Podcasts about Drug discovery

Developing a cancer drug is one of the most expensive, slow, and failure-prone processes in modern science. PharosAI is trying to change that – by building multimodal, AI-ready datasets from donated cancer tissue samples and making them available to researchers, biotech companies, and clinicians.In this episode, Technical Director Adrian La Porta speaks with PharosAI's Dr Lucie Burgess (COO) and Dr Emma Colliver (Research Fellow) about what it takes to curate cancer data at scale, why federated learning matters for patient privacy, where AI is already transforming diagnostics, and what synthetic patients could mean for clinical trials.Whether you work in life sciences, pharma manufacturing, digital health, or clinical data governance, understanding how AI is reshaping the drug discovery pipeline has direct implications for how and when new facilities will need to be built. This one is worth your time.Topics covered:00:00 Introduction00:01 What is PharosAI?00:06 Why AI can fundamentally change cancer care00:08 The venture behind the mission00:12 Lowering the barrier for UK biotech00:16 Are we at an inflection point?00:20 AI in diagnostics – what's already working00:24 Misconceptions about AI in biotech00:30 Data – acquiring, cleaning, and structuring00:34 Patient privacy, consent, and NHS data00:37 What cancer care could look like in 10 years00:40 Why this work matters personallySend us Fan MailTo learn more about Bryden Wood's Design to Value philosophy, visit www.brydenwood.com. You can also follow Bryden Wood on LinkedIn.

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into a series of significant advancements shaping the landscape of our industry. As technology continues to redefine traditional paradigms, the collaboration between Pfizer and Chai Discovery exemplifies this trend. By harnessing artificial intelligence, particularly through custom models like Chai-3, this partnership aims to revolutionize drug discovery. The integration of AI promises not only to accelerate the identification of biologics and antibodies but also to optimize resource allocation in research and development. Such technological integration could pave the way for an enhanced pipeline of innovative treatments, marking a transformative shift in how therapeutic candidates are developed. In the realm of regulatory developments, Lupin's Ranluspec has recently received FDA approval as an interchangeable biosimilar targeting VEGF-A for various retinal conditions. This move underscores the importance of biosimilars in providing cost-effective alternatives to expensive biologics, thereby expanding patient access to essential treatments for conditions like macular degeneration. Additionally, the MHRA's marketing authorization for Aujemflu, an adjuvanted trivalent influenza vaccine for adults aged 50 and over, reflects ongoing efforts to bolster protection against infectious diseases among vulnerable populations. Clinical trial advancements continue to highlight significant progress in therapeutic development. Otsuka Pharmaceuticals' Phase 3 data on Voyxact has shown promising stabilization of kidney function in patients with Immunoglobulin A nephropathy. This protein therapy targets autoimmune pathways, offering new hope for managing this chronic kidney condition. Similarly, Autobahn Therapeutics' Elunetirom has advanced to a pivotal trial following Phase 2 success in treating bipolar depression. This showcases the potential of small molecule therapies targeting thyroid hormone receptors. Meanwhile, Hikma Pharmaceuticals' victory in a landmark patent case regarding skinny labels marks an important development in pharmaceutical intellectual property rights. The unanimous Supreme Court ruling against Amarin supports the legitimacy of using skinny labels to market generic versions of drugs for non-patented indications. This decision could enhance market competition and drive down healthcare costs, setting a precedent for future intellectual property disputes. On the business front, strategic partnerships and mergers continue to shape industry dynamics. Gilead Sciences' acquisition of Ouro Medicines for $1.675 billion strengthens its autoimmune inflammation pipeline. This transaction exemplifies how major deals are reshaping therapeutic portfolios in response to growing demand for treatments targeting rare diseases. Financially, Solix Pharmaceuticals' success in raising $71 million to advance its siRNA pipeline across multiple therapeutic areas demonstrates investor confidence in RNA-based therapeutics as a promising frontier for innovative treatments. Conversely, challenges persist as evidenced by Takeda's $2.5 billion legal provision over an antitrust case related to Amitiza, underscoring ongoing financial risks associated with litigation in the pharmaceutical sector. Corporate restructuring also signals shifts within the industry landscape. Fulcrum Therapeutics' decision to lay off 85% of its workforce following the discontinuation of its sickle cell disease candidate highlights the volatility and high stakes inherent in drug development. Overall, these developments illustrate a dynamic landscape where scientific innovation is propelled by AI-driven approaches and strategic collaborations while regulatory victories and financial maneuvers shape market dynamics. These trends have profound implications for patient care by potentially accelerating the availability of novel therapies and fostering a competitive environment that drives down costs. As we look ahead, stakeholders must navigate these complexities effectively to harness opportunities and address challenges within this rapidly evolving industry landscape. The ability to adapt and capitalize on emerging trends will be crucial as these sectors continue to evolve, ultimately enhancing patient care and advancing therapeutic frontiers globally. Thank you for joining us today on Pharma Daily; stay tuned for more insights into the ever-changing world of pharmaceuticals and biotech.Support the show

Send us Fan MailOver the last several years, artificial intelligence has transformed from a speculative concept in healthcare into one of the most heavily funded movements in pharmaceutical and biotech history.But in 2026, the conversation is changing. The question is no longer whether AI can generate molecules, analyze pathology slides, or identify patterns in clinical data. The real question is: which of these technologies can survive the complexity of biology, regulatory scrutiny, clinical validation, and real-world deployment?Today we're joined once again by returning guest Bill Taranto, President and Founding Partner of the Merck Global Health Innovation Fund ( https://www.merckghifund.com/ ), alongside Managing Director, Dr. David Rubin, who brings a deep scientific background spanning molecular biology, oncology, precision medicine, and digital health investing.Together, Bill and David sit at a unique intersection of pharma strategy, venture investing, translational science, and clinical deployment. Through investments across AI-driven drug discovery, precision medicine, diagnostics, and digital health - including early involvement with companies like PathAI - they've had a front-row seat to what's actually working in AI-enabled healthcare…and what still breaks down when these systems encounter real-world medicine.Today we'll explore where AI is genuinely creating value across the pharmaceutical stack, why some approaches are beginning to achieve meaningful validation, what investors and pharma companies are now demanding beyond hype, and how entirely new experimental models - from organoids to AI-native biology platforms - may reshape the future of drug development.#AI #ArtificialIntelligence #DrugDiscovery #Biotech #Pharma #HealthcareAI #PrecisionMedicine #DigitalHealth #Oncology #PathAI #Roche #Biology #MachineLearning #BiotechInvesting #VentureCapital #Merck #DigitalPathology #ClinicalTrials #FutureOfMedicine #LifeSciences #AIHealthcare #MedicalInnovation #Biotechnology #CancerResearch #PrecisionOncology #Bioinformatics #HealthcareInnovation #Pharmaceuticals #MedTech #ProgressPotentialPossibilitiesSupport the show

In this episode of the AI Agent & Copilot Podcast, Giuseppe Ianni, podcast host and industry interviewer, is joined for a second time by Nandita Puri, PhD Researcher at Georgia Tech working at the intersection of bioinformatics and biochemistry. The conversation explores how AI is transforming drug discovery, accelerating hypothesis generation, reducing experimental costs, improving success rates, enabling rare disease research, and paving the way for virtual cell simulation. Key Takeaways AI Is Creating a New Drug Discovery Workflow: Puri describes a major transition from traditional laboratory-first research toward a hybrid approach combining computational and experimental science. Researchers can now use AI, machine learning, and pattern recognition to analyze massive biological datasets before conducting expensive laboratory work. According to Puri, "I see a healthy combination of 50% dry lab and wet-lab validation becoming the emerging standard." This shift allows scientists to move beyond manual analysis and leverage computational intelligence to generate stronger hypotheses, identify promising targets faster, and focus laboratory resources on the most promising opportunities. Higher Success Rates Mean Lower Costs and Less Waste: One of the most immediate benefits of AI in drug discovery is improved experimental efficiency. Puri notes that individual experiments can cost "$10,000-$12,000" and historically have carried significant failure risk. By consolidating fragmented datasets and identifying meaningful biological signals, AI helps researchers prioritize stronger hypotheses before entering the laboratory. Puri explained that some AI-assisted binder-development efforts achieved "40% 50% of success rate," compared with previous rates of "10% 5%." These improvements reduce wasted resources, shorten research timelines, and allow scientific teams to evaluate more potential treatments with the same budget. AI Is Unlocking Opportunities for Rare Disease Research: Rare diseases have historically faced funding and development challenges due to limited patient populations and expensive clinical validation requirements. Puri explains that AI is helping overcome these barriers by generating synthetic datasets, identifying hidden biological relationships, and revealing common signaling pathways between diseases. She notes that "AI is really, really helping rare disease industry to go forward." Visit Cloud Wars for more.

