Tech and Drugs - Podcast

🎙 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.