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We’ve been developing AlphaFold 3 to advance how we do drug design at Isomorphic Labs. This model allows us to a) do rational structure based drug design in-silico, and b) understand more about the biological context of targets 1/

Max Jaderberg

18,705 subscribers

25,772 次观看 • 2 年前 •via X (Twitter)

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Max Jaderberg 的头像

Max Jaderberg2 年前

With AF3, our scientists are able to create and test hypotheses at the atomic level, and get predicted structures back within seconds, allowing our scientists to design small molecules with AlphaFold 3 predictions in a tight loop. 2/

Max Jaderberg 的头像

Max Jaderberg2 年前

Crucially for drug design, we find that AF3’s predictions can generalise to completely novel targets and mechanisms, such as this novel allosteric binding mode for a novel kinase inhibitor. 3/

Max Jaderberg 的头像

Max Jaderberg2 年前

A richer understanding of a novel target can be achieved by looking at the structure of targets in their full biological context, in complex with other protein binding partners, DNA, RNA, or ligand cofactors. 4/

Max Jaderberg 的头像

Max Jaderberg2 年前

We believe that this broader understanding of the biological context within which drug targets operate will translate into more effective drugs in the clinic. 5/

Max Jaderberg 的头像

Max Jaderberg2 年前

At @IsomorphicLabs we’re combining AlphaFold 3 with our other proprietary AI models that help us understand more about the properties, function, and dynamics of molecular systems. 6/

Max Jaderberg 的头像

Max Jaderberg2 年前

We’ll continue to be heads down in research, tackling the next frontier of fundamental modelling questions in chemistry and biology from first principles with AI, to change the way we design the next generation of therapeutics, and unlock new biology. 7/

Max Jaderberg 的头像

Max Jaderberg2 年前

Read more in our blog post

莊生夢蝶🦋 的头像

莊生夢蝶🦋2 年前

@IsomorphicLabs It is foreseeable that cancer will be conquered in the near future.

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We recently released a technical report highlighting how Isomorphic Labs’ drug design engine is achieving a step change in how we model complex biological mechanisms, consistently outperforming industry benchmarks. Read our blog to discover how we’re bridging the gap between structure prediction and rational drug design. You can find the link in the comments.

We recently released a technical report highlighting how Isomorphic Labs’ drug design engine is achieving a step change in how we model complex biological mechanisms, consistently outperforming industry benchmarks. Read our blog to discover how we’re bridging the gap between structure prediction and rational drug design. You can find the link in the comments.

Isomorphic Labs

125,156 次观看 • 4 个月前

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.

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

93,750 次观看 • 3 个月前

Excited to share Max Jaderberg's pod on Sequoia Capital's new Training Data ep! From Alphastar & Capture The Flag to AI drug discovery, Max shares his vision for building a holy grail model for drug design Isomorphic Labs, and the AF3 and Nobel Prize achievements along the way! ✨

Excited to share Max Jaderberg's pod on Sequoia Capital's new Training Data ep! From Alphastar & Capture The Flag to AI drug discovery, Max shares his vision for building a holy grail model for drug design Isomorphic Labs, and the AF3 and Nobel Prize achievements along the way! ✨

Stephanie Zhan

24,897 次观看 • 1 年前

I’ve always believed the No.1 application of AI should be to improve human health. That work started with AlphaFold, and now at Isomorphic Labs with the mission to reimagine drug discovery and one day solve all disease! We are turbocharging that goal with $2.1B in new funding.

I’ve always believed the No.1 application of AI should be to improve human health. That work started with AlphaFold, and now at Isomorphic Labs with the mission to reimagine drug discovery and one day solve all disease! We are turbocharging that goal with $2.1B in new funding.

