🎙 Tech and Drugs – Episode #11Inside Helical: Bio Foundation Models, Virtual Cells, and the Future of AI-Native Drug DiscoveryThis week I sat down with Rick Schneider, co-founder and CEO of Helical, a Luxembourg-based startup on a mission to democratize bio foundation models.Rick and his team are building something ambitious: an open, AI-native platform that lets pharma and biotech teams actually use large DNA, RNA, and single-cell foundation models, without needing their own supercomputer or an internal army of ML researchers. From training cutting-edge mRNA models on the Luxembourg HPC MeluXina to releasing beginner-friendly open-source tools, Helical is shaping the next generation of AI for science.What we cover:✔️ Rick’s journey from “almost doctor” to engineer, AI specialist, and now biotech founder✔️ What bio foundation models really are… and why unlabeled sequencing data changes the game✔️ Why one model will never solve all of biology, and why Helical is proudly model-agnostic✔️ How the field is inching toward a “virtual cell” built by combining multimodal model embeddings✔️ Pharma’s real bottlenecks: data scarcity, batch effects, validation culture, and… organizational speed✔️ How Helical enables lab-in-the-loop workflows without operating a lab themselves✔️ The explosion of new bio models, and how Helical helps teams evaluate what’s hype vs. useful✔️ Why shifting more hypothesis testing in silico could finally compress drug discovery timelinesIf you’re curious about where bio foundation models are heading, how pharma should rethink its AI stack, or what it means to build a truly AI-native biotech platform, this conversation with Rick is packed with insights.

🎙 Tech and Drugs – Episode #10A special inside look at Ginkgo Bioworks, where automation, biology, and AI collide.I sat down with John Androsavich (GM, Ginkgo Data Points) and Jason Hocking (VP Engineering) to explore how Ginkgo generates huge multimodal datasets, automates complex biology at scale, and builds the infrastructure that today’s AI models desperately need.What we cover:✅ How Ginkgo evolved from synthetic biology to high-throughput data generation✅ Why clean, diverse datasets — not models — are the real AI bottleneck✅ A look at Ginkgo’s automation: modular “Rack” systems, NGS pipelines, and scalable cell models✅ Making data usable for both scientists and ML teams✅ The future of AI-guided labs and closed-loop experimentation✅ How shifting away from animal testing could accelerate human-relevant drug discoveryWhether you're in pharma, biotech, or AI for science, this episode is packed with insights on where R&D is headed next.

ere it is 🎙 "Tech and Drugs – Podcast" Episode #9 with 🧬💻 Douglas "Doug" Selinger from Plex ResearchThe theme for today: Applying Google's Search Recipe to Pharma R&D DataIn this episode of Tech and Drugs, I sit down with Douglas "Doug" Selinger, founder and CEO of Plex Research, whose unique approach is reshaping how scientists utilize data in drug discovery. While many companies are busy building predictive AI models, Doug and his team at Plex Research are leveraging techniques inspired by Google's internet search algorithms to make sense of vast and disparate datasets in pharma and biotech.Doug brings decades of experience, from pioneering microarray technologies in George Church's lab at Harvard to leading computational biology initiatives at Novartis. In 2017, he founded Plex Research to tackle the persistent challenges of data overload, silos, and underutilized information.We cover:✅ Doug's path from early genomics research to developing an innovative search-driven analytical platform designed specifically for scientists✅ How Plex's "focal graph" method integrates massive chemical biology and omics' datasets to reveal hidden connections in biological data✅ Overcoming Pharma’s persistent "data silo" problem by creating algorithms that adapt to the data scientists actually have✅ Real-world examples of Plex Research unlocking novel insights into disease mechanisms, biomarker identification, and precision oncology✅ Why transparency and explainability remain critical for AI adoption among scientists✅ The future potential of autonomous AI systems, combining knowledge graphs and large language models (LLMs), guided by human insightWhether you're navigating the complexities of data-rich environments or curious about pragmatic AI applications, this conversation provides actionable insights for improving decision-making in drug R&D.💬 How do you see search-inspired AI approaches changing drug discovery? Share your thoughts in the comments!