Drug discovery is already an incredibly difficult and tedious process, but what happens when there's little financial incentive to develop the medicine at all? On this exciting episode of Let's Talk Chemistry edited by Presley Vu, hosts Poorvi Iyer and Nina Deng talk with Dr. Michael Pollastri, senior vice provost and academic lead of the Roux Institute at Northeastern University. From his time as a bench chemist at Pfizer to his current role leading academic research on diseases affecting some of the world's most vulnerable populations, Dr. Pollastri discusses the realities of neglected diseases and the role academic labs play in addressing gaps in global healthcare research. We hope you enjoy!

Most people don't realize how broken drug discovery really is. According to Liran Belenzon, companies can spend seven years and hundreds of millions of dollars on a drug candidate—only to see it fail in human trials. In this episode, Liran explains why biology is one of the most complex systems humans have ever tried to understand and how AI can help researchers uncover patterns humans miss. The discussion explores why even small improvements in success rates could completely reshape healthcare outcomes worldwide. This episode is a powerful glimpse into the future of science and medicine. Learn more about your ad choices. Visit megaphone.fm/adchoices

Today I am welcoming two guests: Quin Wills, CEO of Ochre Bio, a biotech developing RNA therapies for chronic liver disease using AI models, and Stéphane Barges, CEO of Lexogen, an RNA transcriptomics company and NGS service provider. It's a deep dive into cutting edge transcriptomics, human-first data, and artificial intelligence.00:55: The challenges of liver disease04:44: How Lexogen supports NGS drug discovery07:29: Major transcriptomics developments10:05: Designing high quality AI data15:47: How the Ochre-Lexogen partnership began17:17: Why a specialist partner is essential for scale18:21: Lexogen delivers on the massive sequencing project21:50: Why high quality data is crucial27:02: Lexogen's role in AI discovery34:51: Future plans and directionsThis episode was produced with the support of Lexogen. Interested in being a sponsor of an episode of our podcast? Discover how you can get involved here! Stay updated by subscribing to our newsletterTo dive deeper into the topic: Deep phenotyping brings accuracy to precision medicineSpatial Transcriptomics: A window into diseaseSpatial Transcriptomics Landscape Shifts With Two Major Acquisitions

Nicolai Tangen meets Pfizer CEO Albert Bourla for a wide-ranging conversation on leadership, science, and the future of global healthcare. Bourla reflects on leading Pfizer through the COVID-19 vaccine breakthrough and how it transformed the company. The discussion also dives into Pfizer's strategic shift toward innovative medicine, including major investments in oncology and obesity, and the high-stakes decisions behind multibillion-dollar acquisitions. Looking ahead, the conversation explores how artificial intelligence is set to transform drug discovery, clinical trials, and the broader healthcare system. Bourla offers a candid view on global competition, particularly the rapid rise of China in biotech, and what it will take for companies like Pfizer to stay ahead. Beyond business, Bourla opens up about leadership, how to build resilience, foster organizational confidence, and continuously evolve as a CEO. He also shares a deeply personal story about his mother, a Holocaust survivor, and how her perspective shaped his optimism and drive.In Good Company is hosted by Nicolai Tangen, CEO of Norges Bank Investment Management. New full episodes every Wednesday, and don't miss our Highlight episodes every Friday. The production team for this episode includes Isabelle Karlsson and PLAN-B's Niklas Figenschau Johansen, Sebastian Langvik-Hansen and Pål Huuse. Background research was conducted by Isabelle Karlsson. Watch the episode on YouTube: Norges Bank Investment Management - YouTubeWant to learn more about the fund? The fund | Norges Bank Investment Management (nbim.no)Follow Nicolai Tangen on LinkedIn: Nicolai Tangen | LinkedInFollow NBIM on LinkedIn: Norges Bank Investment Management: Administrator for bedriftsside | LinkedInFollow NBIM on Instagram: Explore Norges Bank Investment Management on Instagram Hosted on Acast. See acast.com/privacy for more information.

Send us Fan MailPharma has only explored a tiny fraction - less than one-tenth of one percent - of all possible drug-like molecules. So the question is: what happens when AI suddenly opens up the other 99.9%?Dr. Olga Nissan, Ph.D. is Vice President of Business Development at Evogene ( https://evogene.com/ ) where she leads pharmaceutical partnerships for the company's ChemPass AI generative chemistry platform - an advanced system designed to dramatically expand the searchable universe of drug-like molecules.With over 15 years of experience spanning biotech, pharma, and computational biology, Dr. Nissan operates at the intersection of science, strategy, and commercialization. Prior to Evogene, she was Co-Founder and CEO of Protica Bio, a precision oncology company focused on translating novel biological insights into therapeutic opportunities. Earlier in her career, she held scientific and operational roles at Teva Pharmaceutical Industries, as well as EcoPhage and BiomX, building expertise across microbiology, molecular biology, and translational R&D.Dr. Nissan has a strong track record of advancing technologies from early discovery through clinical and commercial partnerships, and of aligning cutting-edge innovation with the practical needs of pharmaceutical companies. She earned her Ph.D. and completed her postdoctoral training at the Weizmann Institute of Science.#DrugDiscovery #AIinPharma #GenerativeAI #Biotech #PharmaInnovation #ChemistryAI #MachineLearning #DrugDevelopment #PharmaceuticalIndustry #Bioinformatics #AIResearch #ChemPass #Evogene #ComputationalChemistry #FutureOfMedicine #DrugDesign #DeepLearning #HealthcareInnovation #BiotechNews #SciencePodcastSupport the show

Send us Fan MailWe used to think of DNA as something we read. Now we're starting to treat it like something we write - and that changes everything about how medicine gets made.Dr. Patrick Finn, Ph.D. is President and COO of Twist Bioscience ( https://www.twistbioscience.com/ ), a company that's helping turn biology into something you can engineer, iterate, and even industrialize. Dr. Finn has spent his entire career building the infrastructure layer of modern biotech - from sequencing and sample prep at Beckman Coulter and Invitrogen, to scaling commercial platforms at Agilent Technologies and now Twist.Dr. Finn also served as Vice President of Sales and Marketing for Enzymatics (recently acquired by QIAGEN), leading commercial activities for North America and Europe, delivering significant top line growth and expanding the base of business to business customers.So this isn't just a conversation about the future - it's about how the tools that make the future possible are actually built and deployed.On this episode we go beyond the buzzwords and dig into what it really means to “program biology”, and how close we actually are to designing medicines the way we design software.In addition to his role at Twist, Dr. Finn currently serves on the Scientific Advisory Board of Lasergen and previously served on the Scientific Advisory Board of Enzymatics. He holds a PhD in Nucleic Acid Chemistry from Southampton University and a BSc Hons in Chemistry from Heriot-Watt University.#SyntheticBiology #Biotech #DrugDiscovery #AIinHealthcare #ArtificialIntelligence #DNA #Genomics #Bioengineering #ProgrammableBiology #CRISPR #GeneTherapy #Biotechnology #LifeSciences #MachineLearning #AIResearch #PharmaInnovation #Biology #SciencePodcast #FutureOfMedicine #Biodesign #SyntheticDNA #GenomeSequencing #Bioeconomy #DeepTech #HealthTechSupport the show

University of Utah chemist Matthew Sigman explains how machine learning is transforming drug discovery. By predicting how molecules form, especially their critical “handedness,” new tools can dramatically cut the time, cost, and trial-and-error required to develop life-saving medicines.