Demis Hassabis

3,169,749 次观看 • 2 个月前

“Some of these research problems we’ve been going after really are holy grail predictive problems for drug design - and we’ve made some incredible breakthroughs there, which have really stunned our chemists.” On Sequoia Capital’s Training Data podcast, our Chief AI Officer Max Jaderberg talks about the scale of AI drug discovery and its potential to transform how we design new medicines. Listen to the full podcast at the link below:

“Some of these research problems we’ve been going after really are holy grail predictive problems for drug design - and we’ve made some incredible breakthroughs there, which have really stunned our chemists.” On Sequoia Capital’s Training Data podcast, our Chief AI Officer Max Jaderberg talks about the scale of AI drug discovery and its potential to transform how we design new medicines. Listen to the full podcast at the link below:

Isomorphic Labs

12,294 次观看 • 1 年前

The ambition at Isomorphic Labs is immense – they’re rethinking drug discovery from the ground up, using AI. 💊 Join Head of Medicinal Drug Design rebecca paul & Chief AI Officer Max Jaderberg as they share the possibilities of AI in tackling disease with host Hannah Fry. Watch their conversation ↓ Timecodes: 00:00 Intro 02:11 AI & Disease 05:30 AI in Biology 06:51 Molecules and Proteins 12:05 AlphaFold 3 14:40 Demo 16:20 Human-AI collaboration 24:30 Drug Design Challenges 39:00 Beyond Animal Models 44:35 AI Drug Future 46:30 Outro

The ambition at Isomorphic Labs is immense – they’re rethinking drug discovery from the ground up, using AI. 💊 Join Head of Medicinal Drug Design rebecca paul & Chief AI Officer Max Jaderberg as they share the possibilities of AI in tackling disease with host Hannah Fry. Watch their conversation ↓ Timecodes: 00:00 Intro 02:11 AI & Disease 05:30 AI in Biology 06:51 Molecules and Proteins 12:05 AlphaFold 3 14:40 Demo 16:20 Human-AI collaboration 24:30 Drug Design Challenges 39:00 Beyond Animal Models 44:35 AI Drug Future 46:30 Outro

Google DeepMind

78,008 次观看 • 1 年前

NEW EPISODE of HARD DRUGS! AlphaFold, ProteinMPNN and other AI tools are transforming biology and drug design. But how do they work? What can’t they do (yet)? And can we use them to make a vaccine against Strep A for the very first time? In this episode, Jacob and I talk about hacking proteins with AI. 03:21 What makes proteins cool 05:29 Can we make a vaccine against Strep A? 10:12 Proteins in nature aren’t enough, and we can improve them with AI 21:12 ProteinMPNN and AlphaFold are transforming drug design 29:52 A short history of protein structure prediction from physical models to deep learning 34:48 Using AI to design a Strep A vaccine 39:27 Validating an AI-designed Strep A vaccine 43:09 Recent AI protein breakthroughs 49:30 Why we don’t already have a Strep A vaccine and the future of optimizing protein drugs

NEW EPISODE of HARD DRUGS! AlphaFold, ProteinMPNN and other AI tools are transforming biology and drug design. But how do they work? What can’t they do (yet)? And can we use them to make a vaccine against Strep A for the very first time? In this episode, Jacob and I talk about hacking proteins with AI. 03:21 What makes proteins cool 05:29 Can we make a vaccine against Strep A? 10:12 Proteins in nature aren’t enough, and we can improve them with AI 21:12 ProteinMPNN and AlphaFold are transforming drug design 29:52 A short history of protein structure prediction from physical models to deep learning 34:48 Using AI to design a Strep A vaccine 39:27 Validating an AI-designed Strep A vaccine 43:09 Recent AI protein breakthroughs 49:30 Why we don’t already have a Strep A vaccine and the future of optimizing protein drugs

Saloni

72,565 次观看 • 9 个月前

Excited to show a glimpse of the next generation of AlphaFold from our teams at Isomorphic Labs and Google DeepMind. This model expands beyond proteins to include other molecules like small molecules and nucleic acids, and improves accuracy on proteins 1/n

Excited to show a glimpse of the next generation of AlphaFold from our teams at Isomorphic Labs and Google DeepMind. This model expands beyond proteins to include other molecules like small molecules and nucleic acids, and improves accuracy on proteins 1/n

