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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...

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Bo Wang

30,644 subscribers

27,626 Aufrufe • vor 1 Jahr •via X (Twitter)

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11 Kommentare

Profilbild von Melinda B. Chu
Melinda B. Chuvor 1 Jahr

Super cool! I’m going to try to include it in an upcoming project! 🤩👩🏻‍🔬🤖🦾

Profilbild von The Information
The Informationvor 1 Jahr

OpenAI is betting on a little-known startup to stay ahead of Elon Musk in the supercomputer race.

Profilbild von Derya Unutmaz, MD
Derya Unutmaz, MDvor 1 Jahr

This is incredible! 👏👏👏

Profilbild von Bo Wang
Bo Wangvor 1 Jahr

Thanks Derya for all your support!

Profilbild von Matt Greving
Matt Grevingvor 1 Jahr

Very nice!

Profilbild von Tigger
Tiggervor 1 Jahr

wow 🫡

Profilbild von Trevor Campbell
Trevor Campbellvor 1 Jahr

Woah… this is very cool! And very timely — There is a lot of LNP exploration and testing in my near future Will definitely be digging deep on this one!

Profilbild von ashutosh
ashutoshvor 1 Jahr

@AdrianoAguzzi Greetings, this looks like the right robot for your lab? thank you.

Profilbild von Prasad Kothari (he/him/his)
Prasad Kothari (he/him/his)vor 1 Jahr

Great work. Congratulations & all the best. Good to see Lumi working on drug discovery.

Profilbild von J Sam🌐
J Sam🌐vor 1 Jahr

Is CAD also opensourced?

Profilbild von Sabeer Saeed
Sabeer Saeedvor 1 Jahr

Super Outstanding!

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Noah B. Price

152,192 Aufrufe • vor 1 Jahr

Excited to share our new work on building a multimodal atlas of human skin in health and inflammatory disease — a project I’m especially proud of, bringing together AI, high-throughput genomics, and clinical science to accelerate discovery. Over the past decade, single-cell genomics has transformed how we map cells in human tissues. But a major challenge remains: can we systematically decode how cells organize into functional niches in situ — including those invisible to standard histopathology? To address this, we integrated large-scale scRNA-seq, spatial transcriptomics, histopathology, and AI-driven modeling frameworks to build an in situ atlas of human skin across health and disease. Led by Lloyd Steele, an MD/PhD student working between Haniffa Lab and my lab at Wellcome Sanger Institute and Cambridge University . Another amazing collaboration with Muzz Haniffa, the mastermind behind the work as part of Human Cell Atlas. A key part of this study is that we didn’t build everything from scratch — we leveraged and combined AI methods that actually work! and showed how they can be used together to extract biological insight at scale. We used: • scArches to build and map into a reference scRNA-seq atlas of human skin: • NicheCompass to identify and characterize spatial niches: • MINT-Flow to extract microenvironment-induced cell states and gene programs: Together, these enabled an end-to-end workflow from atlas construction to spatial mapping, niche discovery, and cell state decoding. At scale, we integrated ~5 million cells and 100+ spatial sections, enabling a systematic view of tissue organization. Using this framework, we identified 26 niches in skin, including known histopathologic structures as well as hidden disease-associated niches not visible on H&E. Among the most striking findings were a resident memory T cell-rich sebaceous gland niche and a plasma cell-rich sweat gland niche, suggesting that appendageal structures act as active immunological microenvironments and may contribute to inflammatory memory and disease persistence. Importantly, this atlas is not just descriptive — it is usable. It can support mapping of new datasets, resolve finer cell types and niches, extract microenvironment-driven programs, and enable predictive analyses at scale. More broadly, this work shows what becomes possible when AI, spatial genomics, and atlas-scale data are integrated end-to-end: not just mapping tissues, but systematically decoding them. This was a massive collaboration, and I’m very grateful to the amazing scientists April Foster, Kenny Roberts, and Chloe Admane. Lloyd is an amazing scientist, and I’m especially excited for the community to see more of his work soon — stay tuned. The data and pre-trained models will be released soon. Preprint:
1:09

Excited to share our new work on building a multimodal atlas of human skin in health and inflammatory disease — a project I’m especially proud of, bringing together AI, high-throughput genomics, and clinical science to accelerate discovery. Over the past decade, single-cell genomics has transformed how we map cells in human tissues. But a major challenge remains: can we systematically decode how cells organize into functional niches in situ — including those invisible to standard histopathology? To address this, we integrated large-scale scRNA-seq, spatial transcriptomics, histopathology, and AI-driven modeling frameworks to build an in situ atlas of human skin across health and disease. Led by Lloyd Steele, an MD/PhD student working between Haniffa Lab and my lab at Wellcome Sanger Institute and Cambridge University . Another amazing collaboration with Muzz Haniffa, the mastermind behind the work as part of Human Cell Atlas. A key part of this study is that we didn’t build everything from scratch — we leveraged and combined AI methods that actually work! and showed how they can be used together to extract biological insight at scale. We used: • scArches to build and map into a reference scRNA-seq atlas of human skin: • NicheCompass to identify and characterize spatial niches: • MINT-Flow to extract microenvironment-induced cell states and gene programs: Together, these enabled an end-to-end workflow from atlas construction to spatial mapping, niche discovery, and cell state decoding. At scale, we integrated ~5 million cells and 100+ spatial sections, enabling a systematic view of tissue organization. Using this framework, we identified 26 niches in skin, including known histopathologic structures as well as hidden disease-associated niches not visible on H&E. Among the most striking findings were a resident memory T cell-rich sebaceous gland niche and a plasma cell-rich sweat gland niche, suggesting that appendageal structures act as active immunological microenvironments and may contribute to inflammatory memory and disease persistence. Importantly, this atlas is not just descriptive — it is usable. It can support mapping of new datasets, resolve finer cell types and niches, extract microenvironment-driven programs, and enable predictive analyses at scale. More broadly, this work shows what becomes possible when AI, spatial genomics, and atlas-scale data are integrated end-to-end: not just mapping tissues, but systematically decoding them. This was a massive collaboration, and I’m very grateful to the amazing scientists April Foster, Kenny Roberts, and Chloe Admane. Lloyd is an amazing scientist, and I’m especially excited for the community to see more of his work soon — stay tuned. The data and pre-trained models will be released soon. Preprint:

