Tech and Drugs - Podcast

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/

In this Tech & Drugs episode, I sit down with Mathieu Bourdenx, Group Leader at the UK Dementia Research Institute / UCL, to explore how AI agents and AI co-scientists are beginning to change neuroscience research.We discuss brain aging, Alzheimer’s disease, dementia, systems biology, Kosmos, Open Scientist, CBrain, and what it means to work with AI tools that can search literature, analyze data, generate hypotheses, and support scientific discovery.Mathieu’s research focuses on brain aging and the mechanisms that may increase the risk of dementia. In this conversation, we start with the biology: why Alzheimer’s disease is so difficult, why patient heterogeneity matters, why biomarkers are essential, and why preventing or delaying dementia could have a major impact on public health.We then move into the changing role of data and AI in neuroscience. Mathieu explains why biology is not a simple linear pathway, why modern scientists need to understand data science, and how high-dimensional, multimodal data can help us understand complex biological systems.A major part of the episode focuses on AI co-scientists. Mathieu shares his experience working with Kosmos, an AI system designed to support parts of the scientific process: forming hypotheses, searching literature, analyzing data, and iterating across evidence. He explains how the system generated a hypothesis from single-cell transcriptomic data, how the team tested signals across independent datasets, and why wet-lab validation remains the bottleneck.We also discuss CBrain and Open Scientist, including the importance of open-source tools, trusted research environments, patient data protection, and the future of AI agents that can support dementia research at scale.Finally, we explore what this means for the future of science: AI agents in the daily routine of researchers, scientific claim verification, hallucinations, code review, agent-to-agent critique, faster research output, pressure on scientific publishing, and the idea of “digital brains” for scientists and labs.Key themes discussed:- Alzheimer’s disease, biomarkers, and patient heterogeneity- Why prevention may matter as much as treatment- The limits of N-of-1 longevity experiments- Systems biology and high-dimensional data in neuroscience- Why scientists need data science and AI skills- Kosmos and the rise of AI co-scientists- I-generated hypotheses and experimental validation- CBrain, Open Scientist, and open-source AI for dementia research- Trusted research environments and protected patient data- AI agents in everyday scientific workflows- Verification, hallucinations, and scientific trust- How AI may change lab notebooks, publications, and institutional memoryWhy this matters:AI in science is often discussed in broad and abstract terms. This episode grounds the conversation in the real work of neuroscience: reading papers, analyzing data, testing hypotheses, validating findings, and deciding what is worth pursuing in the lab. For pharma, biotech, AI, data, and R&D leaders, it is a practical look at how agentic AI could reshape scientific work without removing the need for biological expertise, experimental validation, and careful judgment.Guest: Mathieu BourdenxRole: Group LeaderInstitution: UK Dementia Research Institute / UCLLinkedIn: https://www.linkedin.com/in/mathieu-bourdenx-21995546/ Website: https://www.ukdri.ac.uk/team/mathieu-bourdenx#AICoScientist #Neuroscience #DementiaResearch #DrugDiscovery #TechAndDrugs

