正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

.Seqera runs protein structure prediction for drug discovery and personalized medicine. Using Nebius AI Cloud and NVIDIA NIM gave them a 4x speedup over launching containers natively. Hear Evan Floden, CEO and co-founder, on what that means for drug discovery:

Nebius

35,212 subscribers

33,003 次观看 • 6 天前 •via X (Twitter)

科学技术健康养生新闻政治

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Biology has been “guess and check” for too long The missing piece? Predictive models! Patrick Hsu, co-founder of @ArcInstitute, is using AI to transform drug discovery

Biology has been “guess and check” for too long The missing piece? Predictive models! Patrick Hsu, co-founder of @ArcInstitute, is using AI to transform drug discovery

Pat Grady

45,942 次观看 • 1 年前

AMD CTO Mark Papermaster sits down with Sean McClain, Founder and CEO of Absci, to discuss the future of drug discovery, and how AI is revolutionizing this process. Hear how AI is accelerating innovation, cutting costs, and paving the way for personalized medication. Tune in now:

AMD CTO Mark Papermaster sits down with Sean McClain, Founder and CEO of Absci, to discuss the future of drug discovery, and how AI is revolutionizing this process. Hear how AI is accelerating innovation, cutting costs, and paving the way for personalized medication. Tune in now:

AMD

14,834 次观看 • 1 年前

"The future is really bright in AI and drug discovery." Sean McClain, Founder and CEO of Absci, shared the CES stage with healthcare leaders to illustrate how the compute partnership with AMD has scaled their ability to screen over a million drugs in one single day, resulting in better models for drug discovery.

"The future is really bright in AI and drug discovery." Sean McClain, Founder and CEO of Absci, shared the CES stage with healthcare leaders to illustrate how the compute partnership with AMD has scaled their ability to screen over a million drugs in one single day, resulting in better models for drug discovery.

AMD

15,346 次观看 • 6 个月前

"AMD is helping Absci really push that frontier of AI drug discovery." - @Abscibio Founder and CEO, Sean McClain shares a deeper dive into our collaboration to impact healthcare through AI.

"AMD is helping Absci really push that frontier of AI drug discovery." - @Abscibio Founder and CEO, Sean McClain shares a deeper dive into our collaboration to impact healthcare through AI.

AMD

17,104 次观看 • 7 个月前

🤝 Alphabet and NVIDIA are expanding their decade-long partnership to advance agentic AI, robotics, drug discovery, and more. This involves deep co-engineering with integrated platforms, open-source frameworks, and managed services. ✅ Google Cloud is one of the first to bring the NVIDIA Blackwell platform to the cloud. ✅ Google Distributed Cloud using NVIDIA Confidential Computing on #NVIDIABlackwell for enterprises to run Google Gemini on-premises. ✅ Integration of the NVIDIA AI platform across Vertex AI, Cluster Director and Google Kubernetes Engine. ✅ Bringing the NVIDIA Nemotron family of open models to Vertex AI Model Garden. Watch the full video to learn more ➡️

🤝 Alphabet and NVIDIA are expanding their decade-long partnership to advance agentic AI, robotics, drug discovery, and more. This involves deep co-engineering with integrated platforms, open-source frameworks, and managed services. ✅ Google Cloud is one of the first to bring the NVIDIA Blackwell platform to the cloud. ✅ Google Distributed Cloud using NVIDIA Confidential Computing on #NVIDIABlackwell for enterprises to run Google Gemini on-premises. ✅ Integration of the NVIDIA AI platform across Vertex AI, Cluster Director and Google Kubernetes Engine. ✅ Bringing the NVIDIA Nemotron family of open models to Vertex AI Model Garden. Watch the full video to learn more ➡️

NVIDIA AI Infrastructure

268,567 次观看 • 7 个月前

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 次观看 • 1 个月前

🆕!! 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 ~20% hit rate, compressing years of lab work into weeks. They talk what's next for Chai, what's still challenging, and why they're 𝐛𝐮𝐥𝐥𝐢𝐬𝐡 𝐨𝐧 𝐛𝐢𝐨𝐭𝐞𝐜𝐡

🆕!! 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 ~20% hit rate, compressing years of lab work into weeks. They talk what's next for Chai, what's still challenging, and why they're 𝐛𝐮𝐥𝐥𝐢𝐬𝐡 𝐨𝐧 𝐛𝐢𝐨𝐭𝐞𝐜𝐡

No Priors

14,840 次观看 • 1 年前

From sick care to preventative care, Gen AI is helping researchers make breakthroughs in drug discovery, ushering in a new world of AI for healthcare. Absci Founder and CEO, Sean McClain shares how their collaboration with AMD is helping engineer better biologics for patients, faster.