What does it take to win the Brain Innovation Days 2025 Pitch Competition?In this episode of Brain Talks, Antoni Homs (OWL Lifesciences) shares the story behind his startup, the challenges startups face in the brain space, and why Brain Innovation Days is a key opportunity for emerging companies in the field.

Jared Dashevsky, MD (Healthcare Huddle) and Ala Alenazi, PhD (Kinnevik) join Mustafa to discuss:The rise of the AI Broker (a middleman that's about to emerge)Palantir's £330M NHS break clauseOpenAI as pharma. We also discuss why drug discovery needs a "Human Genome Project 2.0"

Send us Fan MailIs the era of just managing Parkinson's symptoms finally coming to an end?In this clip from our episode “How AI Is Helping The Fight Against Parkinson's”, host David E. Williams and guest Gene Mack, CEO of Gain Therapeutics, share why the early signals from their lead drug candidate are too compelling to ignore.Listen to the full episode here

Have you ever wondered why new medicines can take decades to be developed and why the process is so costly? On this exciting episode of Let's Talk Chemistry edited by Presley Vu, hosts Amber Bakkum, Poorvi Iyer, and Nina Deng go behind-the-scenes of the pharmaceutical industry with Dr. John LaMattina, former president of Pfizer Global Research and Development and current biotech advisor. Dr. LaMattina shares his experience first as a bench scientist to becoming the head of global Research and Development at Pfizer Inc. He talks about the important skills needed for chemists to work their way to leadership positions and dispels common myths about the pharmaceutical industry. We hope you enjoy!

Can artificial intelligence help make cancer therapies safer, more targeted, and more effective?In this episode of BioTalk Unzipped, Gregory Austin sits down with Dr. Daniel Reker, Assistant Professor at Duke University, for a wide-ranging conversation on active machine learning, nanomedicine, drug delivery, and the future of AI in biomedical research.This episode is brought to you by Leucentra.Inspired by Science Empowered by IThttps://leucentra.com/Dr. Reker works at the intersection of AI, chemistry, biomedical engineering, pharmacology, and molecular medicine. His lab develops computational and experimental approaches to better understand small molecules, nanoformulations, and drug delivery systems.The conversation explores how machine learning can support drug discovery and development, especially in areas where datasets are small and the biology is complex. Dr. Reker explains why nanoformulations may be able to improve targeted drug delivery, reduce toxicity, and potentially revive therapeutic agents that previously failed because of safety or tolerability issues.Gregory and Dr. Reker also discuss explainable AI, the risks of black box thinking, AI bias, predictive modeling, FDA considerations, non-animal models, and the responsible use of AI in education and science.Topics include:• Active machine learning in drug discovery• AI and nanomedicine• Cancer therapy and targeted drug delivery• How nanoformulations may reduce toxicity• Small datasets in biomedical AI• Explainable AI and scientific trust• AI bias and model limitations• Regulatory implications for predictive models• The role of AI in education and cognitive development• The future of integrated data in drug developmentGuest bio:Dr. Daniel Reker is an Assistant Professor at Duke University. His research focuses on computational and experimental approaches to molecular medicine, including active machine learning, drug delivery, nanoformulations, small molecules, and translational pharmacology. He was named to Forbes 30 Under 30 Europe in Science and Healthcare.Guest contact:Dr. Daniel RekerEmail: daniel.reker@duke.eduLinkedIn: https://www.linkedin.com/in/danielreker/Duke website: https://rekerlab.pratt.duke.edu/Connect with BioTalk Unzipped:Gregory Austinhttps://www.linkedin.com/in/gregoryaustin1/Dr. Chad Briscoehttps://www.linkedin.com/in/chadbriscoe/BioTalk Unzipped uncovers the stories behind medical progress through conversations with innovators across biotech, pharma, medtech, bioanalysis, clinical research, regulatory science, and drug development.

Send us Fan MailFor decades, Parkinson's patients have been offered only symptom management. No drug has ever slowed the disease itself. A small clinical stage biotech may be about to change that.Gene Mack, CEO, Gain Therapeutics joins host David E. Williams to discuss the science behind a potential first disease modifying therapy for Parkinson's, how AI is accelerating drug discovery, and what it takes to build a biotech in one of the toughest capital markets in years.

Invest Like the Best: Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- My guest today is Alex Karnal. Alex is the co-founder and managing partner of Braidwell, a life sciences investment firm he built after spending 15 years at Deerfield Management. The frame we use throughout the episode is the health stack. Alex talks about how most of the diseases that will claim most of our lives are already addressable with medicines that exist today. We work through the five layers of what a defensive health strategy looks like, why GLP-1 medicines represent the first commercial proof that people are ready to be proactive about their health, and why PCSK9 inhibitors may ultimately be the more important drug class even though they get far less attention. We also get into the science and business of drug discovery itself — why most of the published literature that AI companies are training on cannot be replicated, what it would mean to have a truly agentic scientific lab running 24 hours a day, and why Alex believes we are now on a deterministic curve toward scientific superintelligence in biology. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Visit ⁠vanta.com/invest⁠.  ----- ⁠WorkOS⁠ is a developer platform that enables SaaS companies to quickly add enterprise features to their applications. Visit⁠⁠ ⁠WorkOS.com⁠⁠⁠ to transform your application into an enterprise-ready solution in minutes, not months. ----- ⁠Rogo⁠ is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like the Best (00:02:29) Intro: Alex Karnal (00:03:15) State of the Union: GLP1s and Life Sciences (00:07:01) The Health Stack Framework (00:12:49) Breaking Down the 5 Defensive Layers (00:21:18) GLP-1: What's Driving the Inflection (00:28:28) Diet vs. Drugs: Is Food Enough? (00:31:15) Barriers to Access: Complexity, Cost & Compliance (00:35:04) PCSK9: The Closest Thing to a Free Lunch (00:44:10) Alzheimer's & Neurodegenerative Disease (00:46:59) Cancer: Early Detection & New Treatments (00:54:49) Body Imaging & Diagnostic Trade-offs (00:56:31) How Drugs Are Discovered (01:02:39) AI in Drug Discovery (01:10:57) The Automated Lab of the Future (01:13:05) Peptides & Citizen Pharmacology (01:16:45) Alex's Background (01:28:25) Braidwell's Investment Approach (01:30:39) The Kindest Thing

In this episode of Speaking of Mol Bio, host Steve Lewis speaks with Dr. Leif Larsen, Director of Biology at Vipergen, about the power of DNA-encoded libraries (DELs) in modern drug discovery. DEL technology enables researchers to screen extremely large chemical libraries by attaching a unique DNA barcode to each compound, allowing millions, or even hundreds of millions, of compounds to be analyzed simultaneously through sequencing.  Larsen explains how Vipergen's platform flips traditional screening methods by storing massive compound libraries in a single tube and identifying binding interactions through DNA sequencing. He also describes their proprietary Binder Trap Enrichment (BTE) method, which links DNA barcodes when compounds successfully bind their protein targets. One of the company's most innovative advances is performing DEL screening inside living Xenopus oocytes. By expressing target proteins in these large cells and microinjecting DNA-encoded libraries, researchers can evaluate binding events in a physiologically relevant environment. The discussion also explores how this technology accelerates early drug discovery timelines and enables screening of difficult targets such as transcription factors and membrane proteins. Larsen closes by highlighting emerging areas such as PROTAC-based targeted protein degradation and how DEL screening can help identify molecules suitable for these next-generation therapeutic strategies. Subscribe to get future episodes as they drop and if you like what you're hearing we hope you'll share a review or recommend the series to a colleague.  Visit the Invitrogen School of Molecular Biology to access helpful molecular biology resources and educational content, and please share this resource with anyone you know working in molecular biology. For Research Use Only. Not for use in diagnostic procedures.

