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🆕!! AI and atomic-level structure prediction are accelerating drug discovery — turning a slow, empirical search process into something closer to molecular engineering. Joshua Meier @_jackdent co-founders Chai Discovery chat their new bio foundation model, Chai-2. It enables zero-shot antibody discovery in a single 24-well plate — with a... show more

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14,840 Aufrufe • vor 1 Jahr •via X (Twitter)

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No Priorsvor 1 Jahr

Youtube, or anywhere you get your podcasts

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Jake Mintzvor 1 Jahr

@saranormous @joshim5 @_jackdent @chaidiscovery Bullish on anything @_jackdent

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Robert Youssefvor 1 Jahr

@joshim5 @_jackdent @chaidiscovery sounds great, but let's not forget it still comes down to testing. lab work won't vanish overnight.

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Alex Promptervor 1 Jahr

@joshim5 @_jackdent @chaidiscovery sounds like a game changer in drug discovery. compressing lab work into weeks is huge. curious to see how it unfolds in practice, though.

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Exclusive: Inside $3.8B Chai Discovery After leaving OpenAI & Stripe, co-founders Josh Meier (Joshua Meier) and Jack Dent (Jack Dent) built a pioneering AI drug design model called Chai-1. Now? Their models are used by giants like Eli Lilly, Pfizer and Novartis. Their models design antibodies in hours, not years - with atomic-level accuracy confirmed in the lab. If they're right, we're closer than ever to curing complex diseases. We went to San Francisco to find out. They've raised $600M + from sarah guo Lachy Groom Fred Ehrsam Thrive Capital Sequoia Capital General Catalyst OpenAI Menlo Ventures Index Ventures Kleiner Perkins + more. 0:00 AI drug design - how is it possible? 1:32 Josh: Leaving OpenAI for biology 2:34 90% of drugs used to fail 3:24 An outside scientist's honest take 4:40 Chai-3: Drug design in hours not years 5:30 Why Eli Lilly bet on Chai 6:20 When will this reach patients? 8:56 A hopeful future: Alzheimer's & aging

Exclusive: Inside $3.8B Chai Discovery After leaving OpenAI & Stripe, co-founders Josh Meier (Joshua Meier) and Jack Dent (Jack Dent) built a pioneering AI drug design model called Chai-1. Now? Their models are used by giants like Eli Lilly, Pfizer and Novartis. Their models design antibodies in hours, not years - with atomic-level accuracy confirmed in the lab. If they're right, we're closer than ever to curing complex diseases. We went to San Francisco to find out. They've raised $600M + from sarah guo Lachy Groom Fred Ehrsam Thrive Capital Sequoia Capital General Catalyst OpenAI Menlo Ventures Index Ventures Kleiner Perkins + more. 0:00 AI drug design - how is it possible? 1:32 Josh: Leaving OpenAI for biology 2:34 90% of drugs used to fail 3:24 An outside scientist's honest take 4:40 Chai-3: Drug design in hours not years 5:30 Why Eli Lilly bet on Chai 6:20 When will this reach patients? 8:56 A hopeful future: Alzheimer's & aging

Sachin and Adam

95,842 Aufrufe • vor 20 Tagen

To date, finding a drug has been a process of guess & check… screening millions of molecules hoping one binds. Chai Discovery is changing the paradigm… describe the molecule you want, and the model designs it. Joshua Meier and Matt McPartlon took antibody hit rates from roughly 1 in 1,000 to 1 in 7. That's the jump from search to engineering. On this episode of Training Data, they get into where the field actually is, why 2024 was the moment to start, what drug discovery looks like when the computer does the design, and more. 00:00 Introduction 01:52 From Discovery to Design 03:25 Protein AI Breakthroughs Timeline 06:04 Why Start in 2024 10:13 Diffusion Models Intuition 11:41 Building the Avengers Team 15:22 Hit Rates and Scaling Laws 25:01 Molecular CAD Vision 25:24 Faster Design Loops 26:32 Future Drug Discovery 28:37 Platform Business Model 31:14 Partnering Reality Check 33:44 Data Flywheel Explained 37:16 Staying Ahead at Scale 39:44 Culture and What's Next

To date, finding a drug has been a process of guess & check… screening millions of molecules hoping one binds. Chai Discovery is changing the paradigm… describe the molecule you want, and the model designs it. Joshua Meier and Matt McPartlon took antibody hit rates from roughly 1 in 1,000 to 1 in 7. That's the jump from search to engineering. On this episode of Training Data, they get into where the field actually is, why 2024 was the moment to start, what drug discovery looks like when the computer does the design, and more. 00:00 Introduction 01:52 From Discovery to Design 03:25 Protein AI Breakthroughs Timeline 06:04 Why Start in 2024 10:13 Diffusion Models Intuition 11:41 Building the Avengers Team 15:22 Hit Rates and Scaling Laws 25:01 Molecular CAD Vision 25:24 Faster Design Loops 26:32 Future Drug Discovery 28:37 Platform Business Model 31:14 Partnering Reality Check 33:44 Data Flywheel Explained 37:16 Staying Ahead at Scale 39:44 Culture and What's Next