Mo Lotfollahi

11,813 Aufrufe • vor 4 Monaten

🧬 BREAKING: Our CRISPR-GPT paper is out TODAY in Nature Biomedical Engineering Nature Biomedical Engineering ! 🤯 We built an AI agent that turns ANYONE into a gene-editing expert in 1 DAY instead of months. An undergrad with ZERO experience achieved 90%+ editing efficiency on their FIRST attempt. 🧵 Here's how we're building expert AI agents for cutting-edge biotechnology: 🎯 The Problem: CRISPR is revolutionary but requires PhD-level expertise, it can take weeks to learn, adopt, and design, analyze a CRISPR experiment for R&D or making life-saving medicine. Even Pro scientists can make small mistakes (e.g. typos in guideRNA or cloning design) that cost months to find out, slowing us down. 💡 Our Solution: CRISPR-GPT - an AI co-pilot from Stanford University Princeton University Google DeepMind that guides you through EVERY step via simple conversation 🔬 Real Results: -Novice researcher: ~90% editing on 1st go -Training time: Months → 1 day -100% success rate in our trials -Even experts save days/weeks on data analysis & troubleshooting 🤖 How it works: Our multi-agent system handles: CRISPR system and delivery method selection, guideRNA design, Protocol generation, Real-time troubleshooting, Data analysis, and beyond. All through natural language! No coding, no complex software. 📊 We benchmarked it extensively: -288 evaluation scenarios/cases -Outperformed GPT-4o on ALL gene editing tasks -Trained on 11 years of expert discussions -Covers knockout, base-editing, prime-editing & epigenetic editing 🌍 Why this matters: -Every lab can now use CRISPR with an AI system distilling expert knowledge and skills. -Every student can learn faster. -Every researcher can tackle bigger challenges without worrying about small mistakes. -Customized CRISPR design can be automated based on your need and the context of R&D workflow. -Agentic AI ensure safety, privacy, and responsibility -We're not just automating gene editing - we're using AI to power scientists to cure diseases. 🚀 Try it yourself! Beta access available at: Paper: Code: Benchmark (companion work, Genome-bench): Co-first and key authors: Yuanhao Qu Kaixuan Huang Ming Yin PIs: Le Cong@Stanford, AI+Bio+Gene-Editing Mengdi Wang Key collaborators: Russ Altman Denny Zhou The future of biology and science is conversational. The future is now. Nature Biomedical Engineering Nature Portfolio #CRISPR #AI #GeneEditing #Biotech #Science #AISafety
1:43

🧬 BREAKING: Our CRISPR-GPT paper is out TODAY in Nature Biomedical Engineering Nature Biomedical Engineering ! 🤯 We built an AI agent that turns ANYONE into a gene-editing expert in 1 DAY instead of months. An undergrad with ZERO experience achieved 90%+ editing efficiency on their FIRST attempt. 🧵 Here's how we're building expert AI agents for cutting-edge biotechnology: 🎯 The Problem: CRISPR is revolutionary but requires PhD-level expertise, it can take weeks to learn, adopt, and design, analyze a CRISPR experiment for R&D or making life-saving medicine. Even Pro scientists can make small mistakes (e.g. typos in guideRNA or cloning design) that cost months to find out, slowing us down. 💡 Our Solution: CRISPR-GPT - an AI co-pilot from Stanford University Princeton University Google DeepMind that guides you through EVERY step via simple conversation 🔬 Real Results: -Novice researcher: ~90% editing on 1st go -Training time: Months → 1 day -100% success rate in our trials -Even experts save days/weeks on data analysis & troubleshooting 🤖 How it works: Our multi-agent system handles: CRISPR system and delivery method selection, guideRNA design, Protocol generation, Real-time troubleshooting, Data analysis, and beyond. All through natural language! No coding, no complex software. 📊 We benchmarked it extensively: -288 evaluation scenarios/cases -Outperformed GPT-4o on ALL gene editing tasks -Trained on 11 years of expert discussions -Covers knockout, base-editing, prime-editing & epigenetic editing 🌍 Why this matters: -Every lab can now use CRISPR with an AI system distilling expert knowledge and skills. -Every student can learn faster. -Every researcher can tackle bigger challenges without worrying about small mistakes. -Customized CRISPR design can be automated based on your need and the context of R&D workflow. -Agentic AI ensure safety, privacy, and responsibility -We're not just automating gene editing - we're using AI to power scientists to cure diseases. 🚀 Try it yourself! Beta access available at: Paper: Code: Benchmark (companion work, Genome-bench): Co-first and key authors: Yuanhao Qu Kaixuan Huang Ming Yin PIs: Le Cong@Stanford, AI+Bio+Gene-Editing Mengdi Wang Key collaborators: Russ Altman Denny Zhou The future of biology and science is conversational. The future is now. Nature Biomedical Engineering Nature Portfolio #CRISPR #AI #GeneEditing #Biotech #Science #AISafety