My guest today is Alex Karnal. Alex is the co-founder and managing partner of Braidwell, a life sciences investment firm he built after spending 15 years at Deerfield Management. The frame we use throughout the episode is the health stack. Alex talks about how most of the diseases that will claim most of our lives are already addressable with medicines that exist today. We work through the five layers of what a defensive health strategy looks like, why GLP-1 medicines represent the first commercial proof that people are ready to be proactive about their health, and why PCSK9 inhibitors may ultimately be the more important drug class even though they get far less attention. We also get into the science and business of drug discovery itself — why most of the published literature that AI companies are training on cannot be replicated, what it would mean to have a truly agentic scientific lab running 24 hours a day, and why Alex believes we are now on a deterministic curve toward scientific superintelligence in biology. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Visit ⁠vanta.com/invest⁠.  ----- ⁠WorkOS⁠ is a developer platform that enables SaaS companies to quickly add enterprise features to their applications. Visit⁠⁠ ⁠WorkOS.com⁠⁠⁠ to transform your application into an enterprise-ready solution in minutes, not months. ----- ⁠Rogo⁠ is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like the Best (00:02:29) Intro: Alex Karnal (00:03:15) State of the Union: GLP1s and Life Sciences (00:07:01) The Health Stack Framework (00:12:49) Breaking Down the 5 Defensive Layers (00:21:18) GLP-1: What's Driving the Inflection (00:28:28) Diet vs. Drugs: Is Food Enough? (00:31:15) Barriers to Access: Complexity, Cost & Compliance (00:35:04) PCSK9: The Closest Thing to a Free Lunch (00:44:10) Alzheimer's & Neurodegenerative Disease (00:46:59) Cancer: Early Detection & New Treatments (00:54:49) Body Imaging & Diagnostic Trade-offs (00:56:31) How Drugs Are Discovered (01:02:39) AI in Drug Discovery (01:10:57) The Automated Lab of the Future (01:13:05) Peptides & Citizen Pharmacology (01:16:45) Alex's Background (01:28:25) Braidwell's Investment Approach (01:30:39) The Kindest Thing

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we're exploring a fascinating realm where technology and biology converge, starting with a deepening relationship between biopharma and artificial intelligence. Novartis CEO Vas Narasimhan's recent appointment to the board of AI company Anthropic signals the strategic integration of AI into drug discovery and development processes. This collaboration highlights a growing trend where pharmaceutical companies are increasingly leveraging AI to optimize clinical trials, streamline drug discovery, and personalize patient care strategies. Similarly, Novo Nordisk has announced a strategic partnership with OpenAI to integrate AI technologies across various facets of its operations, including drug discovery and manufacturing. By leveraging OpenAI's machine learning capabilities, Novo Nordisk aims to streamline research efforts and accelerate therapeutic identification—a collaboration reflecting AI's growing role as an essential tool for maintaining competitiveness in drug development. Additionally, Amazon Web Services' launch of the Amazon Bio Discovery AI tool marks another milestone. Designed to expedite antibody design and drug discovery processes, it provides researchers with robust AI-driven platforms enhancing therapeutic design speed and accuracy. The emphasis on monoclonal antibodies aligns with industry trends focusing on targeted therapies for diseases such as cancer. Meanwhile, Eli Lilly's new obesity treatment, Foundayo, has caught the FDA's attention due to potential safety concerns. Despite progressing with its launch, the FDA has requested additional safety information to address unexpected serious risks associated with the drug. This highlights the ongoing regulatory scrutiny that accompanies novel treatments, especially in areas like obesity where patient populations are large and diverse. In another strategic move, Eli Lilly's acquisition of Crossbridge Bio for up to $300 million aims to bolster its oncology pipeline with dual-payload antibody-drug conjugates (ADCs). This acquisition reflects a strategic move enhancing Eli Lilly's position in oncology by integrating cutting-edge ADC technologies known for delivering cytotoxic agents directly to cancer cells while minimizing off-target effects. On another front, Travere Therapeutics is mapping a pathway to a potential $3 billion opportunity in the U.S. market following significant approval for its treatment Filspari, targeted at rare kidney diseases. This approval underscores the increasing focus on rare diseases, which present lucrative opportunities for pharmaceutical companies due to significant unmet needs and often high-cost treatments. Astellas' manufacturing strategy underscores the importance of reliable supply as a critical bridge from research to patient care. Led by Chief Manufacturing Officer Rao Mantri, this strategy highlights how manufacturing excellence can significantly impact drug availability and patient outcomes. It emphasizes that production reliability is vital in ensuring groundbreaking research translates into accessible medical treatments. In contrast, a slowdown in IPOs has been noted amidst an aggressive merger and acquisition spree by major pharmaceutical companies. This consolidation trend reflects strategic shifts within the industry as companies seek to bolster pipelines through acquisitions rather than organic growth. Such dynamics indicate a strategic pivot as firms prioritize acquiring promising assets over developing them from scratch. Ionis Pharmaceuticals' recent win in a drug naming competition exemplifies the complexities involved in branding within the pharmaceutical sector. Crafting a drug name that is memorable yet distinctive involves balancing marketability with regulatory requirements—a reflection of the intSupport the show

In this episode, Frank and Candace sit down with computational chemist Mustafa Javaheri to explore how quantum insights are revolutionizing drug design. Together, they unpack the complex process of screening millions of compounds for drug discovery, discuss the unique advantages quantum computing brings to modeling biological systems, and address common misconceptions about the technology. From the challenges of building powerful quantum hardware to the interplay between AI and quantum chemistry, this conversation shines a light on the present realities and future possibilities at the cutting edge of science. Whether you're fascinated by the promise of faster drug discovery or simply curious about how quantum computers really work, this episode is packed with insights you won't want to miss.LinksMostafa's LinkedIn - https://www.linkedin.com/in/mostafa-javaheri-moghadam/Watch this show on YouTube - https://youtu.be/J0xbQzSoW-kTime Stamps00:00 Drug discovery using computational chemistry04:46 How quantum computers analyze proteins06:54 Comparing quantum and classical computers11:47 Current progress in quantum chemistry17:24 Using lasers and low temperatures19:03 How quantum computers process information24:30 Quantum mechanics in drug design28:22 Supportive mentors and new ideas32:16 Finding joy in problem-solving34:49 Importance of quality data in AI

Abbas Kazimi, CEO of Boston-based Nimbus Therapeutics, on computation and culture for drug discovery.

In this episode of Sounds of Science, Lauren and Matt Noonan share their powerful journey following their daughter Jane's diagnosis with Mowat-Wilson Syndrome. From unexpected medical challenges to finding community and launching their own nonprofit, the OURS Foundation, they discuss how advocacy, collaboration, and emerging research are shaping new hope for families living with rare diseases. Charles River | ASO Development Charles River | ASO Screening Services Charles River | Rare Disease Charles River | Rare Disease Research for Drug Development Mowat Wilson Foundation

In this episode of Data in Biotech, Ross Katz sits down with Robert Abel, Chief Scientific Officer of the Platform at Schrödinger, to explore how physics-based computational modeling is transforming drug discovery.  Robert unpacks why machine learning alone isn't enough to navigate the vast complexity of chemical space - an estimated at 10⁶⁰ possible drug-like molecules - and how integrating atomistic simulations with ML creates a more accurate, reliable, and scalable approach to identifying viable drug candidates. From free energy perturbation calculations to generative AI, Robert offers a rare inside look at how Schrödinger's technology platform is accelerating the path from target identification to clinical candidate and where the field is headed next. What you'll learn in this episode:  >> Why chemical space (~10⁶⁰ molecules) makes purely data-driven ML approaches fundamentally insufficient for drug discovery, and how physics-based sampling solves the training data problem >> How free energy perturbation (FEP) calculations enable quantitative prediction of protein-ligand binding affinities at near-experimental accuracy (~1.2 kcal/mol RMSE) >> How Schrödinger's active learning framework combines physics-based simulations and ML to triage billions of candidate molecules before committing to wet lab synthesis >> Why Schrödinger operates across three business lines; software licensing, collaborative programs, and proprietary drug discovery and how each strengthens the underlying technology platform >> Where the next frontiers lie: routine anti-target selectivity profiling, retrosynthetic AI integration, and the expanding role of generative ML in de novo molecular design Meet our guest: Robert Abel is Chief Scientific Officer, Platform at Schrödinger, where he helps lead the scientific direction behind computational approaches that support modern drug discovery and molecular design. With a PhD in Chemical Physics from Columbia University and a deep background in computational chemistry, he has held multiple senior science leadership roles at Schrödinger, guiding teams that build and scale scientific methods into production-grade platforms used across research and industry. Connect with Robert Abel on LinkedIn  About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz on LinkedIn Connect with us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

This is the latest episode of the free DDW Narrated Podcast. The episode covers two articles written for DDW Volume 25, Issue 2, Spring 2024. The first article is called 'Why more work is needed to fight antimicrobial resistance'. In the article, DDW Editor Reece Armstrong speaks to Professor Janet Hemingway CBE about current efforts in tackling antimicrobial resistance. The second article is called 'The future outlook for mRNA therapies'. In the piece, Reece Armstrong explores the potential opportunities for mRNA-based therapies. You can listen below, or find The Drug Discovery World Podcast on Spotify, Google Play and Apple Podcasts.