Pat Grady

32,400 Aufrufe • vor 13 Tagen

If you aren’t paying attention to the companies behind a new wave of AI-driven drug development, you may end up missing out on essential investment opportunities. One company that has caught my attention is Chai Discovery, co-founded in 2024 by Joshua Meier. After graduating from Harvard, Meier began working as a researcher at OpenAI during the development of GPT-1 and GPT-2. He then worked at Meta's generative biology group, where he co-led the development of ESM1, the first transformer protein-language model. Chai Discovery is currently one of the buzziest startups in AI drug development. In December, the company completed a Series B funding round, raising an additional $130 million at a valuation of $1.3 billion, cementing its unicorn status. Joshua shares his story on my Opening Bid Unfiltered podcast on Yahoo Finance, and what's next: Watch 📈Yahoo Finance: Listen: 🎙️Spotify: 🎧Apple:

If you aren’t paying attention to the companies behind a new wave of AI-driven drug development, you may end up missing out on essential investment opportunities. One company that has caught my attention is Chai Discovery, co-founded in 2024 by Joshua Meier. After graduating from Harvard, Meier began working as a researcher at OpenAI during the development of GPT-1 and GPT-2. He then worked at Meta's generative biology group, where he co-led the development of ESM1, the first transformer protein-language model. Chai Discovery is currently one of the buzziest startups in AI drug development. In December, the company completed a Series B funding round, raising an additional $130 million at a valuation of $1.3 billion, cementing its unicorn status. Joshua shares his story on my Opening Bid Unfiltered podcast on Yahoo Finance, and what's next: Watch 📈Yahoo Finance: Listen: 🎙️Spotify: 🎧Apple:

Brian Sozzi

35,654 Aufrufe • vor 6 Monaten

Chai Discovery cofounder Jack Dent says AI drug discovery hit a major inflection point in 2025. As a result, it's now being deployed at the biggest pharma companies as part of their core discovery engines. "Over the course of 2025, these models went from being research curiosities... to being able to design molecules without needing to fine-tune them on a lot of data." "You can really just prompt them in the way that you'd prompt an LLM with a target, maybe some disease you're going after. And they will design a number of candidates that would help do that, essentially." "A year, year and a half ago, basically none of this stuff worked." "In 2025 we really crossed the Rubicon. And now these technologies are actively being deployed in companies like Eli Lilly, Novartis, Pfizer. These are some of the most scientific, highest-tech companies in the world. And they're putting them into real drug programs and making them part of their core discovery engines."

Chai Discovery cofounder Jack Dent says AI drug discovery hit a major inflection point in 2025. As a result, it's now being deployed at the biggest pharma companies as part of their core discovery engines. "Over the course of 2025, these models went from being research curiosities... to being able to design molecules without needing to fine-tune them on a lot of data." "You can really just prompt them in the way that you'd prompt an LLM with a target, maybe some disease you're going after. And they will design a number of candidates that would help do that, essentially." "A year, year and a half ago, basically none of this stuff worked." "In 2025 we really crossed the Rubicon. And now these technologies are actively being deployed in companies like Eli Lilly, Novartis, Pfizer. These are some of the most scientific, highest-tech companies in the world. And they're putting them into real drug programs and making them part of their core discovery engines."

TBPN

29,183 Aufrufe • vor 1 Monat

"We have this long term vision of turning biology from something which is trial and error and experimental and make it something that looks more like an engineering discipline in the next century." New Episode with Jack Dent ! 0:00 Intro 02:28 Inside Stripe w/ Patrick Collison, at <100 People 08:45 Sam Altman role in Chai Discovery origin 16:20 Chai-2 Antibody Breakthrough 19:24 Approaching biology as an engineering problem 24:45 Using AI models to treat people on an individual scale 26:52 Robotics in labs 27:22 Pharma industry today 29:49 The clinical trial bottleneck 32:53 Personalized biology models & cancer vaccines 36:24 Longevity, peptides, and trillion-dollar drugs 39:02 Does it matter that the government cut exploratory science grants? 41:00 What next-gen Chai models will do 42:08 Can frontier AI labs build this themselves? 43:26 Biology is hard

"We have this long term vision of turning biology from something which is trial and error and experimental and make it something that looks more like an engineering discipline in the next century." New Episode with Jack Dent ! 0:00 Intro 02:28 Inside Stripe w/ Patrick Collison, at <100 People 08:45 Sam Altman role in Chai Discovery origin 16:20 Chai-2 Antibody Breakthrough 19:24 Approaching biology as an engineering problem 24:45 Using AI models to treat people on an individual scale 26:52 Robotics in labs 27:22 Pharma industry today 29:49 The clinical trial bottleneck 32:53 Personalized biology models & cancer vaccines 36:24 Longevity, peptides, and trillion-dollar drugs 39:02 Does it matter that the government cut exploratory science grants? 41:00 What next-gen Chai models will do 42:08 Can frontier AI labs build this themselves? 43:26 Biology is hard

Sabrina Halper

39,122 Aufrufe • vor 2 Monaten

We live in a golden age of biology. So why are people still dying from disease? Because discovery and development move slower than they should. Today, we’re partnering with Incyte to change that. Kosmos is now the first agent that can compress months of drug development into weeks, from the earliest stages of scientific discovery through to FDA approval. Incyte will be the first company to deploy it across their pipeline. Work that used to take a team of scientists months now happens in weeks. Patients can't wait, and neither can we.