From sick care to preventative care, Gen AI is helping researchers make breakthroughs in drug discovery, ushering in a new world of AI for healthcare. Absci Founder and CEO, Sean McClain shares how their collaboration with AMD is helping engineer better biologics for patients, faster.

AMD

14,159 次观看 • 6 个月前

New Unsupervised Learning with Joshua Meier @ NeurIPS (Chai Discovery) on how AI x drug discovery is starting to inflect and: - The 3 waves of AI biotech - How AI models think differently than chemists - Building "Photoshop for molecules" - Deciding what to open source

New Unsupervised Learning with Joshua Meier @ NeurIPS (Chai Discovery) on how AI x drug discovery is starting to inflect and: - The 3 waves of AI biotech - How AI models think differently than chemists - Building "Photoshop for molecules" - Deciding what to open source

Jacob Effron

12,351 次观看 • 1 年前

"It's our goal to prove that this kind of approach - about whether there's a better way to do drug discovery - that ultimately it will be faster to market, less expensive, more efficient," says Recursion CEO on using AI for pharmaceutical development:

"It's our goal to prove that this kind of approach - about whether there's a better way to do drug discovery - that ultimately it will be faster to market, less expensive, more efficient," says Recursion CEO on using AI for pharmaceutical development:

Squawk Box

17,600 次观看 • 2 年前

20 companies that will dominate the next decade: 1/ Nebius $NBIS is redefining cloud computing for the age of AI, offering an infrastructure platform designed specifically for building and running AI models. Nebius services will help both large and small AI companies build innovative AI solutions in robotics, healthcare, finance, military, agriculture, drug discovery, material sciences, and many more. The company just signed a massive 5-year $17.4B contract with Microsoft. It is currently expanding the data center business in: - Finland - Iceland - New Jersey - Alabama - UK - France - Israel

20 companies that will dominate the next decade: 1/ Nebius $NBIS is redefining cloud computing for the age of AI, offering an infrastructure platform designed specifically for building and running AI models. Nebius services will help both large and small AI companies build innovative AI solutions in robotics, healthcare, finance, military, agriculture, drug discovery, material sciences, and many more. The company just signed a massive 5-year $17.4B contract with Microsoft. It is currently expanding the data center business in: - Finland - Iceland - New Jersey - Alabama - UK - France - Israel

Ray Myers

125,907 次观看 • 10 个月前

AI agents are coming and they need serious compute. On the CES keynote stage, OpenAI President and Co-founder Greg Brockman (Greg Brockman) joined AMD Chair and CEO Lisa Su to share how AI is evolving from simple prompts to always-on agents transforming healthcare, enterprise, and scientific discovery. AMD Instinct MI455X is built for what comes next.

AI agents are coming and they need serious compute. On the CES keynote stage, OpenAI President and Co-founder Greg Brockman (Greg Brockman) joined AMD Chair and CEO Lisa Su to share how AI is evolving from simple prompts to always-on agents transforming healthcare, enterprise, and scientific discovery. AMD Instinct MI455X is built for what comes next.

AMD

25,538 次观看 • 6 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

Science is entering a new era - one where AI agents can do scientific work. 🧬 Today NVIDIA is launching the BioNeMo Agent Toolkit - an open, agent-ready toolkit that gives any AI agent callable tools for protein structure prediction, molecular docking, generative chemistry, genomic analysis, and more. (1/2)

Science is entering a new era - one where AI agents can do scientific work. 🧬 Today NVIDIA is launching the BioNeMo Agent Toolkit - an open, agent-ready toolkit that gives any AI agent callable tools for protein structure prediction, molecular docking, generative chemistry, genomic analysis, and more. (1/2)

NVIDIA Healthcare

294,594 次观看 • 1 个月前

The expected vs. the transformative. Gata Co-founder Oliver's Oliver insight from our onchain AI discussion: We see AI generating content and solving problems. We don't see AI revolutionizing drug discovery and material science. "When people see this coming out, it's going to be a really new image to everyone." At CARV, we're preparing for that moment when AI's true potential meets user-owned data infrastructure.