Here it is 🎙 “Tech and Drugs – Podcast” Episode #8 with 👩‍🔬📊 Anindita “Ani” SinhaThe theme for today: Building a Data-Driven Pharma Organization Through Analytics & AIIn this episode of Tech and Drugs, I sit down with Anindita “Ani” Sinha, Vice President of Commercial Operations at Shionogi, whose journey from the microbiology lab to leading analytics, marketing, and field teams has shaped her vision for the future of pharma. With a BA in Biochemistry from Columbia and a PhD in Microbiology from Yale, plus nearly 15 years at Celgene, Bayer, Pfizer, and more, Ani shares how she:We cover:✅ How Ani went from academic research to consulting and then built “analytics first” teams in big pharma✅ Why AI is a powerful tool—but only when you “trust, verify, and know your data’s limits”✅ The critical steps to laying a solid data foundation: understanding, prioritizing, and connecting your datasets✅ Democratizing insights with self-service analytics and AI-driven platforms—finding the sweet spot between structure and flexibility✅ Strategies for attracting and retaining top tech-savvy talent in a highly regulated industry✅ What’s next: next-best-action models, predictive analytics, and cultivating an AI-ready workforceWhether you’re a scientist, data enthusiast, or industry leader, this conversation is packed with practical insights to help you harness analytics and AI for smarter decision-making in drug development and commercialization.💬 What data or AI challenges are you facing in life sciences? Share your thoughts in the comments!

🎙️ From Molecules to Machine Learning: How AI Is Transforming Drug Discovery - Season 1, Episode 7 — with Frédéric CélerseHow do you shrink a multi-month quantum chemistry calculation into a few days? In this episode, we explore the power of augmented intelligence in drug discovery with Frédéric Célerse, researcher, boundary-breaker, and firm believer that good science starts with great data.Join host Thibault Géoui as we dive deep into:🔹 Why Frédéric says AI is more like augmented intelligence than artificial🔹 How molecular dynamics and quantum modeling are being accelerated by machine learning🔹 What AI can (and can’t) do alone, and why human insight is still irreplaceable🔹 Real-world tensions between AI and wet-lab scientists, and how to build true interdisciplinary teams🔹 The data trust gap in life sciences and why data governance might be the unsexy hero of innovation🔹 Why publishing models need to evolve for a faster-moving AI world (yes, we’re looking at you, Nature)🔹 And what advice Frédéric gives to students stepping into this fast-changing fieldIf you’ve ever wondered what it really takes to bridge AI with chemistry, biology, and pharma, and why being curious, collaborative, and data-savvy is key, this episode is for you.💡 Like what you hear? Subscribe to the podcast, leave a review, and follow us for more honest conversations at the frontier of tech and drugs.

🎙 Tech and Drugs - Episode #6 with 🏛📊 Marilia Aires from Kiin AI is now live!The theme for today: AI, Data Ethics & The Legal Challenges in Drug DiscoveryIn this episode, we dive into the legal frontier of AI in healthcare with Marilia Aires, legal counsel and data protection officer at Kiin AI. With 16+ years of experience across global legal landscapes, Marilia is at the intersection of law, data ethics, and biotech, helping organizations navigate the legal and ethical minefield of AI-driven drug discovery.We explore:✅ Who owns the data? Understanding data privacy, IP, and regulatory frameworks✅ GDPR, AI Act & Pharma: How Europe’s AI laws impact biotech innovation✅ The challenge of high-risk AI models in drug R&D and how companies navigate regulations✅ Data contracts & AI governance: Ensuring compliance while fueling innovation✅ Future trends in biotech AI: Personalized medicine, real-world data, and global data governanceWith AI reshaping everything in life sciences, from clinical trials to personalized treatments, Marilia breaks down the biggest legal and ethical questions companies must answer before deploying AI solutions.💬 What’s your take on AI regulation in pharma? Are current laws helping or hindering innovation? Drop your thoughts in the comments!