We live in a golden age of biology. So why are people still dying from disease? Because discovery and development move slower than they should. Today, we’re partnering with Incyte to change that. Kosmos is now the first agent that can compress months of drug development into weeks, from the earliest stages of scientific discovery through to FDA approval. Incyte will be the first company to deploy it across their pipeline. Work that used to take a team of scientists months now happens in weeks. Patients can't wait, and neither can we.

Sam Rodriques

7,031,222 Aufrufe • vor 3 Monaten

How can generative AI and Robotics help advance drug discovery？ 🚀 Excited to introduce LUMI-lab! A foundation model-driven Self-Driving Lab (SDL) for autonomous ionizable lipid discovery in mRNA delivery 🤖🔍 🔬 What is LUMI-lab? LUMI-lab integrates molecular foundation models with autonomous robotic experiments to efficiently explore new LNPs (lipid nanoparticles, mRNA delivery vehicles) with minimal wet-lab data. 🔥 Key Highlights: - 🧠 Foundation model trained on 28M molecules using a three-step strategy: - Unsupervised pretraining to capture broad molecular knowledge - Continual pretraining to specialize in lipid-like molecules - Active learning fine-tuning within a closed-loop experimental system - 🤖 1,700+ new LNPs synthesized & tested across 10 iterative cycles - 🧪 Brominated lipids autonomously identified as a novel structural feature that enhances mRNA transfection—an insight previously unrecognized in LNP design - 🏆 20.3% in vivo CRISPR gene editing efficiency in lung epithelial cells—the highest reported for inhaled LNPs 🚀 Why it matters? LNPs are the backbone of mRNA therapeutics, yet discovery has been slow due to data scarcity. LUMI-lab shows that AI-powered autonomous labs can accelerate mRNA delivery innovation🚀💡 🌐 Beyond mRNA drugs, LUMI-lab exemplifies a scalable framework for AI-driven molecular discovery, pushing boundaries in material science & drug delivery. 📜 Read the preprint: 🔗 💻 Code available on GitHub: 🔗 #AI #DrugDiscovery #mRNA #LNP #SyntheticBiology 🙏 A huge team effort behind this work, with special appreciation to Bowen LI for driving the project. Kudos to Haotian Cui, @YueXu1995, Kuan Pang, Gen Li, and Bettycat567!

How can generative AI and Robotics help advance drug discovery？ 🚀 Excited to introduce LUMI-lab! A foundation model-driven Self-Driving Lab (SDL) for autonomous ionizable lipid discovery in mRNA delivery 🤖🔍 🔬 What is LUMI-lab? LUMI-lab integrates molecular foundation models with autonomous robotic experiments to efficiently explore new LNPs (lipid nanoparticles, mRNA delivery vehicles) with minimal wet-lab data. 🔥 Key Highlights: - 🧠 Foundation model trained on 28M molecules using a three-step strategy: - Unsupervised pretraining to capture broad molecular knowledge - Continual pretraining to specialize in lipid-like molecules - Active learning fine-tuning within a closed-loop experimental system - 🤖 1,700+ new LNPs synthesized & tested across 10 iterative cycles - 🧪 Brominated lipids autonomously identified as a novel structural feature that enhances mRNA transfection—an insight previously unrecognized in LNP design - 🏆 20.3% in vivo CRISPR gene editing efficiency in lung epithelial cells—the highest reported for inhaled LNPs 🚀 Why it matters? LNPs are the backbone of mRNA therapeutics, yet discovery has been slow due to data scarcity. LUMI-lab shows that AI-powered autonomous labs can accelerate mRNA delivery innovation🚀💡 🌐 Beyond mRNA drugs, LUMI-lab exemplifies a scalable framework for AI-driven molecular discovery, pushing boundaries in material science & drug delivery. 📜 Read the preprint: 🔗 💻 Code available on GitHub: 🔗 #AI #DrugDiscovery #mRNA #LNP #SyntheticBiology 🙏 A huge team effort behind this work, with special appreciation to Bowen LI for driving the project. Kudos to Haotian Cui, @YueXu1995, Kuan Pang, Gen Li, and Bettycat567!