Le Cong@Stanford, AI+Bio+Gene-Editing

111,843 Aufrufe • vor 1 Jahr

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource
0:52

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

Bo Wang

199,725 Aufrufe • vor 2 Jahren

This year Demis Hassabis predicted AI could cure all disease in a decade. But Claus Wilke & Derek Lowe say biology is far more complex, or progress will be limited by clinical trials & economics. In a new 4hr episode of the Hard Drugs podcast, we answer: Will AI solve medicine and cure all diseases (within a decade)? We talk about drug discovery, virtual cells, the Human Genome Project, manufacturing, nanobots, innovative clinical trial design, and much more. AI is already being used in drug discovery, and there’s been a lot of progress predicting the structure of soluble proteins, tweaking proteins and designing new structures, as we’ve covered in previous episodes. But there’s still a huge gap in understanding protein dynamics and interactions, as there are many areas where measurement tools and data collection are limited, including events that happen in the span of milliseconds or microseconds, which is how fast many things occur in biological systems. And while computing has scaled exponentially with Moore’s Law, drug development has faced the opposite: Eroom’s Law, where innovation has gotten more complex and more expensive over time. Even with promising drug candidates, we talk about why human testing – not in animals or virtual cells – will continue to be vital, to test which ones are effective and safe, even though models will help earlier in the pipeline. Beyond that, large samples and long follow ups are needed to detect rare side effects, understand whether drugs cause long-term complications, and find ways to manage them. It’s hard to see AI getting around the desire for rigorous safety data in real humans. Another big challenge is the capital and expertise needed to produce and scale personalized medicines and complex biological products, surgeries, transplants, antibodies, and gene-editing tools, which have entirely different cost structures from small molecule drugs. Their manufacturing and delivery often require highly skilled staff and expensive, intensive, individualized procedures. Cost challenges are also severe for tropical and rare diseases, where the financial return to diagnose, do research, develop drugs, manufacture and deliver them at scale, is limited. Without philanthropic funding and economic growth, a lot of diseases are going to remain uncurable, and a lot of people are going to go untreated – whether that’s because of a lack of trust, poor economic and financial incentives, limited public health ambition, and policy. In one sense, we’re skeptical that AI can solve medicine on its own. But in another, there are many areas where we think AI can help. So the episode also functions as a roadmap to speed up medical progress and scale up the delivery of lifesaving medicines – with AI and other approaches to reform the pipeline. What are the economic incentives, innovative trial designs, and data collection efforts that can help drive further medical progress? And how does AI fit in? You’ll have to listen to find out! Timestamps: 0:04:34 Contrasting AI optimism and skepticism 0:32:44 The non-linear path between science and technology 1:01:30 The fundamental need for experiments 1:23:15 Animals, organoids, and virtual cells 1:50:47 The challenges of collecting drug efficacy data in humans 2:34:02 The long road to drug safety data 3:06:09 The cost problem of delivering biological drugs and personalized medicine at scale 3:45:35 The global skew in R&D and healthcare funding 4:01:48 Trust, ambition, and the final barriers to medical progress
4:34:47

This year Demis Hassabis predicted AI could cure all disease in a decade. But Claus Wilke & Derek Lowe say biology is far more complex, or progress will be limited by clinical trials & economics. In a new 4hr episode of the Hard Drugs podcast, we answer: Will AI solve medicine and cure all diseases (within a decade)? We talk about drug discovery, virtual cells, the Human Genome Project, manufacturing, nanobots, innovative clinical trial design, and much more. AI is already being used in drug discovery, and there’s been a lot of progress predicting the structure of soluble proteins, tweaking proteins and designing new structures, as we’ve covered in previous episodes. But there’s still a huge gap in understanding protein dynamics and interactions, as there are many areas where measurement tools and data collection are limited, including events that happen in the span of milliseconds or microseconds, which is how fast many things occur in biological systems. And while computing has scaled exponentially with Moore’s Law, drug development has faced the opposite: Eroom’s Law, where innovation has gotten more complex and more expensive over time. Even with promising drug candidates, we talk about why human testing – not in animals or virtual cells – will continue to be vital, to test which ones are effective and safe, even though models will help earlier in the pipeline. Beyond that, large samples and long follow ups are needed to detect rare side effects, understand whether drugs cause long-term complications, and find ways to manage them. It’s hard to see AI getting around the desire for rigorous safety data in real humans. Another big challenge is the capital and expertise needed to produce and scale personalized medicines and complex biological products, surgeries, transplants, antibodies, and gene-editing tools, which have entirely different cost structures from small molecule drugs. Their manufacturing and delivery often require highly skilled staff and expensive, intensive, individualized procedures. Cost challenges are also severe for tropical and rare diseases, where the financial return to diagnose, do research, develop drugs, manufacture and deliver them at scale, is limited. Without philanthropic funding and economic growth, a lot of diseases are going to remain uncurable, and a lot of people are going to go untreated – whether that’s because of a lack of trust, poor economic and financial incentives, limited public health ambition, and policy. In one sense, we’re skeptical that AI can solve medicine on its own. But in another, there are many areas where we think AI can help. So the episode also functions as a roadmap to speed up medical progress and scale up the delivery of lifesaving medicines – with AI and other approaches to reform the pipeline. What are the economic incentives, innovative trial designs, and data collection efforts that can help drive further medical progress? And how does AI fit in? You’ll have to listen to find out! Timestamps: 0:04:34 Contrasting AI optimism and skepticism 0:32:44 The non-linear path between science and technology 1:01:30 The fundamental need for experiments 1:23:15 Animals, organoids, and virtual cells 1:50:47 The challenges of collecting drug efficacy data in humans 2:34:02 The long road to drug safety data 3:06:09 The cost problem of delivering biological drugs and personalized medicine at scale 3:45:35 The global skew in R&D and healthcare funding 4:01:48 Trust, ambition, and the final barriers to medical progress

