Tech and Drugs - Podcast

What makes an AI drug discovery startup worth backing — and what makes it fail? Mati Gill, CEO of AION Labs, joins Thibault Géoui to discuss the science, teams and evidence that turn promising technology into a business pharma actually needs.Mati shares a particularly revealing lesson: AION’s first startup brought strong AI expertise but lacked a biologist on the team. After a year, they agreed to close it. That experience changed how AION builds companies: relevant scientific expertise has to be there from day one.We explore AION’s approach to building startups around specific R&D challenges, bringing together pharma partners including Pfizer, AstraZeneca, Merck KGaA and Teva, alongside AWS and investors. Rather than waiting until a technology is ready to ask whether pharma wants it, AION involves R&D experts early and defines the proof of concept before the startup is created.In this episode:• Where AI could create value across disease biology, drug design, preclinical prediction and clinical trials.• Why faster and cheaper are no longer enough to differentiate an AI platform.• How to pitch scientists — and why buzzwords can end a meeting.• What a startup needs to demonstrate within its first 18 months.• What milestones and headline deal values mean for enabling technologies.• Why scientific expertise, coachability and collaboration matter when building teams.• Whether pharma, service providers or Big Tech will acquire the next generation of platforms.• What frontier AI companies entering life sciences could mean for partnerships, competition and trust.For founders, scientists, pharma R&D leaders, investors and anyone working where AI meets drug discovery.CONNECT WITH OUR GUESTMati Gill on LinkedIn: https://www.linkedin.com/in/matigill/AION Labs: https://aionlabs.com/AION Labs on LinkedIn: https://www.linkedin.com/company/aion-labs/ABOUT TECH & DRUGSHosted by Thibault Géoui, Tech & Drugs explores how science, data, technology and people are changing drug discovery and development.Subscribe for more conversations at the intersection of AI and life sciences, and tell us in the comments: what evidence would convince you that an AI platform can improve drug R&D?#DrugDiscovery #AIinPharma #TechBio

BenchSci’s CEO advertised for a VP of Marketing. Then he decided not to hire one.What happened next opens a much bigger conversation about AI agents, scientific research, and how companies are built.In this episode of Tech & Drugs, I speak with Liran Belenzon, co-founder and CEO of BenchSci, about two transformations happening together: building an agentic research environment for scientists and redesigning the company behind it around AI.We trace BenchSci’s journey from helping scientists choose antibodies to supporting disease biology research and experimental planning. Liran explains how pharma partnerships and advances in AI changed what the company could build—and why he believes adding AI to existing workflows only takes you so far.Then we get into the difficult questions.What happens to management when everyone can direct a digital workforce? How do you maintain trust through layoffs and constant organisational change? And what should remain human when more work can be automated?We also discuss:• Bringing scientific evidence, models and tools into one research environment.• Why BenchSci reconsidered its VP of Marketing hire.• The leadership habits that need to change when building around AI.• AI costs, scientific publishing, data access and intellectual property.• Why investment in AI drug discovery has yet to deliver the clinical pipeline impact many expected.• Why scientific judgment matters when researchers become orchestrators of AI agents.A candid conversation about scientific ambition, business survival, and the people living through the transition.EXPLORE BENCHSCIhttps://www.benchsci.com/SUBSCRIBE TO TECH & DRUGSYouTube: https://www.youtube.com/@TechandDrugsSpotify: https://open.spotify.com/show/2Y4bCwsd8skxXXMkz8JUJLApple Podcasts: https://podcasts.apple.com/us/podcast/tech-and-drugs-podcast/id1784797151Which part of your organisation would you redesign first around AI—and where would you insist on human leadership? Share your thoughts in the comments.#DrugDiscovery #AgenticAI #TechAndDrugs

Can AI compress years of biomedical research into months? And if it can speed up literature reviews, coding and data analysis, what happens when the next step still depends on an experiment?I’m joined by Stephan Reichl, a biomedical data scientist at CeMM and the Medical University of Vienna, to explore the promise and practical limits of AI in science. Stephan co-authored a study asking how much general-purpose AI could accelerate biomedical research—and where the bottlenecks would move.We discuss smarter experimental design, predictive readouts, AlphaFold, SimulateGPT, the challenges of adopting AI in research teams, and a question that matters more as these systems gain autonomy: who decides what “good science” looks like?GUEST AND RESOURCESStephan’s paper, “What are the limits to biomedical research acceleration through general-purpose AI?”:https://www.nature.com/articles/s41598-025-32583-w“GPT-4 as a biomedical simulator” (SimulateGPT):https://pubmed.ncbi.nlm.nih.gov/38909448/MrBiomics, the bioinformatics workflow project mentioned in the conversation:https://github.com/epigen/MrBiomicsFit For Science, Stephan’s podcast with Rob ter Horst:https://www.youtube.com/@FitForScienceAccelerate Europe:https://accelerate-europe.org/Subscribe to Tech & Drugs for conversations about data, AI and the future of drug R&D:https://www.youtube.com/@TechandDrugsWhat part of scientific research would you trust an AI co-scientist to handle—and where would you insist on a human decision? Tell us in the comments.#AIforScience #DrugDiscovery #TechAndDrugs