Bo Wang

27,626 Aufrufe • vor 1 Jahr

This is WILD! Nvidia just launched something that could compress the most expensive process in medicine from 12 years to 12 months. BioNeMo Agent Toolkit is an open, agent-ready platform that turns AI agents into autonomous scientific workers, giving them the ability to run real drug discovery workflows instead of just generating ideas. And more than 50 companies are already using it, including Anthropic, OpenAI, Eli Lilly, Databricks, Snowflake, Dassault Systèmes and Schrödinger. Here is what it actually does. Traditional drug discovery costs an average of $2.6 billion per drug and takes over a decade. Most of that time and money goes into screening millions of compounds, designing proteins that bind to disease targets and running countless lab experiments to validate whether something works. BioNeMo agents now do all of that computationally before a single lab experiment begins. The demo Nvidia shared makes the speed impossible to ignore. An agent was asked to design 10 protein binders for PD-L1, a critical cancer immunotherapy target and it completed the full design, co-folding, scoring, and 3D structural analysis on GPU in under 90 seconds. What used to require weeks of wet lab work and PhD-level expertise now runs as a callable tool inside an AI workflow. The four core capabilities are virtual drug screening, protein binder design, genomic analysis, and medical imaging each one compressing tasks that previously took weeks into minutes. The institutional validation behind this is unusually strong. Nvidia and Eli Lilly announced a joint investment of up to $1 billion over five years to build a co-innovation lab running entirely on BioNeMo. The University of Washington's Institute for Protein Design is already running RosettaFold3 at 2x faster performance than the prior generation. And the market this unlocks is enormous, and Nvidia is sitting right at the center of it. The AI drug discovery market is projected to grow from $2.9 billion in 2026 to $13.8 billion by 2033 and McKinsey estimates generative AI could deliver $60 to $110 billion in annual economic value to pharma. Bullish on drug discovery!

This is WILD! Nvidia just launched something that could compress the most expensive process in medicine from 12 years to 12 months. BioNeMo Agent Toolkit is an open, agent-ready platform that turns AI agents into autonomous scientific workers, giving them the ability to run real drug discovery workflows instead of just generating ideas. And more than 50 companies are already using it, including Anthropic, OpenAI, Eli Lilly, Databricks, Snowflake, Dassault Systèmes and Schrödinger. Here is what it actually does. Traditional drug discovery costs an average of $2.6 billion per drug and takes over a decade. Most of that time and money goes into screening millions of compounds, designing proteins that bind to disease targets and running countless lab experiments to validate whether something works. BioNeMo agents now do all of that computationally before a single lab experiment begins. The demo Nvidia shared makes the speed impossible to ignore. An agent was asked to design 10 protein binders for PD-L1, a critical cancer immunotherapy target and it completed the full design, co-folding, scoring, and 3D structural analysis on GPU in under 90 seconds. What used to require weeks of wet lab work and PhD-level expertise now runs as a callable tool inside an AI workflow. The four core capabilities are virtual drug screening, protein binder design, genomic analysis, and medical imaging each one compressing tasks that previously took weeks into minutes. The institutional validation behind this is unusually strong. Nvidia and Eli Lilly announced a joint investment of up to $1 billion over five years to build a co-innovation lab running entirely on BioNeMo. The University of Washington's Institute for Protein Design is already running RosettaFold3 at 2x faster performance than the prior generation. And the market this unlocks is enormous, and Nvidia is sitting right at the center of it. The AI drug discovery market is projected to grow from $2.9 billion in 2026 to $13.8 billion by 2033 and McKinsey estimates generative AI could deliver $60 to $110 billion in annual economic value to pharma. Bullish on drug discovery!

Milk Road AI

23,155 Aufrufe • vor 1 Monat

Demis Hassabis just described the exact architecture he is building to cure all human disease and said the breakthrough will not come gradually. It will hit all at once. He is building a full platform to do it. Think of it as a dozen AlphaFold-level models, each one covering a different step in the drug discovery process, all stitched together into a single engine that can be pointed at almost any disease area on earth. AlphaFold solved protein structure. It then folded 200 million proteins in a single year. Hassabis is now building the equivalent of that for every other step like protein interactions, molecular binding, toxicity prediction, drug design, and clinical stratification. Each one its own AlphaFold. All of them connected. He is already testing it on disease profiles in pre-clinical stages right now. Most biotech analysts still model drug discovery timelines in decades. McKinsey published a report estimating AI could reduce discovery timelines by 25 to 50 percent. Hassabis is not describing a 25 percent improvement. He is describing a complete replacement of the process. Do not expect a slow drip of results. Expect nothing for a few years and then a sudden explosion where everything works at once. Demis is running the same play. At a scale that makes AlphaFold look like a warm-up act. (Watch the full interview on YouTube at Two Minute Papers channel)

Demis Hassabis just described the exact architecture he is building to cure all human disease and said the breakthrough will not come gradually. It will hit all at once. He is building a full platform to do it. Think of it as a dozen AlphaFold-level models, each one covering a different step in the drug discovery process, all stitched together into a single engine that can be pointed at almost any disease area on earth. AlphaFold solved protein structure. It then folded 200 million proteins in a single year. Hassabis is now building the equivalent of that for every other step like protein interactions, molecular binding, toxicity prediction, drug design, and clinical stratification. Each one its own AlphaFold. All of them connected. He is already testing it on disease profiles in pre-clinical stages right now. Most biotech analysts still model drug discovery timelines in decades. McKinsey published a report estimating AI could reduce discovery timelines by 25 to 50 percent. Hassabis is not describing a 25 percent improvement. He is describing a complete replacement of the process. Do not expect a slow drip of results. Expect nothing for a few years and then a sudden explosion where everything works at once. Demis is running the same play. At a scale that makes AlphaFold look like a warm-up act. (Watch the full interview on YouTube at Two Minute Papers channel)