Saloni

221,350 Aufrufe • vor 9 Monaten

Introducing LifeGPT, showing that LLMs can simulate complex, Turing-complete systems like Conway's Game of Life with near-perfect accuracy—no prior topology needed.🌐This unlocks new potential for AI in modeling self-organizing systems in biology, materials science, & beyond.🔬🤖 #AI #LifeGPT. Cellular Automata (CA), like Conway's Game of Life ("Life"), are computationally irreducible, meaning their evolution is difficult to predict without an a-priori understanding of the rules of the game, including the topology on which it is played. LifeGPT is a topology-agnostic generative model that learns the rules of Life without prior knowledge of its grid structure or boundary conditions, from only a tiny number of game states. The success in simulating Life suggests promising avenues for scientific discovery, particularly in bridging the gap between AI, artificial life, and real-world biological systems, for both forward and inverse problems. The potential for universal computation within generative AI, including LLMs, through approaches like LifeGPT, represents an exciting area for future research, especially when combined with reinforcement learning. Model Convergence: LifeGPT exhibits rapid convergence during training, achieving high accuracy in predicting next-game-states. We attribute the non-zero cross-entropy loss to the lack of causal relationships within randomly generated ICs. Accuracy & Temperature: LifeGPT achieves near-perfect accuracy, particularly at lower sampling temperatures, but can be continually tuned towards higher creativity to discover patterns that the original ruleset would not be able to produce. This finding highlights the trade-off between model creativity (higher temperature) and accuracy in deterministic predictions, with high relevance to model real-world dynamical systems for which no closed-form rulesets exist. Zero/Few-Shot Learning: Trained on a small fraction of possible initial conditions, LifeGPT demonstrates strong zero/few-shot learning, accurately simulating Life for unseen initial conditions. However, rare prediction errors highlight that LifeGPT approximates rather than perfectly replicates the Life algorithm. Autoregressive Autoregressor: A recursive implementation of LifeGPT demonstrates the model's ability to simulate Life over multiple timesteps. LifeGPT is topology-agnostic with respect to its training data and our results show that a GPT model is capable of capturing the deterministic rules of a Turing-complete system with near-perfect accuracy, given sufficiently diverse training data. The work showcases the possibility for future models to synthesize stochastic generative capabilities with deterministic computational capabilities. Link to code, paper, etc. below. Podcast generated using #NotebookLM. LAMM@MIT DMSE at MIT
9:59

Introducing LifeGPT, showing that LLMs can simulate complex, Turing-complete systems like Conway's Game of Life with near-perfect accuracy—no prior topology needed.🌐This unlocks new potential for AI in modeling self-organizing systems in biology, materials science, & beyond.🔬🤖 #AI #LifeGPT. Cellular Automata (CA), like Conway's Game of Life ("Life"), are computationally irreducible, meaning their evolution is difficult to predict without an a-priori understanding of the rules of the game, including the topology on which it is played. LifeGPT is a topology-agnostic generative model that learns the rules of Life without prior knowledge of its grid structure or boundary conditions, from only a tiny number of game states. The success in simulating Life suggests promising avenues for scientific discovery, particularly in bridging the gap between AI, artificial life, and real-world biological systems, for both forward and inverse problems. The potential for universal computation within generative AI, including LLMs, through approaches like LifeGPT, represents an exciting area for future research, especially when combined with reinforcement learning. Model Convergence: LifeGPT exhibits rapid convergence during training, achieving high accuracy in predicting next-game-states. We attribute the non-zero cross-entropy loss to the lack of causal relationships within randomly generated ICs. Accuracy & Temperature: LifeGPT achieves near-perfect accuracy, particularly at lower sampling temperatures, but can be continually tuned towards higher creativity to discover patterns that the original ruleset would not be able to produce. This finding highlights the trade-off between model creativity (higher temperature) and accuracy in deterministic predictions, with high relevance to model real-world dynamical systems for which no closed-form rulesets exist. Zero/Few-Shot Learning: Trained on a small fraction of possible initial conditions, LifeGPT demonstrates strong zero/few-shot learning, accurately simulating Life for unseen initial conditions. However, rare prediction errors highlight that LifeGPT approximates rather than perfectly replicates the Life algorithm. Autoregressive Autoregressor: A recursive implementation of LifeGPT demonstrates the model's ability to simulate Life over multiple timesteps. LifeGPT is topology-agnostic with respect to its training data and our results show that a GPT model is capable of capturing the deterministic rules of a Turing-complete system with near-perfect accuracy, given sufficiently diverse training data. The work showcases the possibility for future models to synthesize stochastic generative capabilities with deterministic computational capabilities. Link to code, paper, etc. below. Podcast generated using #NotebookLM. LAMM@MIT DMSE at MIT