This Week In Startups is made possible by:Luma AI - https://lumalabs.ai/twistEvery.io -  https://every.ioLemon.io - https://Lemon.io/twistPlaud - https://Plaud.ai/twistToday's show:What do drug discovery, the creator economy, and AI vision models have in common? In the case of Metanova, Bitcast, and Score, the answer is Bittensor. Yes, each of the three companies leverages the Bittensor network to get more work done, more quickly, in a completely decentralized fashion.Metanova uses its subnet to run developer competitions to find exciting molecular candidates, parsing through a mountain of possibilities to pluck out the most promising for further investigation.Bitcast uses its subnet to collect visibility demand from brands, which is served by video creators. The company is focused on the crypto niche to start, but will expand in time to other technology topics.Score uses its subnet to generate highly performant, specialized vision models, which it then sells to customers through a platform (Manako).In each case, the Bittensor's economic engine unlocks global creativity to tackle tasks that were previously time-consuming, fragmented, or expensive to complete. Let's see how quickly each company can scale and whether startups building on Bittensor can grow faster than their non-decentralized peers.Timestamps:0:00 Intro2:19 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off!3:40 What is Bittensor?7:22 Metanova Labs joins the show9:28 Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist17:16 How Metanova tackles the multi-billion dollar cost of drug discovery20:48 Every.io - For all of your incorporation, banking, payroll, benefits, accounting, taxes or other back-office administration needs, visit https://every.io30:20 Bitcast joins the show31:23 Luma AI - Luma builds accessible, professional-grade AI tools for creatives. Try Luma Agents for free at https://lumalabs.ai/twist32:30 Mining crypto with YouTube36:48 Why Bitcast is focused on the crypto space to start39:11 How healthy is the creator economy?47:26 When will the AI bubble collapse?53:44 Score joins the show54:42 How Score will make vision AI more accessible57:16 VLMs v. LLMs1:01:14 Demo of the Manako platformSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisCheck out all our partner offers: https://partners.launch.co/Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com

Send us Fan MailIs this AI moment in healthcare really different from all the hype that came before?In this clip from our episode “A Brief History of AI in Healthcare”, Lekan Wang, Partner at JSL Health Capital, makes his case for why the data is finally backing up the optimism.Check out the full episode here

In this conversation, hosts Frank La Vigne and Candace Gillhoolley sit down with Helena Bahrami, a trailblazing AI and machine learning expert, entrepreneur, and quantum enthusiast currently based in Auckland, New Zealand. Together, they explore the transformative intersection of quantum computing, artificial intelligence, and neuroscience, uncovering how these fields are converging to tackle some of medicine's toughest challenges—like accelerating drug discovery and finding solutions for neurodegenerative diseases.Helena Bahrami shares her unique journey from an early fascination with quantum physics to pioneering research at the crossroads of brains, algorithms, and quantum circuits. The conversation also dives into the cultural shifts in STEM, the evolving role of women in quantum science, and the challenges and misconceptions organizations still face when it comes to adopting quantum technology.Whether you're curious about the real-world potential of quantum computers, the nuances of interdisciplinary research, or seeking advice on entering this fast-evolving field, this episode gives you an inspiring look at the future of science and innovation. So tune in as the Impact Quantum team breaks down barriers and brings cutting-edge science to life!LinksHelena's LinkedIn - https://www.linkedin.com/in/helenabahrami/ Watch on YouTube - https://youtu.be/dQF1oy3FrekTime Stamps00:00 "AI, Quantum, Neuroscience Innovation"06:18 From Physics to Quantum AI08:27 Quantum AI for Precision Medicine15:23 "Quantum Realm Fades in Classical"18:21 "Challenges of Spiking Neural Computing"19:59 "Building Research for Drug Discovery"24:17 "Barriers to Quantum Adoption"28:05 "Quantum Optimization in Drug Design"32:21 "Quantum Computing Revolution in Progress"34:08 Quantum Models for Drug Testing37:31 "Passion Shapes Future Careers"40:38 "Quantum Computing's Broad Future"46:45 "Quantum Computing: Strategic Considerations"48:08 "Quantum Computing Challenges and Promise"

Send us Fan MailAI has been promising to transform healthcare for decades. So what's actually different this time? From Palantir's early data integration work to the frontier of AI-driven drug discovery, the evidence for optimism is growing and so is the urgency to get it right.Lekan Wang, Partner, JSL Health Capital joins host John Driscoll to discuss the real history of AI, where it's already delivering results in healthcare, and what investors are betting on next.

In this episode, you'll be listening to  Akshay Rai, principal, Healthcare & Biotech Investments at Premji Invest and Viswa Colluru, CEO and founder, Enveda. They discuss how AI platforms must now prove themselves through data, focused pipelines and clinical readouts and that promises of faster, cheaper drug discovery are not enough to entice strong investor engagement.  HostJennifer Smith-Parker, Director of Insights, BioSpaceGuestsViswa Colluru, CEO & Founder, EnvedaAkshay Rai, Principal, Healthcare & Biotech Investments, Premji InvestDisclaimer: The views expressed in this discussion by guests are their own and do not represent those of their organizations. 

For more thoughts, clips, and updates, follow Avetis Antaplyan on Instagram: ⁠⁠⁠⁠⁠https://www.instagram.com/avetisantaplyan⁠⁠⁠⁠In this episode of The Tech Leader's Playbook, Avetis Antaplyan sits down with Alok Tayi, a Harvard-trained scientist, repeat tech founder, and the founder of Vibe Bio. Alok shares his journey from academia and engineering into entrepreneurship, where he built multiple pharmaceutical software companies collectively worth nearly $1 billion before launching Vibe Bio with a deeply personal mission. After his daughter was born with two rare diseases that had no available treatments, Alok turned his attention to one of biotech's most overlooked challenges: accelerating innovation for rare disease patients.The conversation explores how AI is changing drug discovery, why rare disease innovation has historically been underfunded, and how new tools, data, and regulatory pathways are creating fresh opportunities for founders and investors alike. Alok explains how Vibe Bio uses proprietary AI to evaluate drug programs, support pharma decision-making, and guide venture investments into high-potential therapeutics. He also shares hard-won lessons on leadership, mission-driven company building, culture, and the importance of staying obsessed with the problem while remaining flexible on tactics. This episode is a thoughtful look at the intersection of science, entrepreneurship, capital, and meaningful impact.TakeawaysIntro to Alok Tayi and the mission behind Vibe BioFrom scientist to serial founder in life sciences softwareHow Alok's daughter's diagnosis changed his life and careerLeadership lessons from scaling companies at different stagesWhat Vibe Bio actually does and how its AI worksWhy biotech and pharma are harder than most founders expectBalancing regulation, speed, and commercial realityWhy rare disease communities have been historically overlookedWhy rare disease innovation may become more viable nowWhy non-scientists can still play a major role in biotechCapital efficiency, biotech cycles, and the real funding questionWhy AI is an accelerant for biotech, not a replacementThe rise of parent-led and unconventional biotech foundersVibe Bio's AI platform versus its venture fundPlatform companies vs. individual therapy companiesHow AI-driven evaluation changes therapeutic investingAlok's biggest business and culture lessons as a founderBooks that shaped Alok's thinkingFinal advice on building with both impact and economic successChapters00:00 Intro to Alok Tayi and the mission behind Vibe Bio01:09 From scientist to serial founder in life sciences software03:16 How Alok's daughter's diagnosis changed his life and career04:28 Leadership lessons from scaling companies at different stages06:48 What Vibe Bio actually does and how its AI works10:37 Why biotech and pharma are harder than most founders expect13:51 Balancing regulation, speed, and commercial reality15:54 Why rare disease communities have been historically overlooked17:38 Why rare disease innovation may become more viable now19:25 Why non-scientists can still play a major role in biotech22:04 Capital efficiency, biotech cycles, and the real funding question24:33 Why AI is an accelerant for biotech, not a replacement26:57 The rise of parent-led and unconventional biotech founders29:50 Vibe Bio's AI platform versus its venture fund33:43 Platform companies vs. individual therapy companies37:12 How AI-driven evaluation changes therapeutic investing39:48 Alok's biggest business and culture lessons as a founder43:15 Books that shaped Alok's thinking46:22 Final advice on building with both impact and economic success48:29 Where to find Alok and Vibe BioAlok Tayi's Social Media Links:https://www.linkedin.com/in/aloktayi/https://x.com/aloktayiResources and Links:⁠⁠⁠⁠⁠https://www.hireclout.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.podcast.hireclout.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/hirefasthireright⁠