Ihtesham Ali

101,315 Aufrufe • vor 1 Monat

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao Yuan Cao (formerly DeepMind) and Andrew Lew, Haiqian Yang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by Playground Global with participation from AIX, E14 Fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and Thomas Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building Unreasonable Labs 👇

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao Yuan Cao (formerly DeepMind) and Andrew Lew, Haiqian Yang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by Playground Global with participation from AIX, E14 Fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and Thomas Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building Unreasonable Labs 👇

Markus J. Buehler

55,052 Aufrufe • vor 5 Monaten

HAPPENING NOW: More than 20 homeless drug addicts are sleeping in Seattle's Occidental Square Friday evening. If you look closer, most have brand new tents and sleeping bags. One guy says "mutual aid" comes every weekend to feed them, supply them, and offer free "harm reduction" supplies like meth pipes and fentanyl foil. Now you know why these men and women have zero incentive to get off the streets. They are treated like spoiled children. The far-left activists are essentially keeping them in their suffering instead of offering them a way out into treatment and recovery. The Mayor(Mayor Katie B. Wilson) should really know what's happening on his streets. Center on Wealth & Poverty at Discovery Institute

Jonathan Choe

199,989 Aufrufe • vor 11 Monaten

Learn more about Boltz-2, the new open source AI model from MIT and Recursion capable of predicting protein binding affinity with unprecedented speed, scale and accuracy. ▪️ Boltz-2 is the first model to combine structure and binding affinity prediction, approaching the accuracy of physics-based free energy perturbation (FEP) calculations while being over 1,000 times faster and less computationally expensive. 💥 The model addresses a critical bottleneck in small molecule drug discovery. ▪️ A powerful tool built on novel machine learning: “By predicting both molecular structure and binding affinity simultaneously with unprecedented speed and scale, Boltz-2 gives R&D teams a powerful tool to triage more effectively and focus resources on the most promising compounds,” says Najat Khan, PhD, Chief R&D Officer and Chief Commercial Officer at Recursion. ▪️ The added power of open source: “Because Boltz-2 is open-source, including its training code, scientists can easily adapt it for specific types of molecules, making it even more powerful as a tool to accelerate discovery," says Regina Barzilay, PhD, MIT School of Engineering Distinguished Professor for AI and Health, AI faculty lead at MIT Jameel Clinic for AI & Health and MIT CSAIL principal investigator. 👉 Learn more, read the preprint, and access Boltz-2 here: 📅 Join us for live presentations, demos and discussions: 📎 MIT Presentation (Cambridge) – Monday, June 9. 📎 NVIDIA #GTC25 (Paris) – Wednesday, June 11. 📎 Molecular Machine Learning Conference - MoML (Montreal) – Tuesday, June 17.

Learn more about Boltz-2, the new open source AI model from MIT and Recursion capable of predicting protein binding affinity with unprecedented speed, scale and accuracy. ▪️ Boltz-2 is the first model to combine structure and binding affinity prediction, approaching the accuracy of physics-based free energy perturbation (FEP) calculations while being over 1,000 times faster and less computationally expensive. 💥 The model addresses a critical bottleneck in small molecule drug discovery. ▪️ A powerful tool built on novel machine learning: “By predicting both molecular structure and binding affinity simultaneously with unprecedented speed and scale, Boltz-2 gives R&D teams a powerful tool to triage more effectively and focus resources on the most promising compounds,” says Najat Khan, PhD, Chief R&D Officer and Chief Commercial Officer at Recursion. ▪️ The added power of open source: “Because Boltz-2 is open-source, including its training code, scientists can easily adapt it for specific types of molecules, making it even more powerful as a tool to accelerate discovery," says Regina Barzilay, PhD, MIT School of Engineering Distinguished Professor for AI and Health, AI faculty lead at MIT Jameel Clinic for AI & Health and MIT CSAIL principal investigator. 👉 Learn more, read the preprint, and access Boltz-2 here: 📅 Join us for live presentations, demos and discussions: 📎 MIT Presentation (Cambridge) – Monday, June 9. 📎 NVIDIA #GTC25 (Paris) – Wednesday, June 11. 📎 Molecular Machine Learning Conference - MoML (Montreal) – Tuesday, June 17.

Recursion

15,593 Aufrufe • vor 1 Jahr

Alex on why AI + fully autonomous labs will compress drug discovery from five years to two: "If scientists are armed with agentic infrastructure that has an intelligence that's a multiple of Einstein, they will do science faster, with higher probability, for lower cost, and have greater impact." "AI coming into the discovery of medicines is one of the biggest unlocks we will see in history because it means we're no longer constrained by our hands or minds. We can identify all n number of hypotheses that could exist, figure out the best experiment to run, and run those 24/7." "The sheer ability to be more comprehensive in our hypothesis set, and to run experimentation at unprecedented scale, means we're going to speed breakthroughs from timelines that take three to five years to timelines that take less than a couple of years."