In this Tech & Drugs episode, I sit down with Thomas Steger-Hartmann, former investigational toxicology lead at Bayer and industry lead of the VICTOR Consortium, to explore the future of drug safety.We discuss how toxicology is moving from descriptive animal studies toward more predictive, data-driven, and less animal-dependent approaches — without losing sight of the ultimate goal: protecting patients.Thomas explains why animal models are often more useful than public debates suggest, where they fall short, and how new approach methodologies, virtual control groups, organ-on-chip systems, historical control data, and AI could reshape preclinical safety assessment.A major theme of the conversation is the VICTOR Consortium, which aims to use historical control data, statistics, and artificial intelligence to build virtual control groups and potentially reduce animal use in toxicology studies.We also discuss the role of regulators, why regulatory acceptance is often misunderstood, and how collaboration between industry, EMA, FDA, and scientific consortia can help move new methods into practice.Key themes discussed:Why toxicology is central to drug discovery and developmentWhat animal studies do well — and where they fall shortThe limits of translating animal findings to humansWhy rare adverse events are statistically difficult to detectHow virtual control groups workThe VICTOR Consortium and historical control dataHow AI can help extract value from toxicology datasetsNAMs, in vitro systems, in silico tools, and organ-on-chip modelsRegulatory science, qualification, validation, and acceptanceWhy better data curation is essential for safer and more ethical R&DWhy this matters:Drug safety sits at the intersection of biology, data, regulation, and ethics. As pharma and biotech move toward AI-enabled R&D, toxicology is becoming a critical test case for how the industry can use historical data, computational methods, and new experimental systems responsibly — reducing animal use while maintaining scientific and regulatory confidence.Guest information:Guest: Thomas Steger-HartmannRole: Former investigational toxicology lead at Bayer; industry lead of the VICTOR ConsortiumCompany: Bayer / VICTOR ConsortiumLinkedIn: https://www.linkedin.com/in/thomas-steger-hartmann-b7b05a55/Company website: https://www.bayer.com/ & https://www.vict3r.eu/Tech & Drugs explores how data, AI, and technology are changing pharma, biotech, and drug R&D. Hosted by Thibault Geoui, the podcast brings together leaders, scientists, technologists, and builders working at the interface of science and technology.If you enjoyed this conversation, subscribe to Tech & Drugs for more discussions on AI, data, and the future of pharma and biotech.#Toxicology #DrugSafety #AIinPharma #DrugDiscovery #NAMs

In this Tech & Drugs episode, I sit down with Patrick Ansems, globally responsible for life sciences strategy at Siemens across pharma and medical devices, to explore how AI, data, automation, and platforms are reshaping drug development.Recorded live in London at the Pistoia Alliance meeting 2026, this conversation looks at a central question for the industry: can pharma make drug development learn faster than it forgets?Patrick has worked across science, software, R&D, lab informatics, and manufacturing, with experience at PerkinElmer Informatics, Tetrascience, Dotmatics, and now Siemens. In this episode, we discuss why AI is forcing leaders to think differently, why FAIR data has new urgency, and why pharma still depends so much on Excel, PowerPoint, documents, and institutional memory.We also explore the “messy middle” of tech transfer, the role of context in scientific data, and the idea of “pharma in the loop” — connecting discovery, development, and manufacturing into a more integrated learning system from molecule to market.Key themes discussed:- Why AI is changing leadership in pharma and life sciences- FAIR data, AI-ready data, and why data context matters- The gap between scientific progress and slow decision-making- Why pharma workflows still rely on Excel, PowerPoint, Word, and institutional memory- Tech transfer as the messy middle between R&D and manufacturing- How to reduce corporate amnesia in scientific organizations- The role of Dotmatics within Siemens- Scientific intelligence platforms and the future of connected lab data- Lab automation, manufacturing, simulation, and AI-enabled experimentation- Why AI is not a silver bullet for messy infrastructure- “Pharma in the loop” from molecule to marketWhy this matters:AI will only create real impact in pharma and biotech if it is connected to high-quality data, scientific context, robust workflows, and the realities of development and manufacturing. For R&D, digital, data, technology, and manufacturing leaders, the challenge is no longer just adopting AI tools. It is building systems that can learn across the full drug development lifecycle.

Guest information:Guest: Patrick Ansems
Role: Global Head of Life Sciences at Siemens Digital Industries SoftwareCompany:

SiemensLinkedIn: https://www.linkedin.com/in/patrickansems/Company website: https://www.siemens.com/en-us/company/about/businesses/digital-industries/About the podcast:Tech & Drugs explores how data, AI, and technology are changing pharma, biotech, and drug R&D. Hosted by Thibault Geoui, the podcast brings together leaders, scientists, technologists, and builders working at the interface of science and technology.If you enjoyed this conversation, subscribe to Tech & Drugs for more discussions on AI, data, and the future of pharma and biotech.#AIinPharma #DrugDevelopment #LifeSciences #PharmaR&D #TechAndDrugs