Max Jaderberg

117,447 次观看 • 2 年前

Australian man used ChatGPT and AlphaFold to design a custom mRNA cancer vaccine for his rescue dog from scratch. He paid $3000 to sequence her tumour DNA, AI identified mutated proteins, matched them to drug targets. Tumour halved, dog is alive and happy! The ingenuity of the Australian people. So much talent, skill and intelligence in this country. We should be a tech powerhouse like Japan or Taiwan

Australian man used ChatGPT and AlphaFold to design a custom mRNA cancer vaccine for his rescue dog from scratch. He paid $3000 to sequence her tumour DNA, AI identified mutated proteins, matched them to drug targets. Tumour halved, dog is alive and happy! The ingenuity of the Australian people. So much talent, skill and intelligence in this country. We should be a tech powerhouse like Japan or Taiwan

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244,921 次观看 • 4 个月前

Most of The Browser Company's browser competitiors have begun to copy elements of Arc's design and features – sometimes pretty shamelessly. A lot of you have been asking us about it. How do we feel? Decided to answer head-on – here's how we're feeling about copycat competition...

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21,226 次观看 • 2 年前

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Jayneil Dalal

32,539 次观看 • 1 年前

"How did this drug do this? How did 350 milligrams of a white solid produce this effect? And then I realized the drug had almost nothing to do with it." — Sasha Shulgin, on his first psychedelic experience.

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Taylor Sterling

21,005 次观看 • 2 个月前

After relapsing on the only drug in development for my rare disease, my doctors told me ‘We’ve tried everything, there’s nothing more that we can do.’ To make things worse, I didn’t have a billion dollars and 10 years to develop a new drug from scratch. My only hope was to find a drug that was already FDA-approved that I could repurpose for my disease. It’s been over 9.5 years that I’ve been in remission and we have been able to identify and advance numerous treatments for diseases they weren’t initially intended for. Now through Every Cure we are utilizing artificial intelligence to maximize the potential of all existing drugs to treat every disease they possibly can. What rare disease would you like to find a repurposed drug to treat? #MedTwitter

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David Fajgenbaum, MD

138,669 次观看 • 2 年前

Today marks a pivotal moment for Isomorphic Labs. We have secured $2.1 Billion in our second external funding round, led by Thrive Capital. They are joined at the table by Alphabet, GV and new investors MGX, Temasek, CapitalG and the UK Sovereign AI Fund. This milestone accelerates our ability to build the pioneering novel AI models that power our AI drug design engine (IsoDDE) and deploy them at scale: delivering scientific breakthroughs with a precision previously thought impossible, accelerating and expanding our pipeline of therapeutic programs toward the clinic. All with the ultimate goal of delivering life-changing new medicines to patients. Moving forward, we will scale our drug candidate pipelines across multiple therapeutic areas, expand our global footprint, and push the boundaries of frontier AI research to power our drug design engine. Deeply grateful to everyone sharing our vision to solve all disease with AI. Let’s build the future of medicine. Read the full announcement here:

Today marks a pivotal moment for Isomorphic Labs. We have secured $2.1 Billion in our second external funding round, led by Thrive Capital. They are joined at the table by Alphabet, GV and new investors MGX, Temasek, CapitalG and the UK Sovereign AI Fund. This milestone accelerates our ability to build the pioneering novel AI models that power our AI drug design engine (IsoDDE) and deploy them at scale: delivering scientific breakthroughs with a precision previously thought impossible, accelerating and expanding our pipeline of therapeutic programs toward the clinic. All with the ultimate goal of delivering life-changing new medicines to patients. Moving forward, we will scale our drug candidate pipelines across multiple therapeutic areas, expand our global footprint, and push the boundaries of frontier AI research to power our drug design engine. Deeply grateful to everyone sharing our vision to solve all disease with AI. Let’s build the future of medicine. Read the full announcement here:

Isomorphic Labs

278,054 次观看 • 2 个月前

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.

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 次观看 • 3 个月前

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.

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