Alex on why AI + fully autonomous labs will compress drug discovery from five years to two: "If scientists are armed with agentic infrastructure that has an intelligence that's a multiple of Einstein, they will do science faster, with higher probability, for lower cost, and have greater impact." "AI coming into the discovery of medicines is one of the biggest unlocks we will see in history because it means we're no longer constrained by our hands or minds. We can identify all n number of hypotheses that could exist, figure out the best experiment to run, and run those 24/7." "The sheer ability to be more comprehensive in our hypothesis set, and to run experimentation at unprecedented scale, means we're going to speed breakthroughs from timelines that take three to five years to timelines that take less than a couple of years."

Patrick OShaughnessy

39,883 Aufrufe • vor 3 Monaten

For the first time in human history, we are teaching a Foundation Model to master the diverse tasks of medicinal chemists, biologists, and computational scientists all in one place. In our latest collaboration with Liquid AI, we are moving away from fragmented, specialized tools toward a single, super-intelligent model. What surprised me most? This model isn't just performing at reasonable levels—it has started outperforming specialist models across physics-based tasks, imaging, and longitudinal data. Why this changes everything: -Synergy over Specialization: Fine-tuning on specific tasks has unlocked unexpected capabilities in synergetic areas, opening a new frontier in multimodal AI research. -Zero-Shot Potential: We are building a model that can perform out-of-scope tasks, moving us closer to an "AI deity" for drug discovery. -Quality First: The goal isn't just to bypass regulations to save time; it’s about using these synergies to develop better, more effective drugs. We are no longer just looking at linear regression or simple text; we are looking at the future of how humanity fights disease. #LiquidAI #InsilicoMedicine #GenerativeAI #DrugDiscovery #DeepTech #BiotechInnovation

For the first time in human history, we are teaching a Foundation Model to master the diverse tasks of medicinal chemists, biologists, and computational scientists all in one place. In our latest collaboration with Liquid AI, we are moving away from fragmented, specialized tools toward a single, super-intelligent model. What surprised me most? This model isn't just performing at reasonable levels—it has started outperforming specialist models across physics-based tasks, imaging, and longitudinal data. Why this changes everything: -Synergy over Specialization: Fine-tuning on specific tasks has unlocked unexpected capabilities in synergetic areas, opening a new frontier in multimodal AI research. -Zero-Shot Potential: We are building a model that can perform out-of-scope tasks, moving us closer to an "AI deity" for drug discovery. -Quality First: The goal isn't just to bypass regulations to save time; it’s about using these synergies to develop better, more effective drugs. We are no longer just looking at linear regression or simple text; we are looking at the future of how humanity fights disease. #LiquidAI #InsilicoMedicine #GenerativeAI #DrugDiscovery #DeepTech #BiotechInnovation

Alex Zhavoronkov, PhD (aka Aleksandrs Zavoronkovs)

10,544 Aufrufe • vor 4 Monaten

Welcome to the Lab of the Future! 🧬🤖 Excited to share LUMI-lab, out today in Cell — a self-driving platform that pairs an AI foundation model with a robotic lab to autonomously discover ionizable lipids (LNPs) for mRNA delivery. The core problem: Designing lipid nanoparticles (LNPs) is hard. The chemical space of ionizable lipids is vast, experimental cycles are slow, and — critically — historical LNP datasets are far too small to train a predictive model from scratch. Most AI approaches in this space hit a wall immediately: not enough data to learn from. Our solution: lab-in-the-loop foundation model learning. Instead of training on LNP data alone, LUMI starts as a transformer-based foundation model pretrained across broad chemical space, building rich molecular representations before it ever sees a single LNP experiment. Then it enters a closed loop with a robotic synthesis platform: predict → synthesize → assay → update. Each round of real wet-lab experiments fine-tunes the model, which then proposes smarter candidates for the next round. The lab isn't just validating AI predictions — it's actively teaching the model, continuously. What happened when we let it run: LUMI-lab autonomously synthesized and screened 1,700+ ionizable lipids in human bronchial epithelial cells. The top candidate — LUMI-6 — features a brominated lipid tail, a structural motif that had been largely overlooked in LNP design. LUMI found it without being told where to look. When formulated into LNPs and delivered intratracheally to mice, LUMI-6 achieved 20.3% gene editing efficiency in lung epithelial cells — a compelling result for one of the hardest-to-reach therapeutic targets, directly relevant to diseases like cystic fibrosis and alpha-1 antitrypsin deficiency. Why this matters beyond LNPs: This is a proof of concept for a broader thesis — that foundation model pretraining + active learning + robotic experimentation can overcome the data scarcity bottleneck that plagues AI-driven discovery in biology. You don't need a massive domain-specific dataset to start. You need a model that can generalize, a lab that can generate the right data, and a loop that connects them. Huge congratulations to first authors Yue Xu, Haotian Cui, and Kuan Pang, and to the entire Bowen LI team. Grateful to our collaborators at University Health Network and Leslie Dan Faculty of Pharmacy, and to Princess Margaret Cancer Centre Research Princess Margaret Cancer Centre Research. 📄 Paper:

