encouraging early activity in heavily pretreated cancer patients Our potential first-in-class oral RBM39 degrader demonstrated three partial responses (two confirmed in endometrial cancer; one unconfirmed in liver cancer) and 91.7% disease control (11/12) at doses ≥45 mg. No dose-limiting toxicities or treatment-related serious adverse events were observed at the selected 45 mg expansion dose. 🚀 Sanofi | First development candidate optioned Two challenging targets, one oral molecule. A potential first-in-class candidate for inflammatory disease, enabled by Recursion’s AI platform and small-molecule chemistry capabilities alongside Sanofi’s expertise. Phase 1 expected in 2027, marking the sixth progress-based milestone across our collaboration. 🚀 $200M Oversubscribed Follow-On Offering | Extending Runway Into 2029 Broad demand from leading healthcare specialists, long-only institutions, and growth investors, including Janus Henderson Investors, Invus, Longaeva Partners, ARK Invest, and Driehaus Capital Management. The financing supports advancement of our clinical pipeline, continued AI engine innovation, and strategic partnerships. Multiple independent proof points. Across diseases, programs, and partnerships. Powered by one integrated AI-enabled engine. Thank you to our employees, collaborators, clinical investigators, patients, and investors who make this progress possible. Links below 👇 Business Update: Sanofi announcement: Public offering:
0:55

Recursion Announces New Clinical Data Across Two Potential First-in-Class Programs, Sanofi Partnership Milestone, and Oversubscribed $200M Offering Today, we announced important progress across our clinical pipeline, partnerships, and capital position—reflecting growing evidence of what our integrated AI-enabled drug discovery platform can deliver. 🚀 REC-4881 | Deep, durable polyp reductions in FAP In an expanded adult population (18+), 87% of patients achieved polyp burden reductions after 12 weeks (47% median), with 85% remaining below baseline after 12 weeks off treatment. In the upper GI—the area of greatest unmet need—92% achieved reductions (55% median), including two patients with near-complete reductions (≥98%). Of those with reductions, 92% remained below baseline after 12 weeks off treatment. With no approved pharmacologic therapies for FAP, we are targeting registrational trial initiation in 2H 2027, pending FDA discussions. Full data will be presented November 2 at CGA-IGC. 🚀 REC-1245 | Encouraging early activity in heavily pretreated cancer patients Our potential first-in-class oral RBM39 degrader demonstrated three partial responses (two confirmed in endometrial cancer; one unconfirmed in liver cancer) and 91.7% disease control (11/12) at doses ≥45 mg. No dose-limiting toxicities or treatment-related serious adverse events were observed at the selected 45 mg expansion dose. 🚀 Sanofi | First development candidate optioned Two challenging targets, one oral molecule. A potential first-in-class candidate for inflammatory disease, enabled by Recursion’s AI platform and small-molecule chemistry capabilities alongside Sanofi’s expertise. Phase 1 expected in 2027, marking the sixth progress-based milestone across our collaboration. 🚀 $200M Oversubscribed Follow-On Offering | Extending Runway Into 2029 Broad demand from leading healthcare specialists, long-only institutions, and growth investors, including Janus Henderson Investors, Invus, Longaeva Partners, ARK Invest, and Driehaus Capital Management. The financing supports advancement of our clinical pipeline, continued AI engine innovation, and strategic partnerships. Multiple independent proof points. Across diseases, programs, and partnerships. Powered by one integrated AI-enabled engine. Thank you to our employees, collaborators, clinical investigators, patients, and investors who make this progress possible. Links below 👇 Business Update: Sanofi announcement: Public offering:

Recursion

636,399 Aufrufe • vor 27 Tagen

MARTIN SHKRELI ON AI DOOMERS: “You can turn off the computer. It's not that big of a deal.” “I don't think there'll be a single AI-relevant drug in the next 10 years. AI does not help drug discovery.”
1:14

MARTIN SHKRELI ON AI DOOMERS: “You can turn off the computer. It's not that big of a deal.” “I don't think there'll be a single AI-relevant drug in the next 10 years. AI does not help drug discovery.”

Godel Terminal

132,076 Aufrufe • vor 1 Monat

"A hypothesis from a model isn't a discovery yet. So, it goes back into the lab. The biology the model predicted is the biology we saw at the bench." A computational prediction alone does not cure a disease. In Part 4 of our platform series, Associate Principal Scientist Celia Fernandez explains how Recursion closes the loop to turn AI generated hypotheses into validated biological targets. Once our foundation models point to a potential target, we run the lab in the opposite direction. The hypothesis enters a validation cascade that becomes progressively more demanding and highly specific to the disease. This physical testing allows us to aggressively derisk our computational discoveries directly at the bench. We test these targets against the exact same high bar that our global pharma partners hold their own internal programs to. When the loop finally closes, we can confidently prove that the biology the machine learning model predicted matches the physical reality we see in the lab. 🚀Follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery. #TechBio #AIdrugdiscovery #AI #drugdiscovery
0:42

"A hypothesis from a model isn't a discovery yet. So, it goes back into the lab. The biology the model predicted is the biology we saw at the bench." A computational prediction alone does not cure a disease. In Part 4 of our platform series, Associate Principal Scientist Celia Fernandez explains how Recursion closes the loop to turn AI generated hypotheses into validated biological targets. Once our foundation models point to a potential target, we run the lab in the opposite direction. The hypothesis enters a validation cascade that becomes progressively more demanding and highly specific to the disease. This physical testing allows us to aggressively derisk our computational discoveries directly at the bench. We test these targets against the exact same high bar that our global pharma partners hold their own internal programs to. When the loop finally closes, we can confidently prove that the biology the machine learning model predicted matches the physical reality we see in the lab. 🚀Follow along over the coming weeks as we continue this series, covering how Recursion maps biology, optimizes precision drug design, and closes the gap between patient and discovery. #TechBio #AIdrugdiscovery #AI #drugdiscovery

Recursion

1,014,580 Aufrufe • vor 1 Monat

Artificial Intelligence is far more than a productivity tool. Its greatest potential lies in advancing knowledge and driving the next wave of scientific discovery. At the #RaisinaDialogue2026 in New Delhi, emphasised how AI can catalyse breakthroughs and drive innovations capable of transforming lives across the world.
2:52

Artificial Intelligence is far more than a productivity tool. Its greatest potential lies in advancing knowledge and driving the next wave of scientific discovery. At the #RaisinaDialogue2026 in New Delhi, emphasised how AI can catalyse breakthroughs and drive innovations capable of transforming lives across the world.

Smriti Z Irani

31,278 Aufrufe • vor 8 Monaten