Markus J. Buehler

114,217 Aufrufe • vor 1 Jahr

Demis Hassabis says AI won’t just accelerate drug discovery. It will replace the process entirely. The pharmaceutical industry finds drugs the same way it has for decades. Synthesize a compound. Test it on animals. Test it on humans. Wait years for approval. Hope the molecule doesn’t kill someone along the way. Every step is physical. Every step is slow. Every step is expensive enough to make most diseases not worth curing. Hassabis: “We’re focusing on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, checking it’s not toxic, and all the different properties you need for drugs to be safe.” That sounds incremental. It isn’t. AlphaFold solved protein folding. Isomorphic Labs is now working through the rest of the chain. Compound design. Toxicity screening. Safety profiling. All computational. None of it requires a lab. Hassabis: “I think we’ll have that whole drug design engine ready in the next five to 10 years.” Not a tool that assists chemists. A system that replaces the chemistry. But designing the drug was never the bottleneck that killed people. Clinical trials were. A single drug takes over a decade to move from lab to patient. Most of that time isn’t science. It’s bureaucracy, logistics, and the blunt reality of testing molecules on living tissue one dose at a time. Hassabis: “Simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly the right type of drug that’s suitable for their genomic makeup.” Simulate the patient before you treat the patient. Map individual DNA. Model personal metabolism. Test the drug on a digital replica before it touches a vein. Not personalized medicine as a marketing phrase. Personalized medicine as an engineering output. The final wall is regulatory. The FDA exists because humans make mistakes with molecules. Every approval gate was built to catch errors that cost lives. The entire structure assumes the process is fallible. What happens when the process stops being fallible. Hassabis: “Perhaps like the animal testing is not needed anymore, maybe we can go up the dosage ladder quicker, because you can rely on these models.” He’s not speculating. He’s describing a sequence. AI-designed drugs enter the existing pipeline. A dozen compounds go through full traditional trials. Regulators collect data. They back-test model predictions against real outcomes. Hassabis: “Then the government and the regulatory bodies see that and they have enough data to sort of back-test the predictions of those models.” When the models prove more accurate than the trials they’re meant to replace, the trials become the bottleneck. Not the science. The paperwork. Animal testing shortened. Dosage ladders compressed. Entire stages of the pipeline collapsed into computation. The drug doesn’t get discovered faster. The drug gets discovered differently. The laboratory moves from a building to a server. The clinical trial moves from a hospital ward to a simulation. The patient moves from a statistic to a genome. Hassabis isn’t promising a cure for one disease. He’s describing the architecture that makes curing disease an engineering problem with a known solution path. The bottleneck was never biology. It was the speed at which humans were allowed to solve it. That speed limit is about to be revoked.
1:37

Demis Hassabis says AI won’t just accelerate drug discovery. It will replace the process entirely. The pharmaceutical industry finds drugs the same way it has for decades. Synthesize a compound. Test it on animals. Test it on humans. Wait years for approval. Hope the molecule doesn’t kill someone along the way. Every step is physical. Every step is slow. Every step is expensive enough to make most diseases not worth curing. Hassabis: “We’re focusing on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, checking it’s not toxic, and all the different properties you need for drugs to be safe.” That sounds incremental. It isn’t. AlphaFold solved protein folding. Isomorphic Labs is now working through the rest of the chain. Compound design. Toxicity screening. Safety profiling. All computational. None of it requires a lab. Hassabis: “I think we’ll have that whole drug design engine ready in the next five to 10 years.” Not a tool that assists chemists. A system that replaces the chemistry. But designing the drug was never the bottleneck that killed people. Clinical trials were. A single drug takes over a decade to move from lab to patient. Most of that time isn’t science. It’s bureaucracy, logistics, and the blunt reality of testing molecules on living tissue one dose at a time. Hassabis: “Simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly the right type of drug that’s suitable for their genomic makeup.” Simulate the patient before you treat the patient. Map individual DNA. Model personal metabolism. Test the drug on a digital replica before it touches a vein. Not personalized medicine as a marketing phrase. Personalized medicine as an engineering output. The final wall is regulatory. The FDA exists because humans make mistakes with molecules. Every approval gate was built to catch errors that cost lives. The entire structure assumes the process is fallible. What happens when the process stops being fallible. Hassabis: “Perhaps like the animal testing is not needed anymore, maybe we can go up the dosage ladder quicker, because you can rely on these models.” He’s not speculating. He’s describing a sequence. AI-designed drugs enter the existing pipeline. A dozen compounds go through full traditional trials. Regulators collect data. They back-test model predictions against real outcomes. Hassabis: “Then the government and the regulatory bodies see that and they have enough data to sort of back-test the predictions of those models.” When the models prove more accurate than the trials they’re meant to replace, the trials become the bottleneck. Not the science. The paperwork. Animal testing shortened. Dosage ladders compressed. Entire stages of the pipeline collapsed into computation. The drug doesn’t get discovered faster. The drug gets discovered differently. The laboratory moves from a building to a server. The clinical trial moves from a hospital ward to a simulation. The patient moves from a statistic to a genome. Hassabis isn’t promising a cure for one disease. He’s describing the architecture that makes curing disease an engineering problem with a known solution path. The bottleneck was never biology. It was the speed at which humans were allowed to solve it. That speed limit is about to be revoked.

