Episode 8: The Equity Gap in Diagnostic AI

Episode 16: Why Chemistry has Held Drug Discovery Back

Stan Jastrzębski [https://www.linkedin.com/in/sjastrzebski/] is the co-founder of molecule.one and a deep learning researcher who made a deliberate pivot into synthetic chemistry. In this episode, he explains why chemistry is the real bottleneck in drug discovery today. Unlike biology, which has the Protein Data Bank and tools like AlphaFold, chemistry lacks the massive, balanced datasets AI needs to work. Scientific literature makes it worse, publishing wins and burying failures, which starves models of exactly the negative data they learn from most. Stan also talks about "vibe binding," the industry's growing tendency to over-rely on biological binding models that only work on well-trodden targets and quietly kill scientific creativity in the process. He closes with a sharp take on where LLMs actually hit their ceiling, and why he thinks scientific discovery is not just a use case for AI but its ultimate test.

Episode 15: Stacking AI with Quantum in the Sciences

Anna Benefiel [https://www.linkedin.com/in/anna-benefiel/]has spent over 20 years in healthcare innovation, and she's tired of watching life sciences sit on the sidelines of quantum computing. In this episode, she makes a direct case for why pharma needs to stop treating quantum as a future problem and start treating it as a present opportunity. We get into what quantum actually does that classical computing cannot, why probabilistic nuance matters for complex biological problems, and where the real near-term value lives: clinical trial optimization, supply chain, and cell culture yields. We also dig into what happens when you stack quantum on top of AI. Anna points to models already using standard histopathology slides to generate spatial proteomic maps for lung cancer, and argues quantum is what takes discoveries like that to the next order of magnitude. *The views represented on this podcast are not a reflection of Strangeworks.

Episode 14: The Drug Discovery Problems AI Alone Can't Solve

In this episode of From Models to Medicine, we speak with Vid Stojevic [https://www.linkedin.com/in/vid-stojevic-32640314/], the co-founder and CEO of Kuano, a Cambridge-based company using quantum algorithms and AI to tackle the drug discovery problems that traditional computational chemistry keeps failing. In this episode, he explains why he deliberately ignored the broad platform play and went narrow instead, targeting the specific early-stage problems where getting the physics right changes everything. We get into what a "quantum lens" actually means in practice, why transition states are a better design target than natural substrates, and how Kuano is succeeding on targets that pharma had written off as undruggable. Vid makes a sharp case for how generating synthetic quantum data turns a low-data drug discovery problem into something AI can actually work with. He closes with honest advice on when quantum simulation is the right tool and when it simply isn't.

Episode 13: The Microbiome is Messier Than You Think

Jenny Yang [https://www.linkedin.com/in/jennyyang259/] is the co-founder and CEO of Outpost Bio, where her team is working to make human microbiology computable. In this episode, she breaks down why bias in ML models is so easy to miss. High overall accuracy can hide terrible performance on specific subgroups, and in healthcare, that gap has consequences. She traces the problem upstream, from skewed training datasets to the way clinical definitions themselves carry historical bias, and explains the real trade-offs involved in trying to correct for it. We also get into what makes the microbiome such a hard problem is that our microbiomes can differ by up to 90% from person to person. Jenny walks us through how Outpost Bio's "lab in the loop" model tightly integrates wet lab experiments with AI to generate better, less biased data from the ground up, and why rigorous external validation is the thing she'd tell every biotech founder to prioritize before anything else.

Episode 12: Your Life Sciences Data Isn't Ready for AI

Bogdan Knezevic [https://www.kaleidoscope.bio/company] is the CEO and co-founder of Kaleidoscope Bio, and he's seen enough failed AI implementations to know where they almost always break down. In this episode, he walks us through what minimum viable data standardization actually looks like in practice, why consistent naming conventions and structured data entry matter more than people want to admit, and what every biotech CEO should ask their team before writing another AI budget line. We also get into a guardrails conversation and Bogdan is direct about what happens when autonomous agents operate without proper permissions. He closes with a sharp framework for deciding what to build in-house versus hand off, with some great resource hand-offs. * Data vs keys [https://blog.kaleidoscope.bio/unlocking-the-importance-of-structure-data-vs-keys/] * AI needs context [https://blog.kaleidoscope.bio/ai-cant-reason-with-context-it-doesnt-have/] * Iterating your way to success via better data management [https://blog.kaleidoscope.bio/experimenting-your-way-to-success-data-management-in-biotech/] * The data maturity ladder [https://blog.kaleidoscope.bio/where-is-your-biotech-on-the-data-maturity-ladder/] * Before AI there was good data [https://blog.kaleidoscope.bio/before-ai-there-was-good-data/]