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From Models to Medicine: Daily Summaries Delivered
by KAMI Think Tank
At KAMI Think Tank, we love cutting through the AI hype to showcase where the technology actually stands and how to use it meaningfully within the life sciences. Now, we're bringing that same clarity with our community to a brand new podcast: From Models to Medicine. Every week, we sit down with a real practitioner in the life sciences, whether they're working in the lab, leading innovation at a biotech, or building tools to better serve scientists. We discuss how they cut through the hype of AI and practically leverage the tool in their scientific workflows. Glad to have you here.
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- Today40 min
Episode 18: Why Does mRNA Keep Breaking AI Models?
In this episode, we sit down with Chris Nelson, an mRNA scientist at nCode Bio whose career has taken him from building blood transfusion matching tests now used by the NHS, to cancer genomics at Personalis, to the frontier of mRNA sequence design. Chris breaks down why the untranslated regions of mRNA might actually change how we dose therapeutics, reduce side effects, and engineer cells inside the body entirely.Then we talk about why AI has conquered protein folding but keeps hitting a wall with mRNA. Chris walks us through the data problem, the cost problem, and the modeling choices nCode Bio is making that go against the grain of what most of the field is building. He's also candid about where the big model providers are letting biology down and which scrappy research groups he thinks are quietly building the tools that will actually matter.
- 1 weeks ago41 min
Episode 17: Garbage Data, Toxic Exposure, and the AI Gap in Environmental Health
In this episode of From Models to Medicine, we sit down with Sarah Daniels, an environmental health researcher, who makes the argument that the chemicals making us sick might also be our best clues for building better drugs. She traces that idea from a 1976 discovery about dioxins all the way to an FDA-approved psoriasis treatment in 2023 — and asks what AI could accelerate if the field finally got its data act together.Sarah brings up where AI in environmental health is genuinely stuck; siloed industry data, models built only for acute toxicity, and a chronic low-dose problem that no one has cracked. She also shares how she used APIs to build her own real-time funding tracker for microplastics research in under a month, and why the gap between what AI promises and what environmental health can actually feed it is one of the most under appreciated bottlenecks in the field right now.Links mentioned in this episode: Historic ARPA-H program funding announcement on Microplastics: https://arpa-h.gov/explore-funding/programs/stompSarah's two cents on the ARPA-H Microplastics program: https://engineeredresilience.substack.com/p/a-microplastics-prelude-for-engineeredAggregated trends in microplastics from the non-peer-reviewed literature (Trendspotter App):https://www.engineeredresilience.org/microplasticsStay up to date on latest releases from Sarah's work on Engineered Resilience by signing up for substack: https://engineeredresilience.substack.com/Report on unexpected health effects observed after LA fires showing how questions remain post-exposure to pollutants:https://www.latimes.com/environment/story/2025-12-17/la-fires-heart-attacks-strange-blood-test-results-spikedCan a Biologist Fix a Radio? - great piece on the necessity to understand biology at the systems-level: https://www.cell.com/cancer-cell/fulltext/S1535-6108(02)00133-2
- 2 weeks ago37 min
Episode 16: Why Chemistry has Held Drug Discovery Back
Stan Jastrzębski 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.
- 3 weeks ago44 min
Episode 15: Stacking AI with Quantum in the Sciences
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.
- Jun 24, 202645 min
Episode 14: The Drug Discovery Problems AI Alone Can't Solve
In this episode of From Models to Medicine, we speak with Vid Stojevic, 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.
- Jun 17, 202630 min
Episode 13: The Microbiome is Messier Than You Think
Jenny Yang 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.
- Jun 10, 202649 min
Episode 12: Your Life Sciences Data Isn't Ready for AI
Bogdan Knezevic 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 keysAI needs contextIterating your way to success via better data managementThe data maturity ladderBefore AI there was good data
- Jun 3, 202648 min
Episode 11: From the Gut to the Brain - Rethinking Parkinson's with AI
In this episode of From Models to Medicine, we sit down with Minna Schmidt, a postdoctoral researcher at the Buck Institute for Research on Aging. Minna walks us through Braak's hypothesis and the emerging "brain-first vs. body-first" framing of the disease, explaining how symptoms can appear up to 30 years before a clinical diagnosis is ever made. We also get into the data side of the work. Minna uses a dataset with over 54,000 participants and talks honestly about what AI actually does and doesn't unlock when you're staring down that volume of microbiome and clinical data. She uses LLMs to organize her thinking, speed up literature reviews, and learn basic programming, while being clear-eyed about where the field's biggest bottleneck actually is: not the tools, but the data itself. ----------------------------------------------------------------------This episode was sponsored by CleanSpace. CleanSpace designs, manufactures, and installs advanced controlled environments—delivering complex projects months faster with guaranteed costs and uncompromising performance. Please contact Chelsea for more information or with any questions at CLauridsen@CleanSpaceus.com.
About From Models to Medicine
At KAMI Think Tank, we love cutting through the AI hype to showcase where the technology actually stands and how to use it meaningfully within the life sciences. Now, we're bringing that same clarity with our community to a brand new podcast: From Models to Medicine. Every week, we sit down with a real practitioner in the life sciences, whether they're working in the lab, leading innovation at a biotech, or building tools to better serve scientists. We discuss how they cut through the hype of AI and practically leverage the tool in their scientific workflows. Glad to have you here.
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