Data in Biotech
Claim This Podcastby CorrDyn
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Podcast Overview
<p><i>Data in Biotech </i>is a fortnightly podcast exploring how companies leverage data to drive innovation in life sciences. </p> <p>Every two weeks, Ross Katz, Principal and Data Science Lead at CorrDyn, sits down with an expert from the world of biotechnology to understand how they use data science to solve technical challenges, streamline operations, and further innovation in their business. </p> <p>You can learn more about CorrDyn - an enterprise data specialist that enables excellent companies to make smarter strategic decisions - at <a href="https://www.corrdyn.com" target="_blank">www.corrdyn.com</a></p>
Language
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Publishing Since
9/25/2023
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Recent Episodes
June 17, 2026
Synthesizable by Design: Rethinking AI's Role in Small Molecule Drug Discovery
<p>In this episode of Data in Biotech, host Ross Katz sits down with Paul Finn, Chief Scientific Officer at Oxford Drug Design, for a conversation on what it actually takes to find a drug molecule that works not just on paper but also in the lab, in the cell, and, ultimately, in the clinic. Paul brings four decades of experience across what became GSK, Pfizer, and a series of Oxford-area spinouts and has shepherded a compound all the way to a marketed drug. That perspective gives him a particular kind of skepticism toward AI results that look too good to be true because he's done the work of checking whether they are.</p> <p>The conversation moves through synthesizability as a first-class constraint, why chemistry has proven so much harder for AI than biology, how 3D molecular representation gets closer to the physics that actually matters, and what rigorous multi-parameter optimization looks like when you're trying to kill cancer cells and drug-resistant bacteria at the same time.</p> <p><b>What you'll learn in this episode:</b></p> <p>>> Why synthesizability is chronically underestimated and why changing a single atom in a structure can take a molecule from trivially easy to make to practically impossible</p> <p>>> How Oxford Drug Design constrains the generative search to reaction schemes and purchasable building blocks, and why that chemical space is still so vast that novelty is not meaningfully sacrificed</p> <p>>> Why most generative AI models learn from a 2D string representation of a molecule; two steps removed from the 3D physics that govern how a drug actually binds to its target</p> <p>>> How Bayesian optimization over reagent space, rather than molecular space, allows an active learning loop to focus on the structural patterns associated with activity</p> <p>>> Why benchmarking complex models against simple ones is the discipline that exposes false correlations and why Paul and his co-authors were able to recover the Halicin result using methods decades older than deep learning</p> <p>>> What a pharma company should actually ask an AI drug discovery vendor before buying what they're selling</p> <p><b>Meet our guest:</b></p> <p>Paul Finn is Chief Scientific Officer at Oxford Drug Design, a computational drug discovery company with roots in Oxford's chemistry department. His career spans over 40 years of computational drug discovery, from early structure-activity modeling in the 1980s through to modern generative AI methods, with deep experience at what became GSK and Pfizer before moving into the Oxford spinout ecosystem. At Oxford Drug Design, Paul leads internal programs in oncology and antibacterial resistance, combining novel computational methods with a rigorous, synthesizability-first approach to multi-parameter optimization.</p> <ul><li>Connect with Paul Finn on LinkedIn: https://uk.linkedin.com/in/paul-finn-2250616</li></ul> <p><b>About the host:</b></p> <p>Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.</p> <ul><li>Connect with Ross Katz on LinkedIn:<a href="https://www.linkedin.com/in/b-ross-katz/" target="_blank"> https://www.linkedin.com/in/b-ross-katz/</a></li></ul> <p><b>Connect with us:</b></p> <ul><li>Follow the podcast for more insightful discussions on the latest in biotech and data science.</li><li>Subscribe and leave a review if you enjoyed this episode!</li></ul> <p><b>Sponsored by…</b></p> <p>This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.<a href="https://www.linkedin.com/company/corrdyn/" target="_blank"> https://www.linkedin.com/company/corrdyn/</a></p>
June 2, 2026
From Tissue to Mechanism to Decision: Building AI for Computational Oncology