In this episode of IDEA Collider, host Mike Rea sits down with Nello Mainolfi, founder and CEO of Kymera Therapeutics. Nello shares his journey from medicinal chemist to biotech founder and discusses the opportunities and lessons learned from building a company at the forefront targeted protein degradation.    Episode Timestamps;  00:00 Welcome to Idea Collider  00:18 Kymera 2026 Catalysts  02:33 De Risking with Degraders  02:54 Targeted Protein Degradation 101  06:20 KT-621 STAT6 Degrader 08:16 Rethinking Type 2 Disease Treatment 12:21 Nello's Path to CEO  16:32 Building a Standalone Biotech  23:19 Partnering with Big Pharma  26:51 Culture and Office Energy  32:03 Capital Plan and Potential Launches 36:24 Belief and Execution Mindset  39:00 Life Outside the Lab  40:07 Where to Follow Kymera  40:35 Closing Thanks    Don't forget to Like, Share, Subscribe, Rate, and Review!      Keep up with Nello Mainolfi & Kymera LinkedIn: https://www.linkedin.com/in/nello-mainolfi-2b55421a/ or https://www.linkedin.com/company/kymeratherapeutics/ Website: https://www.kymeratx.com/      Follow IDEA Pharma On;  Website: https://www.ideapharma.com/  LinkedIn: https://www.linkedin.com/company/idea-pharma/      Listen to more fantastic podcast episodes: https://podcast.ideapharma.com/

In this episode of EBRC In Translation, Talia Jacobson and Will Grubbe interview Jim Collins of MIT, a pioneer of synthetic biology. He reflects on his path from human biomechanics and early gene circuit design to AI-driven antibiotic discovery.Collins discusses how deep learning is enabling new antibiotics despite market challenges, what makes research translation succeed, and the growing role of computation in biology. He also recounts developing RNA diagnostics and early CRISPR-based platforms like SHERLOCK, along with efforts to expand accessible infectious disease testing. Stay up to date with PHARE BIO for antibiotic discovery.For more information about EBRC: Visit our website at ebrc.org. If you are interested in getting involved with the EBRC Student and Postdoc Association, fill out a membership application for graduate students and postdocs or for undergraduates and join today! Transcription:Episode transcripts are the unedited output from Whisper and likely contain errors.

80% of all autoimmune diseases occur in women, and no one can explain why. Cancer cells are always present in your body, but it's only when your T cells go into energy deficit that cancer starts overtaking the system. And here's what almost no one is talking about: the mitochondria in your immune cells are the reason MS, chronic fatigue, neurodegeneration, and even cancer progression happen when they happen. In this episode, I sit down with Dr. Anurag Singh, MD, PhD immunologist who spent 20 years studying mitochondria and screened 4000 compounds from pomegranates to discover one molecule that changes cellular aging. We break down immunometabolism, the emerging field linking immune health and metabolism, why your T regulatory cells are the CEOs of your immune system, how mitochondrial dysfunction in immune cells triggers autoimmune conditions, and why rejuvenating mitochondria can get your immune system in check to defeat cancer. We also cover NAD+ (and why NMN and NR supplements don't work the way people think), the creatine sweet spot for muscle quality (500mg-1g, not the 5g everyone's taking), why Parkinson's is linked to paraquat, a mitochondrial toxin used in fertilizers and dry cleaning and how AI is fast-tracking the discovery of next-generation molecules for neurodegeneration. This conversation completely shifted how I think about immune health, brain protection, and what's actually driving the diseases we fear most. Reduce your risk of Alzheimer's with my science-backed protocol for women 30+: https://go.neuroathletics.com.au/youtube-sales-page Subscribe to The Neuro Experience for evidence-based conversations at the intersection of brain science, longevity, and performance. _____ TOPICS DISCUSSED 00:00 Intro: Why 80% of Autoimmune Diseases Occur in Women 01:24 Why Dr. Anurag Became an Immunologist 03:19 Immunometabolism: The Link Between Immune Health and Metabolism 04:20 T Cells, B Cells, and the Thymus Gland 05:51 MS and Autoimmune Disease: The T Regulatory Cell Problem 11:32 Mitochondrial Dysfunction and Immune Exhaustion 18:45 Cancer Cells and T Cell Energy Deficit 24:10 Urolithin A: Screening 4000 Pomegranate Compounds 31:20 Mitophagy and Autophagy: Cellular Housekeeping 38:50 NAD+ vs NMN and NR Supplements: What Actually Works 43:15 Creatine Dosing: The 500mg-1g Sweet Spot for Muscle Quality 48:30 Gut-Brain Connection and Neurodegeneration 50:54 Parkinson's Disease and Paraquat: The Mitochondrial Toxin 53:25 AI in Drug Discovery and Next-Generation Molecules 55:38 Skincare and Mitochondrial Health: Collagen Synthesis _______ Thank you to our sponsors KetoneIQ: https://ketone.com/NEURO for 30% OFF Caraway: Carawayhome.com/neuro10 Jones Road Beauty: https://www.jonesroadbeauty.com - Use code NEURO _______ I'm Louisa Nicola - clinical neurophysiologist - Alzheimer's prevention specialist - founder of Neuro Athletics. My mission is to translate cutting-edge neuroscience into actionable strategies for cognitive longevity, peak performance, and brain disease prevention. If you're committed to optimizing your brain- reducing Alzheimer's risk - and staying mentally sharp for life, you're in the right place. Stay sharp. Stay informed. Join thousands who subscribe to the Neuro Athletics Newsletter → https://bit.ly/3ewI5P0 Instagram: https://www.instagram.com/louisanicola_/ Twitter : https://twitter.com/louisanicola_ Learn more about your ad choices. Visit megaphone.fm/adchoices

Guest: Drs. Shuibing Chen and Hans Clevers, members of the Steering Committee for the ISSCR Consortium on Advanced Stem Cell-Based Models in Drug Discovery and Development, discuss the need to accelerate the responsible integration of stem cell–derived models into preclinical drug development. Their conversation reflects growing regulatory and policy momentum around new approach methodologies (NAMs) and underscores the importance of rigorous standards, regulatory alignment, and cross-sector collaboration to improve reproducibility and advance more predictive, human-relevant therapies. Building on its long-standing leadership in global standards, ethics, and policy, the ISSCR is uniquely positioned to convene industry, academia, and regulators around this effort. The initiative also reflects the Society's expanding industry engagement, with industry membership increasing nearly 180% over the past five years – creating new opportunities for strategic partnerships to address shared scientific and translational challenges. Featured Products and Resources: Learn how organoids can be used to expand clinical applications of diseases and disorders.   Get a free wallchart showing how organoids are used as model systems to study infectious diseases, cancer, congenital disorders, and tissue regeneration. The Stem Cell Science Round Up Treating Frailty with Stem Cells – In a clinical trial, mesenchymal stem cell therapy improved walking distance and physical function in older adults with frailty. Combined Bone & Bone Marrow Organoids – Researchers developed a scalable iPSC-derived bone marrow organoid that models human lympho-myeloid hematopoiesis and disease. CAR-NK Progenitors Prevent Relapse – Engineered pluripotent stem cell–derived CAR-expressing NK progenitor cells reduced minimal residual disease and prevented relapse in leukemia models following chemotherapy. Whole-Body Single-Cell Mapping – Scientists have developed a 3D single-cell-resolution map of mouse organs and the whole neonatal body. Photo Reference: Courtesy of Drs. Shuibing Chen and Hans Clevers Subscribe to our newsletter! Never miss updates about new episodes. Subscribe