Dustin

43,980 Aufrufe • vor 4 Monaten

Anthropic admitted they built an AI so capable they were scared to release it and the number that explains why is 250. Anthropic's CFO Krishna Rao described in this clip what happened when they ran Mythos against an open source codebase that a previous frontier model had already analyzed. The prior model found 22 security vulnerabilities, Mythos found 250. In the same codebase, that the previous model had already reviewed and flagged as relatively clean. That number, more than 11 times as many vulnerabilities discovered is not just a benchmark improvement, it is a signal that there is an entire layer of software infrastructure that humanity has been operating under the assumption was secure and that assumption may no longer hold. The UK AI Security Institute independently evaluated Mythos Preview and confirmed what the internal numbers suggested. On expert level capture the flag challenges that no model could complete before April 2025, Mythos succeeded 73% of the time and it became the first model ever to complete a complex end-to-end attack range from start to finish, autonomously, without human guidance. The World Economic Forum called this a new security-driven era for AI, the Governor of the Bank of England publicly warned that Anthropic may have found a way to unlock the entire cyber-risk landscape, and the European Central Bank began quietly contacting financial institutions to assess their security posture. The response from Anthropic is what makes this story genuinely important. Rather than shelving the model or publishing it as a standard API release, Rao described a phased approach restricting access to a controlled group, focusing specifically on how the cyber capabilities can be used defensively rather than offensively and treating that framework as a template for how to release powerful but dangerous models in the future. The broader context makes that framing even more significant. AI generated code is already creating ten times more security vulnerabilities than human-written code, 63% of organizations reported experiencing an AI driven cyberattack in the past 12 months, and traditional signature-based security tools were built for a threat model that no longer describes the attack surface companies are defending against. Mythos represents a genuine leap in what autonomous security reasoning can do and it cuts both ways. The model that can find 250 vulnerabilities in a codebase a prior model rated as mostly clean is also, in the wrong hands, the model that can exploit those 250 vulnerabilities before a human defender has even finished reading the report. Anthropic's phased release strategy is not just a legal or PR decision, it is the most honest signal yet from a frontier lab that safety governance and capability development can no longer be treated as separate workstreams. The question is not whether this technology gets deployed, it is whether the institutions using it defensively stay ahead of the ones who will eventually use it offensively and whether the labs building it can keep those two timelines from inverting.
1:20

Anthropic admitted they built an AI so capable they were scared to release it and the number that explains why is 250. Anthropic's CFO Krishna Rao described in this clip what happened when they ran Mythos against an open source codebase that a previous frontier model had already analyzed. The prior model found 22 security vulnerabilities, Mythos found 250. In the same codebase, that the previous model had already reviewed and flagged as relatively clean. That number, more than 11 times as many vulnerabilities discovered is not just a benchmark improvement, it is a signal that there is an entire layer of software infrastructure that humanity has been operating under the assumption was secure and that assumption may no longer hold. The UK AI Security Institute independently evaluated Mythos Preview and confirmed what the internal numbers suggested. On expert level capture the flag challenges that no model could complete before April 2025, Mythos succeeded 73% of the time and it became the first model ever to complete a complex end-to-end attack range from start to finish, autonomously, without human guidance. The World Economic Forum called this a new security-driven era for AI, the Governor of the Bank of England publicly warned that Anthropic may have found a way to unlock the entire cyber-risk landscape, and the European Central Bank began quietly contacting financial institutions to assess their security posture. The response from Anthropic is what makes this story genuinely important. Rather than shelving the model or publishing it as a standard API release, Rao described a phased approach restricting access to a controlled group, focusing specifically on how the cyber capabilities can be used defensively rather than offensively and treating that framework as a template for how to release powerful but dangerous models in the future. The broader context makes that framing even more significant. AI generated code is already creating ten times more security vulnerabilities than human-written code, 63% of organizations reported experiencing an AI driven cyberattack in the past 12 months, and traditional signature-based security tools were built for a threat model that no longer describes the attack surface companies are defending against. Mythos represents a genuine leap in what autonomous security reasoning can do and it cuts both ways. The model that can find 250 vulnerabilities in a codebase a prior model rated as mostly clean is also, in the wrong hands, the model that can exploit those 250 vulnerabilities before a human defender has even finished reading the report. Anthropic's phased release strategy is not just a legal or PR decision, it is the most honest signal yet from a frontier lab that safety governance and capability development can no longer be treated as separate workstreams. The question is not whether this technology gets deployed, it is whether the institutions using it defensively stay ahead of the ones who will eventually use it offensively and whether the labs building it can keep those two timelines from inverting.