Here it is 🎙 “Tech and Drugs - Podcast” Episode #5 with 🤖💊 Nicolas Maignan is now live! The theme for today: Rethinking Drug Discovery Through AI & PolypharmacologyIn this episode of Tech and Drugs, I sit down with Nicolas Maignan, the AI expert and COO at Kantify, who’s redefining drug discovery with a fresh, data-driven perspective. With a background in industrial engineering and over seven years of hands-on experience in machine learning, Nicolas shares his journey from consultancy to revolutionizing R&D in healthcare—challenging the outdated one-drug, one-target dogma and championing a polypharmacological approach.We cover:✅ How Nicolas transitioned from industrial engineering to AI-powered drug discovery✅ The limitations of the one-drug, one-target paradigm and why it’s time for a change✅ The promise of polypharmacology: targeting multiple pathways for more effective treatments✅ Overcoming R&D challenges with big data and heterogeneous datasets✅ Future trends in pharma, including the rise of AI-first companies in tackling rare diseasesWhether you’re a tech enthusiast, a life sciences professional, or just curious about the future of drug discovery, this conversation is packed with insights to inspire and inform.💬 What are your thoughts on shifting from single-target to multi-target drug development? Drop your views in the comments!

🎙 Tech and Drugs - Episode 4: Navigating Pharma, Tech & The Future of Work with Adam WalkerIn this episode of Tech and Drugs, I sit down with Adam Walker, a seasoned consultant, technologist, and thought leader with nearly 30 years of experience in pharmaceuticals, clinical research, and medtech. Adam has worked across biometrics, quality assurance, and real-world evidence, leading global teams and driving technology adoption in major pharma companies, including AstraZeneca.We cover:✅ The evolving role of AI in drug discovery and preclinical research✅ The job market turmoil in pharma and biotech—layoffs, personal branding, and resilience✅ The tension between security and innovation in pharma’s approach to digital transformation✅ The future of remote work vs. on-site policies in life sciences✅ How data assets are reshaping pharma and the growing demand for ‘bilingual’ scientists and data expertsWhether you’re navigating a career shift, exploring AI’s impact on drug R&D, or curious about where life sciences is heading in 2025, this conversation is packed with insights.💬 What’s your take on AI, job market shifts, or remote work in pharma? Drop your thoughts in the comments!#Pharma #AI #DrugDiscovery #RealWorldEvidence #ClinicalResearch #DataScience #FutureOfWork #Biotech #LifeSciences

This time, I sit down with Jakob Zeitler, an expert in causal inference and machine learning, to explore how AI-driven experimentation is reshaping drug R&D. If you’ve ever wondered how active learning can revolutionize drug discovery, or why machine learning in pharma is both promising and problematic, this one’s for you.

🔹 Jakob’s Journey: From early coding to cutting-edge research at Oxford University and Matterhorn Studio

🔹 Pharma’s Efficiency Problem: Why drug development keeps getting more expensive and how AI might fix it

🔹 Active Learning 101: How AI decides which experiment to run next (smarter, not harder!)

🔹 Machine Learning in Drug Discovery: Where it works, where it fails, and why we need more than one “AlphaFold”

🔹 "Lab-in-the-Loop": The future of AI-powered experimentation in pharma

🔹 Adopting AI in R&D" Practical steps for pharma leaders looking to integrate active learning today

n this episode, I sit down with Benjamin Szilagyi, a true pioneer in data science and digital transformation. With over 25 years of experience, Ben shares insights from his leadership roles at Roche and dsm-firmenich, diving into the real stories behind building data-driven ecosystems in Pharma and Biotech.

Here’s a sneak peek of the themes we explored:

1️⃣ The Power of Starting Small: Why focusing on one use case can drive large-scale digital transformation and break through corporate inertia.

2️⃣ From FAIR Philosophy to Action: How to operationalize FAIR data principles step-by-step and align data workflows for real impact.

3️⃣ Human-Centric Transformation: The surprising role of empathy, trust, and cross-functional alignment in leading change at scale.

4️⃣ The Data Pyramid Problem: Why focusing on foundational data quality is the secret to unlocking sustainable AI-driven insights.

5️⃣ Purpose and Talent: How to attract, motivate, and retain top talent by centering mission and impact in your work.