Welcome to the Lab of the Future! 🧬🤖 Excited to share LUMI-lab, out today in Cell — a self-driving platform that pairs an AI foundation model with a robotic lab to autonomously discover ionizable lipids (LNPs) for mRNA delivery. The core problem: Designing lipid nanoparticles (LNPs) is hard. The chemical space of ionizable lipids is vast, experimental cycles are slow, and — critically — historical LNP datasets are far too small to train a predictive model from scratch. Most AI approaches in this space hit a wall immediately: not enough data to learn from. Our solution: lab-in-the-loop foundation model learning. Instead of training on LNP data alone, LUMI starts as a transformer-based foundation model pretrained across broad chemical space, building rich molecular representations before it ever sees a single LNP experiment. Then it enters a closed loop with a robotic synthesis platform: predict → synthesize → assay → update. Each round of real wet-lab experiments fine-tunes the model, which then proposes smarter candidates for the next round. The lab isn't just validating AI predictions — it's actively teaching the model, continuously. What happened when we let it run: LUMI-lab autonomously synthesized and screened 1,700+ ionizable lipids in human bronchial epithelial cells. The top candidate — LUMI-6 — features a brominated lipid tail, a structural motif that had been largely overlooked in LNP design. LUMI found it without being told where to look. When formulated into LNPs and delivered intratracheally to mice, LUMI-6 achieved 20.3% gene editing efficiency in lung epithelial cells — a compelling result for one of the hardest-to-reach therapeutic targets, directly relevant to diseases like cystic fibrosis and alpha-1 antitrypsin deficiency. Why this matters beyond LNPs: This is a proof of concept for a broader thesis — that foundation model pretraining + active learning + robotic experimentation can overcome the data scarcity bottleneck that plagues AI-driven discovery in biology. You don't need a massive domain-specific dataset to start. You need a model that can generalize, a lab that can generate the right data, and a loop that connects them. Huge congratulations to first authors Yue Xu, Haotian Cui, and Kuan Pang, and to the entire Bowen LI team. Grateful to our collaborators at University Health Network and Leslie Dan Faculty of Pharmacy, and to Princess Margaret Cancer Centre Research Princess Margaret Cancer Centre Research. 📄 Paper:

Bo Wang

57,510 Aufrufe • vor 5 Monaten

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: *Decode proteins to fight disease *Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: Decode proteins to fight disease Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

Konstantine Buhler

13,014 Aufrufe • vor 10 Monaten

Today, we expand zero-shot drug design beyond binding to the design of multifunctional medicines, the intracellular proteome, and state-of-the-art atomic precision with our model, JAM-2. In a new report (below), we show: 1. The first drug-grade, fully computationally designed multispecific antibodies against five peptide-MHCs: Routine picomolar T-cell activation/cell-killing EC50s, >100-fold selectivity, and drug-like developability 2. The first fully generatively designed, drug-grade dual-variant KRAS G12 multispecifics: They recruit primary T-cells from human donors to kill G12V and G12C presenting cells at pM to single-digit-nM potency, completely sparing wild-type. 3. Atomic accuracy, from sequence alone: Angstrom-level agreement between Cryo-EM and JAM-2 de novo designs, requiring only target sequences (not structure) as input. 4. Unrivaled speed with an AI-native in-house wet lab: Designed, built, and tested five programs in one parallelized campaign, end-to-end in-house in ~6 weeks. 5. A higher validation bar for AI-generated drug candidates: In a field increasingly rife with hype and uneven standards of proof, we provide the highest quality public wet-lab validation of AI-designed antibodies to date. We share experimental methods in full, and invite folks to adopt and build on these standards. Truly individualized therapies will be the most important contribution of AI in drug design. These advances help accelerate this future.

Today, we expand zero-shot drug design beyond binding to the design of multifunctional medicines, the intracellular proteome, and state-of-the-art atomic precision with our model, JAM-2. In a new report (below), we show: 1. The first drug-grade, fully computationally designed multispecific antibodies against five peptide-MHCs: Routine picomolar T-cell activation/cell-killing EC50s, >100-fold selectivity, and drug-like developability 2. The first fully generatively designed, drug-grade dual-variant KRAS G12 multispecifics: They recruit primary T-cells from human donors to kill G12V and G12C presenting cells at pM to single-digit-nM potency, completely sparing wild-type. 3. Atomic accuracy, from sequence alone: Angstrom-level agreement between Cryo-EM and JAM-2 de novo designs, requiring only target sequences (not structure) as input. 4. Unrivaled speed with an AI-native in-house wet lab: Designed, built, and tested five programs in one parallelized campaign, end-to-end in-house in ~6 weeks. 5. A higher validation bar for AI-generated drug candidates: In a field increasingly rife with hype and uneven standards of proof, we provide the highest quality public wet-lab validation of AI-designed antibodies to date. We share experimental methods in full, and invite folks to adopt and build on these standards. Truly individualized therapies will be the most important contribution of AI in drug design. These advances help accelerate this future.