Kevin Cramer has spent over two decades in lab informatics. He founded Sapio Sciences, unifying LIMS and electronic lab notebooks into a single R&D platform, and now leads Sigmatic Sciences, building autonomous, agentic AI systems and Scout — an AI scientist built for the wet lab.In this episode of Tech & Drugs, Kevin and Thibault Géoui get into why pharma was great at science but treated software as an afterthought, why "how do I get data OUT of the system" was the question nobody asked, and what a post-ELN world actually looks like. They dig into agentic pipelines and why accuracy compounds across agents, the lab-in-the-loop DMTA cycle, "agentic burnout" when you're the gatekeeper for 300 agents, the difference between LLMs and specialized scientific agents, and why the real bottleneck is trust and change management rather than technology. Kevin closes with the bet he'd stake his career on.A conversation about where in silico discovery is heading — and what has to change for scientists to actually use it.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━CONNECT WITH KEVIN CRAMERLinkedIn: https://www.linkedin.com/in/kevincramerveloxity/Sigmatic Sciences: https://www.sigmaticsciences.com/Sapio Sciences: https://www.sapiosciences.com/━━━━━━━━━━━━━━━━━━━━━━━━━━━━━CONNECT WITH THIBAULT GÉOUILinkedIn: https://www.linkedin.com/in/thibaultgeoui/━━━━━━━━━━━━━━━━━━━━━━━━━━━━━Tech & Drugs explores how AI, data, and technology are reshaping pharma and life sciences R&D — in conversation with the people building it.Subscribe for new episodes.#TechAndDrugs #AIinPharma #DrugDiscovery #AgenticAI #LabInformatics #LifeSciences #AIScientist

In this episode of Tech & Drugs, I sit down with Dr. Stephanie Linker and Dr. Philipp Schnee from Merck KGaA, Darmstadt, Germany to explore how AI is changing protein design, drug discovery, and the role of scientists.We discuss de novo protein design, structure prediction, AlphaFold, scientific agents, AI adoption, and what happens when computational models suggest biological solutions that scientists might never have considered themselves.Stephanie works as a Senior Computational Biochemist in the Digital Innovation team at Merck KGaA, Darmstadt, Germany, focusing on AI-driven protein design and structure prediction. Philipp works in the Data & AI environment at Merck KGaA, Darmstadt, Germany, bringing together scientific expertise and technology to develop end-to-end solutions, including a de novo protein design platform.A central theme of our conversation is the move from predicting biology to increasingly being able to engineer it. Stephanie describes proteins as the “machines of the cell”—and explains how de novo design allows scientists to start designing proteins for specific interactions rather than only discovering what already exists in nature.We also discuss what happens when AI challenges established scientific intuition. Stephanie shares an example of applying new protein-design methods to a target that had previously been considered “undruggable”—and how the results challenged those initial assumptions.Beyond protein design, we explore a much bigger change in scientific work. If AI can access information, write code, generate hypotheses and increasingly orchestrate parts of the research process, where does human scientific value move?For Stephanie and Philipp, curiosity, asking the right questions, connecting disciplines, choosing where to focus, and building trust around AI-generated results become increasingly important.Key themes discussed• AI-driven protein structure prediction and de novo protein design• Moving from predicting proteins to engineering new proteins• AlphaFold, co-folding models and protein language models• Exploring targets previously considered “undruggable”• When AI-generated designs challenge scientific intuition• Balancing exploration and exploitation in protein design• Why transparency and trust matter when scientists use AI• Building an internal protein-design platform for scientists• Making advanced AI tools accessible beyond AI specialists• How scientific agents could change research workflows• Why the scientist of the future may increasingly orchestrate AI agents• Bottom-up versus top-down AI adoption• Why AI in science goes far beyond chatbots and LLMs• Open-source models, frontier models and technological dependency• Choosing the right AI model for the right scientific task• Training scientists and the potential role of AI champions• World models and connecting digital models with physical experiments• What AI-enabled science could look like over the next five yearsGuestsDr. Stephanie LinkerSenior Computational BiochemistMerck KGaA, Darmstadt, GermanyLinkedIn: https://www.linkedin.com/in/stephanie-linker-957ba1188/Dr. Philipp SchneeData & AI / Go GlobalMerck KGaA, Darmstadt, GermanyLinkedIn: https://www.linkedin.com/in/philippschnee/