The expected vs. the transformative. Gata Co-founder Oliver's Oliver insight from our onchain AI discussion: We see AI generating content and solving problems. We don't see AI revolutionizing drug discovery and material science. "When people see this coming out, it's going to be a really new image to everyone." At CARV, we're preparing for that moment when AI's true potential meets user-owned data infrastructure.

CARV

18,941 次观看 • 1 年前

A miner on Micaela Bazo's (Micaela) Bittensor Subnet, SN68 NOVA (@metanovalabs), applied an energy supply optimization strategy for drug discovery to outcompete other miners. "One of the winning code submissions for Nova Blueprint used this optimization strategy that is usually used for energy supply, had never been applied to drug discovery, and performed really well across different targets. In fact it outcompeted a really well respected published method called the Thompson Sampling Technique, and it even did so for a hard-to-drug oncology target."

A miner on Micaela Bazo's (Micaela) Bittensor Subnet, SN68 NOVA (@metanovalabs), applied an energy supply optimization strategy for drug discovery to outcompete other miners. "One of the winning code submissions for Nova Blueprint used this optimization strategy that is usually used for energy supply, had never been applied to drug discovery, and performed really well across different targets. In fact it outcompeted a really well respected published method called the Thompson Sampling Technique, and it even did so for a hard-to-drug oncology target."

Boomer

23,449 次观看 • 3 个月前

🔍 We're entering a new era of vulnerability discovery, where AI and human ingenuity come together to unlock new possibilities. Over the past five months, AI hasn’t replaced researchers—it’s amplified them. Together, we’re achieving new levels of speed, scale, and precision in offensive security. With these changes, HackerOne is reshaping how collaboration drives impact in security testing. 📽️ Hear from our co-founder Michiel Prins on what this means for the future of our platform and our community. 🔗 Read Michiel's blog for more: #OffensiveSecurity #AIinCybersecurity #togetherwehitharder #AI #cybersecurity

🔍 We're entering a new era of vulnerability discovery, where AI and human ingenuity come together to unlock new possibilities. Over the past five months, AI hasn’t replaced researchers—it’s amplified them. Together, we’re achieving new levels of speed, scale, and precision in offensive security. With these changes, HackerOne is reshaping how collaboration drives impact in security testing. 📽️ Hear from our co-founder Michiel Prins on what this means for the future of our platform and our community. 🔗 Read Michiel's blog for more: #OffensiveSecurity #AIinCybersecurity #togetherwehitharder #AI #cybersecurity

HackerOne

26,287 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 • 10 个月前

In what order will AI solve the three big problems in drug development? Ben Liu, co-founder of Formation Bio: "There are actually three consequential predictions: drug design and discovery, tox prediction, and clinical efficacy. We believe they will all one day happen, but the winners will be disproportionately right about the order." "The framework is: where are the benchmarks the cleanest, the data variability the lowest, and the feedback loops the shortest." "Drug discovery gets solved first... we know what a drug binding another drug looks like. The feedback loops are in minutes or days. Historically very low variability because it's physics, it's chemistry." "Tox is a little more complicated, but I know what a liver tox signal looks like. The rodents used are genetically identical. And tox studies are weeks, not years." "Clinical efficacy is gonna take the longest, the feedback loops are the longest, the data is the most highly variable, and you actually don't have really clean benchmarks for most diseases of what getting better looks like."

In what order will AI solve the three big problems in drug development? Ben Liu, co-founder of Formation Bio: "There are actually three consequential predictions: drug design and discovery, tox prediction, and clinical efficacy. We believe they will all one day happen, but the winners will be disproportionately right about the order." "The framework is: where are the benchmarks the cleanest, the data variability the lowest, and the feedback loops the shortest." "Drug discovery gets solved first... we know what a drug binding another drug looks like. The feedback loops are in minutes or days. Historically very low variability because it's physics, it's chemistry." "Tox is a little more complicated, but I know what a liver tox signal looks like. The rodents used are genetically identical. And tox studies are weeks, not years." "Clinical efficacy is gonna take the longest, the feedback loops are the longest, the data is the most highly variable, and you actually don't have really clean benchmarks for most diseases of what getting better looks like."

MTS

17,344 次观看 • 1 个月前