On today's episode of Dr. M's Women and Children First Podcast, we welcome a scientist whose work has quietly shaped the cardiovascular health of millions around the world. Dr. Sundeep Dugar is a pharmaceutical innovator, inventor, and industry leader with more than three decades at the forefront of drug discovery. He is best known as a co-inventor of ezetimibe — marketed as Zetia® — a landmark cholesterol-lowering medication that transformed lipid management by targeting intestinal cholesterol absorption. He also co-inventor of the combination therapy Vytorin® (ezetimibe plus simvastatin), expanding treatment options for patients at high cardiovascular risk. For this groundbreaking work, Dr. Dugar and his colleagues received the prestigious 2005 National Inventor of the Year Award from the Intellectual Property Owners Association and the Heroes of Chemistry award from the American Chemical Society. Across his career, Dr. Dugar has contributed to more than 140 patents and has authored over 70 scientific publications, reflecting a lifetime devoted to translating chemistry into real-world therapies. He is currently the founder of Aayam Therapeutics, where he leads efforts to develop innovative, accessible medicines through collaborative global research. He also serves as Co-Chief Executive Officer of Blue Oak Nutraceuticals, advancing a novel mitochondrial-targeted compound known as Mitokatlyst™, designed to stimulate mitochondrial biogenesis and cellular energy — with potential implications for muscle strength, metabolic health, cardiovascular function, and inflammation. He is the first one to decipher the mechanism by which exercise induces mitochondria levels. Mitokatlyst mechanism of action mimics this process. Dr. Dugar's scientific journey spans continents and some of the world's premier institutions. He earned both his Bachelor's and Master's degrees in Organic Chemistry from the University of Delhi, completed his PhD in Chemistry at the University of California, Davis, and pursued postdoctoral research at ETH Zürich in Switzerland and at Cornell University. Today, we'll explore the story behind major pharmaceutical breakthroughs, the science of mitochondrial health, and what the future of therapeutics may look like when innovation meets global accessibility. Please join me in welcoming Dr. Sundeep Dugar.

Ep194: Ansu Satpathy on Cancer and Autoimmune Drug Discovery by Timmerman Report

When Terry Pirovolakis learned his son had an ultra-rare neurodegenerative disease, SPG50, he refused to accept “no options.” What started as a desperate search for hope became Elpida Therapeutics, a nonprofit driving gene therapy innovation for multiple rare diseases. In this episode, Terry shares the remarkable journey from diagnosis to clinical trials, the power of partnerships, and why urgency matters when every day counts.Show NotesFrom Mystery to Medicine: The Science Behind a Mother's Search | PodcastTaking a Customized and Collaborative Approach to Therapeutic Development | PodcastRare Disease Research for Drug Development | Charles RiverRare Disease | Charles RiverDiscovery | Charles RiverBeyond The Diagnosis

a16z general partner Jorge Conde talks with Vasant Narasimhan, CEO of Novartis International, about transforming a 250-year-old conglomerate into a pure play medicines company and unlocking $180 billion of value in the process. They cover Novartis's platform technologies: cell and gene therapies, RNA medicines, and radioligand therapies. They also discuss AI in drug discovery, the rise of China as a biotech competitor, and what Vasant looks for when evaluating startup partnerships, including his advice on the killer experiments and CMC work that can make or break a deal. Resources: Follow Vasant Narasimhan on X: https://twitter.com/VasNarasimhanFollow Jorge Conde on X: https://x.com/JorgeCondeBio  Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

In this episode of Outside the OR, Steven C. Katz, MD, FACS shares his journey at the intersection of surgical oncology and industry. From clinical insight to drug development, Dr. Katz discusses how surgeons can play a pivotal role in advancing innovation, translating research into therapeutics, and collaborating with industry partners to bring new treatments to patients. Tune in for an engaging conversation about leadership, entrepreneurship, and how surgical oncologists can help shape the future of drug discovery.

In this episode of Data in Biotech, host Ross Katz sits down with James Yoder, Founder and CEO of OpenBench, to unpack a radical new approach to early-stage drug discovery. James shares how OpenBench's "success-driven" model shifts risk away from biotech partners by only charging for validated hits. They dive deep into computational screening, molecular modeling, and the company's evolving tech stack that's making hit discovery smarter and more accessible. Discover how data, AI, and strategic collaboration are redefining biotech R&D. What you'll learn in this episode: >> Why OpenBench moved away from SaaS to a success-based service model >> How their computational platform predicts binding affinity and screens trillions of compounds >> The role of data flywheels and ML in improving drug discovery success rates >> Real-world case studies from biotech collaborations >> How OpenBench evaluates druggable targets in one week Meet our guest James Yoder is the Founder and CEO of OpenBench. With a background in statistics, data science, and applied machine learning, he leads OpenBench's mission to deliver validated drug discovery hits through computational innovation and a success-driven business model. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with our guest: Sponsor: CorrDyn, a data consultancyConnect with James Yoder on LinkedIn  Connect with us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode!Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

Episode SummaryPotent in vitro hits often fail in vivo—Martin Marro details how robust assay choice and pathway deconvolution can revive GPCR drug discovery programs.Listeners will learn practical approaches to assay development for GPCR drug discovery, the pitfalls of calcium readouts, and how identifying pathway bias impacts translational success. Dr. Marro shares his experience bridging in vitro–in vivo gaps, refining selection flowcharts, and leveraging pharmacology research to drive clinical candidates. His strategic perspective is rooted in years of leading multimodal discovery teams in pharma and biotech. Key TakeawaysAssay selection critically shapes the trajectory from hit to clinic.Calcium and IP1 assays may not predict in vivo efficacy for all Gq-coupled receptor targetsAlternative pathway analysis may be essential for mechanism elucidation.Persistence in probing beyond standard readouts can rescue high-profile discovery programs. Team structure and collaborative problem-solving are pivotal in resolving translational bottlenecks.Explore Dr. GPCR Resources- Dr. GPCR Ecosystem- Membership & Pricing- Weekly NewsExplore the full depth of GPCR resources, events, and member-exclusive tools with Dr. GPCR Premium.About the GuestDr. Martin Marro leads the Cell Pharmacology group in the DOCTA division at Lilly's Seaport Innovation Center in Boston, MA. Trained as a pharmacologist, Dr. Marro has accumulated over 20 years of experience spanning large pharmaceutical firms—including GSK, Novartis, and Lilly—and innovative biotech such as Tectonic Therapeutic. He holds deep expertise in early drug discovery across small molecules, peptides, and antibody therapeutics for metabolic, cardiovascular, and gastrointestinal diseases.Dr. Marro's research has been central to the discovery and characterization of multiple clinical candidates, with a focus on GPCR target validation, receptor pharmacology, and translational assay strategies. He played a key role in patenting and developing novel fatty acid-conjugated GLP-1 receptor agonists. Driven by the challenge of translating robust in vitro science into clinical proof-of-concept, Dr. Marro's leadership continues to impact the field of GPCR drug discovery.Keywords: gpcr podcast, assay development, pharmacology research.