Milk Road AI

24,356 Aufrufe • vor 3 Monaten

🚀 Introducing PantheonOS ( A Fully Open-Source Agent OS for Science PantheonOS began as a research project in my Stanford lab and has since evolved into a vision to redefine data science in the era of AI—starting with computational biology, especially single-cell and spatial genomics. PantheonOS is a general agent platform built from the ground up. It is arguably the first distributed agent framework designed for scientific data analysis. 🔑 Key Features 1. Multi-Agent Collaboration – Built-in paradigms for distributed, cross-machine cooperation among agents and toolsets. 2. Native Toolset Support – Python, R, Julia, LaTeX, and more—designed for real scientific workflows. 3. Modular & Extensible – Developer-friendly design with shallow wrappers, plus LLM-driven toolset generation. 4. Evolvable Agents – Capable of evolving large-scale code projects to achieve superhuman performance (e.g., evolving upon the original Harmony [I Korsunsky, 2019, Nature Biotechnology] and Scanorama [BL Hie, 2019, Nature Biotechnology] implementations), and even evolving the system itself to adapt to new fields. 🎉 Stepwise Release Strategy We’re releasing PantheonOS in stages: Pantheon-CLI (today!), followed by Pantheon-Lab, Pantheon-Notebook, Pantheon-Slack, and more. 🌟 Pantheon-CLI Highlights - We're not just building another CLI tool. We're defining how scientists will interact with data in the AI era. - Open, Powerful, Python-First – The first fully open-source, endlessly extendable scientific “vibe analysis” framework. - Mixed Programming Magic – Combine Python, natural language, R, or Julia—seamlessly in the same environment. - PhD-Level Assistant – A command-line agent for complex real-world genomics and beyond, handling workflows at the PhD level. - Privacy by Design – Run entirely offline with local LLMs—your data never leaves your computer. ✅ Proven Applications (10 Demonstrations) Computational biology: 1. ATAC-seq: From raw reads to peak matrix 2. RNA-seq: From raw reads to expression matrix 3. Complex single-cell workflows (PhD-level) 4. Hybrid natural language + R for Seurat annotation 5. Learning from web tutorials + invoking single-cell foundation models 6. Cell segmentation on 10x Genomics HD Visium data And beyond: 7. Mixed Python & R programming examples 8. Molecular docking & structural analysis 9. Exploratory factor analysis for behavioral survey data 10. Customer segmentation & finance analytics 🌐 Learn More & Get Started Website: Pantheon-CLI Documentation: GitHub Repo: 💬 Join our community: PantheonOS Slack: PantheonOS Discord:
1:16

🚀 Introducing PantheonOS ( A Fully Open-Source Agent OS for Science PantheonOS began as a research project in my Stanford lab and has since evolved into a vision to redefine data science in the era of AI—starting with computational biology, especially single-cell and spatial genomics. PantheonOS is a general agent platform built from the ground up. It is arguably the first distributed agent framework designed for scientific data analysis. 🔑 Key Features 1. Multi-Agent Collaboration – Built-in paradigms for distributed, cross-machine cooperation among agents and toolsets. 2. Native Toolset Support – Python, R, Julia, LaTeX, and more—designed for real scientific workflows. 3. Modular & Extensible – Developer-friendly design with shallow wrappers, plus LLM-driven toolset generation. 4. Evolvable Agents – Capable of evolving large-scale code projects to achieve superhuman performance (e.g., evolving upon the original Harmony [I Korsunsky, 2019, Nature Biotechnology] and Scanorama [BL Hie, 2019, Nature Biotechnology] implementations), and even evolving the system itself to adapt to new fields. 🎉 Stepwise Release Strategy We’re releasing PantheonOS in stages: Pantheon-CLI (today!), followed by Pantheon-Lab, Pantheon-Notebook, Pantheon-Slack, and more. 🌟 Pantheon-CLI Highlights - We're not just building another CLI tool. We're defining how scientists will interact with data in the AI era. - Open, Powerful, Python-First – The first fully open-source, endlessly extendable scientific “vibe analysis” framework. - Mixed Programming Magic – Combine Python, natural language, R, or Julia—seamlessly in the same environment. - PhD-Level Assistant – A command-line agent for complex real-world genomics and beyond, handling workflows at the PhD level. - Privacy by Design – Run entirely offline with local LLMs—your data never leaves your computer. ✅ Proven Applications (10 Demonstrations) Computational biology: 1. ATAC-seq: From raw reads to peak matrix 2. RNA-seq: From raw reads to expression matrix 3. Complex single-cell workflows (PhD-level) 4. Hybrid natural language + R for Seurat annotation 5. Learning from web tutorials + invoking single-cell foundation models 6. Cell segmentation on 10x Genomics HD Visium data And beyond: 7. Mixed Python & R programming examples 8. Molecular docking & structural analysis 9. Exploratory factor analysis for behavioral survey data 10. Customer segmentation & finance analytics 🌐 Learn More & Get Started Website: Pantheon-CLI Documentation: GitHub Repo: 💬 Join our community: PantheonOS Slack: PantheonOS Discord:

