Tech and Drugs - Podcast

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

In this Tech & Drugs episode, I sit down with Laura Matz, Chief Science and Technology Officer at Merck KGaA Darmstadt, Germany, to explore how AI, data, automation, and digital technologies are reshaping science at scale.Laura brings a rare perspective across chemistry, semiconductors, life science, healthcare, and advanced materials. We discuss what pharma can learn from the semiconductor industry, why AI is changing how scientists design experiments, and what it takes to move from pilots to real impact inside a large organization.The conversation goes beyond generic “AI in pharma” claims. Laura shares a practical view of how AI can help scientists make better experimental decisions, why data governance and infrastructure matter, how autonomous labs are being built, and why leadership in the AI era requires both speed and responsibility.We also explore the future of foundation models for chemistry, biology, and physics, the role of Europe in global science and technology, and what young scientists should do to prepare for careers at the intersection of science and technology.Key themes discussed:- How AI is changing the role of science and technology leadership- What pharma can learn from Moore’s law and the semiconductor ecosystem- Bayesian optimization and smarter experimental design- Human intuition vs machine-guided discovery- Scaling AI beyond successful pilots- AI-ready data, governance, and secure access- Autonomous labs and the connection between physical and digital science- How leaders can balance speed, stability, and experimentation- Europe’s role in global science and technology competitiveness- Foundation models for chemistry, biology, and physics- Career advice for scientists entering an AI-enabled worldWhy this matters:AI will not transform pharma, biotech, or R&D through models alone. The real challenge is connecting data, infrastructure, scientific expertise, leadership, and operating models in a way that helps scientists move faster while preserving rigor, safety, and trust.Chapters:00:00 Introduction01:10 Laura Matz’s background and early scientific curiosity03:25 Basketball, teamwork, and leadership05:05 From pre-med to chemistry08:00 What a Chief Science and Technology Officer does10:00 How the CSTO role changed after ChatGPT11:25 What pharma can learn from semiconductors14:35 Is pharma truly more complex than other industries?16:20 Biology, engineering, and the tension between tech and life sciences18:40 Where AI is already impacting R&D workflows19:15 Bayesian optimization and better experiment design21:15 Pairing AI experts with scientific experts22:25 Human intuition vs machine-driven discovery25:20 Scaling AI pilots beyond the “messy middle”28:15 Adoption friction in large organizations29:45 AI-ready data and the foundations of AI transformation35:50 Data access, governance, and security37:25 Building an autonomous chemistry lab in Boston39:25 Decision-making in the AI era40:15 Why leaders need to experiment with AI themselves41:10 Balancing speed and stability in AI transformation47:30 AI as augmentation, not replacement50:00 Europe, innovation, and global competitiveness52:00 Foundation models for chemistry, biology, and physics52:45 Career advice for young scientists54:00 Closing thoughtsGuest information:Guest: Laura MatzRole: Chief Science and Technology OfficerCompany: Merck KGaA Darmstadt, GermanyLinkedIn: https://www.linkedin.com/in/laura-m-matz/Company website: https://www.merckgroup.com/enTech & 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 #DrugDiscovery #AutonomousLabs #Biotech #TechAndDrugs

In this Tech & Drugs episode, I sit down with Walid Kamoun, VP and Global Head of Oncology R&D at Servier, to explore how AI is changing oncology drug discovery and development.We discuss where AI is already useful today, what remains difficult, and how pharma leaders can think about AI beyond hype, pilots, and generic “transformation” language.Walid shares a grounded view from the front lines of oncology R&D: how AI can support asset leaders, clinical scientists, target discovery, molecule design, trial planning, regulatory work, and patient matching. We also discuss why AI adoption is not only a technology question, but an operating model, culture, data, and leadership question.A central theme of the conversation is AI as a booster for expert work. Rather than replacing scientific and clinical judgment, AI may help teams create stronger “draft zero” development plans, accelerate decision-making, and focus human expertise where it matters most.Key themes discussed:- How AI is changing oncology drug discovery and development- Why AI should support, not replace, expert judgment- The role of AI in asset leadership and integrated development plans- “Draft zero” thinking for oncology programs- AI in synthetic chemistry and synthetic biology- AI use cases in clinical development, protocol writing, and regulatory work- Matching the right patient to the right drug in precision oncology- Why AI-ready data and infrastructure matter- How biotechs may benefit from AI as an accelerator- How pharma can evaluate AI partners beyond marketing claims- The role of big tech in pharma and biotech R&D- Why oncology R&D still needs strong human, scientific, and clinical leadershipWhy this matters:AI is already influencing how pharma and biotech teams discover, develop, and evaluate new medicines. But the real opportunity is not simply using more tools. It is understanding where AI can improve R&D decisions, accelerate timelines, strengthen development strategies, and ultimately help bring better therapies to patients.Guest information:Guest: Walid KamounRole: VP and Global Head of Oncology R&DCompany: ServierLinkedIn: https://www.linkedin.com/in/walid-kamoun-10223288/Company website: https://servier.com/en/servier/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 #Oncology #DrugDiscovery #Biotech #PharmaRND