Brant Peterson, Vice President & Fellow at Valo Health, joins Data in Biotech to explore how his team leverages real-world data, genetic insights, and machine learning to de-risk drug discovery. From building causal DAGs to identifying patient subtypes in neurodegenerative diseases like Parkinson's, this episode dives deep into a patient-first, data-driven approach to biomedical innovation. What You'll Learn in This Episode: >> How Valo Health uses real-world evidence and EHR data to prioritize drug targets earlier in the development pipeline. >> Why integrating wet lab experiments and causal DAGs accelerates therapeutic validation. >> The importance of genetic pleiotropy and Mendelian randomization in refining disease hypotheses. >> How Valo Health identifies high-impact patient subgroups in neurodegenerative diseases like Parkinson's and Alzheimer's. >> Where machine learning models succeed and fall short, in uncovering mechanisms of disease from sparse longitudinal data. Meet Our Guest Brant Peterson is Vice President & Fellow in Data Science at Valo Health. He brings deep expertise in genetics, computational biology, and biomedical innovation. Formerly a Distinguished Data Scientist at Valo and Computational Biologist at Novartis, Brant focuses on leveraging patient-centric data to drive causal discovery in drug development. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancyConnect with Brant Peterson on LinkedIn  Connect with Us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode!Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

Send us a textJesse Mendelsohn and Michael Grosberg from Model N discuss why U.S. pricing complexity is spreading globally, the collapse of the PBM rebate model, what's really driving the pharmaceutical manufacturing boom, and why direct-to-consumer discount programs won't solve America's drug access problem.00:00 Introduction to Life Science Success Podcast00:34 Pressing Issues in Pharmaceutical Manufacturing00:51 Introducing the Experts from Model N03:08 Understanding International Reference Pricing04:14 Impact of US Pricing on Global Drug Launches09:26 Challenges with Pharmacy Benefit Managers16:29 Domestic Manufacturing Boom in Pharmaceuticals23:08 AI in Drug Discovery and Personalized Medicine29:03 Access and Policy Discussion30:01 Direct to Consumer Pricing31:32 TrumpRx Overview33:11 Compliance Challenges38:30 Pharmaceutical Revenue Management41:38 AI in Life Sciences48:35 Future of Life Sciences50:45 Concerns and Challenges53:52 Excitement in Current Work55:54 Conclusion and Final Thoughts

Synopsis: Fresh from the JPM 2026 in San Francisco, Alok Tayi welcomes Johan Luthman, Executive Vice President of R&D at Lundbeck, for a sweeping, deeply personal conversation on the future of neuroscience drug development. From his early days as a Swedish clinician-scientist to leading breakthrough Alzheimer's programs and rebuilding Lundbeck's pipeline from the ground up, Johan shares the pivotal moments—and phone calls—that shaped a 30-year career across AstraZeneca, Merck, Serono, and now Denmark's neuroscience powerhouse. The discussion dives into Lundbeck's bold strategic reset: letting biology lead, de-risking early in patients, embracing rare disease and sleep medicine, and making disciplined bets on monoclonal antibodies, migraine prevention, epilepsy, and neuroendocrine disorders. Johan explains how the company shifted capital toward innovation, rebuilt its portfolio through targeted acquisitions, and built one of the most advanced neuroscience pipelines in pharma today. In one of the episode's most powerful moments, Johan opens up about his personal motivation—caring for family members with Alzheimer's and dedicating his career to diseases of the brain. From AI-driven R&D productivity and adaptive trials to Denmark's unique foundation-owned pharma model, this conversation is a masterclass in scientific rigor, decision-making under uncertainty, and keeping patients at the center of everything. Biography: In 1991, Johan Luthman began his career in the pharmaceutical industry in Astra, later AstraZeneca. In 2005, Johan joined Serono as Head of Neuroscience & Immunology Research, and subsequently, in MerckSerono, as Therapy Area Head, Neurology & Immunology. In 2009, he became CEO of biotech start-up GeNeuro. In late 2009, Johan joined Merck as VP & Franchise Integrator for Neuroscience and Ophthalmology. In 2014, he came to Eisai where he was Senior Vice President and Head of Clinical Development. Johan joined Lundbeck as Executive Vice President, R&D in March 2019. Johan is a Swedish national and is trained as a Doctor of Dental Sciences from the Karolinska Institute, Sweden. He also holds a PhD in Neurobiology and Histology as well as an Associate Professor title from the Karolinska Institute, Sweden. Johan is a Member of the Board of Directors of Brain+.

When Patricia Weltin's daughters were diagnosed with Ehlers-Danlos Syndrome after years of uncertainty, she turned her frustration into a global movement. In this episode of Sounds of Science, Patricia shares the story behind Beyond the Diagnosis, a powerful art and advocacy initiative that uses portraiture to humanize rare diseases and inspire empathy in medical professionals, students, and communities around the world. From medical schools to courthouses and even Parisian galleries, the traveling exhibit is reshaping how we see children with rare diseases—not as diagnoses, but as vibrant individuals with stories worth telling. Tune in to hear how Patricia's mission is bridging the gap between science and compassion, and how you can help carry it forward.Show NotesFrom Mystery to Medicine: The Science Behind a Mother's Search | PodcastTaking a Customized and Collaborative Approach to Therapeutic Development | PodcastRare Disease Research for Drug Development | Charles RiverRare Disease | Charles RiverDiscovery | Charles RiverBeyond The Diagnosis

Cofounders Jeremy Wohlwend and Gabriele Corso join the a16z podcast to discuss the launch of Boltz, a public benefit company building AI infrastructure for molecular biology. The conversation explains how breakthroughs following AlphaFold moved the field beyond protein structure prediction into modeling biomolecular interactions and binding strength, why open-source Boltz models saw rapid adoption across pharma and biotech, and how that work is now being productized. They outline the launch of Boltz Lab, a platform that brings protein and small-molecule design agents into scientist workflows, Boltz's decision to operate as an infrastructure company rather than a therapeutics company, and how AI could reduce early drug discovery bottlenecks by improving molecular design and speeding iteration between computation and the lab. Resources: Follow Gabriele on X: https://twitter.com/GabriCorso Follow Jeremy on X: https://twitter.com/jeremyWohlwend Follow Jorge X: https://twitter.com/jorgecondebio Follow Zak on X: https://twitter.com/zakdoric   Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X:https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://twitter.com/eriktorenberg](https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

“There are hundreds, maybe thousands, of drug repurposing opportunities just waiting to be uncovered,” explains David Fajgenbaum, M.D.  David Fajgenbaum, M.D., physician-scientist, bestselling author of Chasing My Cure, co-founder of Every Cure, and leader in the global push for drug repurposing, joins us today to explain why the cures of tomorrow may already be on pharmacy shelves today—and how his team is racing to uncover them. - From college athlete to ICU (~3:15) - Finding a cure (~7:20) - Hope needs to drive action (~9:45) - Repurposing drugs (~11:10) - Use cases of generic drugs (~13:30) - Lithium for bipolar & Alzheimer's (~16:00) - Lidocaine & breast cancer (~17:25) - GLP-1 for longevity benefits (~19:20) - Increasing awareness in the healthcare system (~20:10) - The 3 main hurdles for repurposing drugs (~22:00) - Opportunities in the space (~23:10) - 14 advanced repurpose treatments (~28:00) - The power of AI (~32:50) - Using AI for personalized medicine (~34:30) - AI for treatment options (~37:45) - Common drugs with big potential (~41:00) - The future of healthcare & drug discovery (~44:50) - How you can help (~49:30) Referenced in the episode:  - Follow Fajgenbaum on Instagram (@dfajgenbaum)  - Check out his website (https://davidfajgenbaum.com/)  - Pick up his book, Chasing My Cure (https://www.amazon.com/Chasing-My-Cure-Doctors-Action/dp/1524799637/)  - Listen to his TED Talk (https://www.youtube.com/watch?v=sb34MfJjurc)   - Learn more about Every Cure (https://everycure.org/) We hope you enjoy this episode, and feel free to watch the full video on YouTube! Whether it's an article or podcast, we want to know what we can do to help here at mindbodygreen. Let us know at: podcast@mindbodygreen.com. Learn more about your ad choices. Visit megaphone.fm/adchoices