evo-devo

17,384 Aufrufe • vor 1 Jahr

I've had many engineers ask me why its worth their time and effort to learn biology in response to this post. Why should they be excited? We are poised for a revolution in biotech that will be uniquely enabled by computers. Convince yourself by digging into the examples I link: - The tooling is getting better. Assays are able to measure a broad array of molecules at a falling cost and increasing throughput. Look to ScaleBio, Curio Bioscience, AtlasXOmics for inspiration. We are sequencing millions of single cells and building spatial maps of the molecular state of tumors. After two generations of "next-generation sequencing", and stagnating DNA read + write costs under the monopoly of Illumina, this wave of the new assays will have a profound impact on the iteration speed and scale of experimentation. Little needs to be said about the impact of compounding trends in core tooling over a sufficient period of time. - Biotechs are using data to guide decisions and are incorporating domain informed machine learning as a core part of the molecular design process. There is great synergy here with better tooling as a means of abundant, cheap data. Look to Recursion, Manifold Bio, Dyno Tx, Asimov. There is also the cross pollination of biology informed architectures with the recent explosion in new machine learning techniques. These models are starting to do useful things, like generate functional gene editing proteins and entire prokaryotic organisms. Look to ESM, AlphaFold3, RFdiffusion. - New classes of therapies - genetic medicines and engineered immune cells - are having real success in the clinic. One dose cures for cardiovascular disease (Verve w ACSD), vaccines for cancer (Moderna w mRNA 4157), in vivo gene editing proteins (CRISPR Tx, Beam, Ensoma), metabolic disease (Novartis, Eli Lily w GLP-1/GIP modulators) are being dosed in real people right now + transforming lives. - The AI craze is commoditizing accelerated hardware and fast storage devices like NVMes, improving developer frameworks for writing code against these devices and maturing the systems tooling for moving around lots of data between computers for distributed training. One happy accident of this bubble will be the reuse of these components to build a new systems stack for the large scale processing of molecular data. This will be very important to construct a 1 billion single cell atlas and beyond. (For reference, the state of the art is ScaleBio's 2M cell kit, dubbed QuantumScale, and it is pushing things with the hardware + software we have today.) - Language models might be the perfect tool to distill the unstructured corpus of public data, literature, and methods sitting around on the Internet into real biological insights for scientist asking questions in natural language. It will also allow them to install, configure and run the slew of useful but poorly maintained academic computational tools to explore and hypothesize new biology on their own. Increasing the productivity of each scientist will do much to reverse Eroom's law. - There is an appetite from the market for new applications of biotech beyond drug development. Bacteria driven lithium mining (maverick), cell agriculture (growing cows in vats), early signs of consumer biologics (Geltor), biofoundries (Ginkgo). My guess is some of the greatest minds of our generation will want to do more than perturb the human body with therapies. I think it is also important to recognize that the need for computers and software is a secular trend in the progression of biotech independent of the interests of Silicon Valley. Clusters of computers, the information they store and the software that runs on them are precisely the technology needed by this field as it transforms into a discipline of information management, towards reducing living things into well characterized building blocks we can rebuild in our image. Software companies, as some local and hyper efficient structure in the arc of capitalism, with established methods and well trod rails to attract resources, talent and easily distribute product to an entire market, are the perfect place to incubate and disseminate these tools. There will probably be many, very large computer companies in biology in the next century.
0:52

I've had many engineers ask me why its worth their time and effort to learn biology in response to this post. Why should they be excited? We are poised for a revolution in biotech that will be uniquely enabled by computers. Convince yourself by digging into the examples I link: - The tooling is getting better. Assays are able to measure a broad array of molecules at a falling cost and increasing throughput. Look to ScaleBio, Curio Bioscience, AtlasXOmics for inspiration. We are sequencing millions of single cells and building spatial maps of the molecular state of tumors. After two generations of "next-generation sequencing", and stagnating DNA read + write costs under the monopoly of Illumina, this wave of the new assays will have a profound impact on the iteration speed and scale of experimentation. Little needs to be said about the impact of compounding trends in core tooling over a sufficient period of time. - Biotechs are using data to guide decisions and are incorporating domain informed machine learning as a core part of the molecular design process. There is great synergy here with better tooling as a means of abundant, cheap data. Look to Recursion, Manifold Bio, Dyno Tx, Asimov. There is also the cross pollination of biology informed architectures with the recent explosion in new machine learning techniques. These models are starting to do useful things, like generate functional gene editing proteins and entire prokaryotic organisms. Look to ESM, AlphaFold3, RFdiffusion. - New classes of therapies - genetic medicines and engineered immune cells - are having real success in the clinic. One dose cures for cardiovascular disease (Verve w ACSD), vaccines for cancer (Moderna w mRNA 4157), in vivo gene editing proteins (CRISPR Tx, Beam, Ensoma), metabolic disease (Novartis, Eli Lily w GLP-1/GIP modulators) are being dosed in real people right now + transforming lives. - The AI craze is commoditizing accelerated hardware and fast storage devices like NVMes, improving developer frameworks for writing code against these devices and maturing the systems tooling for moving around lots of data between computers for distributed training. One happy accident of this bubble will be the reuse of these components to build a new systems stack for the large scale processing of molecular data. This will be very important to construct a 1 billion single cell atlas and beyond. (For reference, the state of the art is ScaleBio's 2M cell kit, dubbed QuantumScale, and it is pushing things with the hardware + software we have today.) - Language models might be the perfect tool to distill the unstructured corpus of public data, literature, and methods sitting around on the Internet into real biological insights for scientist asking questions in natural language. It will also allow them to install, configure and run the slew of useful but poorly maintained academic computational tools to explore and hypothesize new biology on their own. Increasing the productivity of each scientist will do much to reverse Eroom's law. - There is an appetite from the market for new applications of biotech beyond drug development. Bacteria driven lithium mining (maverick), cell agriculture (growing cows in vats), early signs of consumer biologics (Geltor), biofoundries (Ginkgo). My guess is some of the greatest minds of our generation will want to do more than perturb the human body with therapies. I think it is also important to recognize that the need for computers and software is a secular trend in the progression of biotech independent of the interests of Silicon Valley. Clusters of computers, the information they store and the software that runs on them are precisely the technology needed by this field as it transforms into a discipline of information management, towards reducing living things into well characterized building blocks we can rebuild in our image. Software companies, as some local and hyper efficient structure in the arc of capitalism, with established methods and well trod rails to attract resources, talent and easily distribute product to an entire market, are the perfect place to incubate and disseminate these tools. There will probably be many, very large computer companies in biology in the next century.

Kenny Workman

113,534 Aufrufe • vor 1 Jahr