Nabla Bio

179,160 Aufrufe • vor 1 Monat

"Mike - OMG, I just saw a promo for the new season of Deadliest Catch and you are SO YOUNG! I never knew you were actually in the show. Why aren’t you still? Congratulations on 20 years!" Denise Rigel Hi Denise Yeah, I saw that too. 20 years. Don’t they go by in a blink? The story of why I’m not the host of Deadliest Catch begins with the fact that 20 years ago, Dirty Jobs was not yet a hit on Discovery, and the network didn’t really know what to do with me. Back in 2004, the pilot episodes for DJ were considered to be “off-brand,” and Discovery had no plans to take the show to series. They liked me, though, and started sending me around the world on a series of expeditions. One of those trips was to a place called Dutch Harbor, Alaska, to bear witness to life on a crab boat. Well, bear witness I did. I was up there in October of 2004, hosting a show that was not yet a show, trying with the producers to figure out if we were filming a documentary, a reality show, a competition show, or a mini-series. It still wasn’t clear three months later, in January of 2005, when I returned to host the snow crab season. That was the year that Deadliest Catch, tragically, lived up to its name, when six men died in the course of filming the show. And that’s when everyone stepped back and said, “Wait…what is this thing, exactly? How can so much danger be an everyday part of getting a job done?” Later that year, Deadliest Catch and Dirty Jobs both went into full scale production, and cable television was forever changed. That's not hyperbole. The number of work-themed shows that evolved directly from the success of those two shows is stunning. Discovery, quite wisely, determined that Catch needed a narrator, not a host. Thus, all of my on-camera stuff was cut from Season 1. I was, however, invited to narrate the show, which I was honored to do. In fact, I’m headed to the studio right now to do a few more episodes for Season 20, which premieres tonight at 8pm, on Discovery. Check it out, and be reminded once again of a great and abiding truth, proven every year now for the last two decades – you can’t script The Bering Sea. A big thanks to the captains and their crews, for sharing their world with the rest of us. And to all of the producers and cameramen who made it happen. And to Discovery, for giving us a home. It’s a hell of an accomplishment.

The Real Mike Rowe

88,216 Aufrufe • vor 2 Jahren

I love RLM and there's still merit to some of the stuff they said about the prequels but yeah half their wants for the sequels came true and they were all bad choices. Also they seemed to lack foresight by praising the Star Trek reboot as being what the prequels should have been and to what precedent it was setting for the future of Star Trek. The irony being they would spend the next decade lamenting the path Star Trek took away from slow, thought provoking, dialog heavy conundrums when they praised the reboot for turning Star Trek into a fun but shallow lens flair filled, shaky camera action movie. A reboot that set the first stone of that path towards Discovery and Academy. And when the sequels took a lot of their wants for Star Wars and it was bad, they soft balled it and ignored or glossed over aspects they would have dedicated 20 minutes to for the prequels as "eh just enjoy the ride" which is the kind of thought process they complained about in stuff like Nerd Crew.

I love RLM and there's still merit to some of the stuff they said about the prequels but yeah half their wants for the sequels came true and they were all bad choices. Also they seemed to lack foresight by praising the Star Trek reboot as being what the prequels should have been and to what precedent it was setting for the future of Star Trek. The irony being they would spend the next decade lamenting the path Star Trek took away from slow, thought provoking, dialog heavy conundrums when they praised the reboot for turning Star Trek into a fun but shallow lens flair filled, shaky camera action movie. A reboot that set the first stone of that path towards Discovery and Academy. And when the sequels took a lot of their wants for Star Wars and it was bad, they soft balled it and ignored or glossed over aspects they would have dedicated 20 minutes to for the prequels as "eh just enjoy the ride" which is the kind of thought process they complained about in stuff like Nerd Crew.

LA\/ENDER

42,032 Aufrufe • vor 3 Monaten

There is a massive advantage to hiring young people, and Shopify was the first company to figure it out, says Pragmatic Engineer's Gergely Orosz. "Shopify's Head of Engineering Farhan Thawar told me that he saw something interesting years back. Shopify was so early to AI. They got Copilot licenses for everyone, with no cost limit. But they didn't see many teams using it." "But there was this one team that was using it a lot more. There was an intern on the team. They onboarded the intern and gave the intern his two-week task and the intern was done in a day. And they're like, 'How did you do that?' He's like, 'Oh well I just used AI? What's next?'" "And suddenly the people on the team felt stupid, right? So they started to learn from the intern and use AI." "The next thing Farhan did was hire an intern for every single Shopify team. This is why Shopify had a thousand interns."

There is a massive advantage to hiring young people, and Shopify was the first company to figure it out, says Pragmatic Engineer's Gergely Orosz. "Shopify's Head of Engineering Farhan Thawar told me that he saw something interesting years back. Shopify was so early to AI. They got Copilot licenses for everyone, with no cost limit. But they didn't see many teams using it." "But there was this one team that was using it a lot more. There was an intern on the team. They onboarded the intern and gave the intern his two-week task and the intern was done in a day. And they're like, 'How did you do that?' He's like, 'Oh well I just used AI? What's next?'" "And suddenly the people on the team felt stupid, right? So they started to learn from the intern and use AI." "The next thing Farhan did was hire an intern for every single Shopify team. This is why Shopify had a thousand interns."

TBPN

135,588 Aufrufe • vor 6 Monaten