🎙 Tech and Drugs – Season 02, Episode 03From Code to Cells: Dov Gertz of Converge Bio on Generative AI and the Future of Drug DiscoveryIn this episode, I sat down with Dov Gertz, CEO and co-founder of Converge Bio, a company pushing generative AI directly into the language of biology.Dov represents a new breed of scientist.Trained in computer science and bioinformatics, with research roots in CRISPR discovery alongside leading pioneers like Jennifer Doudna, he is now building AI systems that don’t just analyze biology but actively design it.This conversation sits at the heart of what’s changing in our industry right now - From data to models to real-world impact.What we cover:✔️ Dov’s journey from computer science to CRISPR research and AI-driven biology✔️ Why the biggest bottleneck in AI for drug discovery is not compute, but data quality✔️ The shift from traditional AI to generative AI and why it changes everything✔️ How Converge Bio trains models directly on DNA, RNA, and protein sequences✔️ Why biology is fundamentally harder than NLP despite having more raw data✔️ The rise of autonomous labs and why they are critical to unlock AI’s full potential✔️ Why most clinical failures are not about targets, but about molecules✔️ How generative models can design better antibodies in a single iteration✔️ The reality of AI agents in science and why we are still far from “AI researchers”✔️ Why small, highly skilled teams are outperforming large R&D organizations✔️ The transition from AI experimentation to industrialization in pharma✔️ When we will actually see AI impact FDA approvals (and why it will take time)One idea that stood out for me: We are finally moving from AI as a tool… to AI as a generator of biology.That’s a very different paradigm.Dov brings a clear and grounded perspective on where AI truly works today, where it still struggles, and why the next breakthroughs will come from combining better data, better models, and tighter integration with experimental systems.If you care about the future of drug discovery, the role of generative AI in biology, and what it takes to move from promise to real impact, this episode is for you.I hope you enjoy this conversation as much as I did.

🎙 Tech and Drugs – Season 02, Episode 02
From Tennis Courts to Molecular Design: Tim Hoctor on Data, Discovery, and the Future of Pharma
Last December in Berlin, I had the privilege of sitting down with my friend and longtime mentor Tim Hoctor, one of the true legends at the intersection of technology and life sciences.
Tim’s career defies categories.
He started as a professional tennis player in California. From there, he stepped into early Silicon Valley startups, then into Molecular Design Limited, the birthplace of computerized chemical registration and what many still call the “MDL Mafia.” Later, he became a senior leader at Elsevier, helping shape how scientific data, literature, and databases connect in the digital era.
This conversation is part industry history lesson, part strategic deep dive, and part personal reflection.
What we cover:
✔️ Tim’s unconventional journey from tennis pro to engineer to life sciences data executive
✔️ How MDL pioneered digital molecular representation and why it became foundational to modern pharma
✔️ Why linking structured databases to scientific literature was visionary in the 1990s and still unfinished business today
✔️ The persistent data silos in pharma and why culture, more than technology, is often the bottleneck
✔️ Why 6 billion dollar drug development costs are a systems problem, not just a science problem
✔️ The real role of regulators in AI adoption and how agencies are asking industry to help define the future
✔️ What COVID changed forever in automation, digital adoption, and supply chain resilience
✔️ How tools like ChatGPT are reshaping behavior across pharma teams
✔️ Why pharma still hasn’t had its “SpaceX moment” and what it would take to truly disrupt the model
✔️ The vision of garage biotech powered by autonomous labs, shared data, and AI driven discovery
Tim speaks with rare clarity about what holds our industry back, what gives him hope, and why better data sharing may ultimately matter more than the next algorithm.
If you care about the evolution of pharma R&D, the cultural barriers to AI adoption, and what it will take to move from incremental efficiency to true system level change, this episode is for you.
I hope you enjoy this conversation as much as I did.