<p>In this episode of Data in Biotech, host Ross Katz sits down with Arvind Rao, Professor of Computational Medicine and Bioinformatics at the University of Michigan, for a discussion on the gap between what biomedical AI can do and what it can reliably be trusted to do in clinical practice. Arvind's research sits at the intersection of computational oncology and AI governance and his lab works across H&E histopathology, multiplex immunofluorescence, spatial transcriptomics, and single-cell RNA sequencing, not just to build predictive models, but to understand the full lifecycle from data to model to inference, and to ask where that lifecycle can be trusted and where it can't. </p> <p>The conversation moves through two of his recent papers on SPIFEE, a graph-based framework that replaces scalar interaction scores in the tumor microenvironment with spatially resolved functional representations, and a multimodal framework that traces a path from stained tissue slides to nominated drug targets via morphological pattern discovery and spatial transcriptomic mapping. </p> <p><b>What you’ll learn in this episode: </b></p> <p>>> Why the field's central failure is not algorithmic but translational and the gap between a model that performs well on a benchmark and one that can be consistently trusted in a high-stakes clinical setting </p> <p>>> How SPIFEE replaces the conventional scalar edge representation of cell-cell interactions in the tumor microenvironment with spatially resolved functional edges</p> <p>>> How Arvind's multimodal framework moves from H&E pathology slides labeled with clinical outcomes, through morphological pattern discovery via multiple instance learning, to spatial transcriptomic mapping, to the nomination of molecular mechanisms and actionable drug targets</p> <p>>> Why Goodhart's Law applies directly to foundation model evaluation in biology </p> <p>>> What the AI literacy gap costs when it goes unaddressed in healthcare and pharma organizations </p> <p><b>Meet our guest:</b></p> <p><b>Arvind Rao</b> is a Professor of Computational Medicine and Bioinformatics, with a joint appointment in Radiation Oncology, at the University of Michigan. His research focuses on establishing trust in biomedical AI predictions across the full data-to-decision pipeline, integrating H&E histopathology, spatial transcriptomics, multiplex immunofluorescence, and single-cell RNA sequencing to build models that are predictive, interpretable, and biologically credible. Alongside his research, Arvind develops AI literacy programs for healthcare and pharma professionals, helping clinical and procurement teams evaluate and govern AI systems with the rigor those decisions demand.</p> <ul><li>Connect with Arvind Rao on<a href="https://www.linkedin.com/in/scott-lipnick-2a64b41a/" target="_blank"> </a>LinkedIn: https://www.linkedin.com/in/arvind-rao-3301301ba/</li></ul> <p><b>About the host:</b></p> <p><b>Ross Katz</b> is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.</p> <ul><li>Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/</li></ul> <p><b>Connect with us:</b></p> <ul><li>Follow the podcast for more insightful discussions on the latest in biotech and data science.</li><li>Subscribe and leave a review if you enjoyed this episode!</li></ul> <p><b>Sponsored by…</b></p> <p>This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn<b>. </b>https://www.linkedin.com/company/corrdyn/</p>
May 13, 2026
Cavities in the Data: Building FDA-Cleared AI for Dental Imaging with Overjet
<p>In this episode of Data in Biotech, host Ross Katz sits down with Sadegh Salehi, Director of Research and Principal Scientist at Overjet, to explore what rigorous model evaluation actually looks like when the stakes are clinical. </p> <p>Overjet builds FDA-cleared vision models that detect and quantify dental disease across billions of X-ray images from thousands of practices - a data problem with a staggering number of dimensions. Thirty-two teeth per adult patient, each with different morphology. Multiple image types capturing different anatomy. Fifteen to twenty sensor manufacturers producing perceptually distinct images, each with different contrast, resolution, and noise characteristics. And disease severity distributions ranging from barely visible early-stage decay to obvious pathology. </p> <p>Sadegh walks through what it takes to evaluate models responsibly across all of those dimensions and discusses why aggregate metrics like F1 score can mask catastrophic failures on specific subgroups, how models find and exploit shortcuts in training data, and why the same flawed sampling that creates gaps in your training set also creates them in your test set. </p> <p>He also traces Overjet's architectural evolution from over twenty narrow task-specific models to a single foundation model they call Unity, explains how treatment plan procedure codes provide a noisy but real production feedback signal, and describes how Overjet became one of the first companies to secure the FDA's Predetermined Change Control Plan (a framework that allows model updates without filing a new clearance each time.)</p> <p><b>What you’ll learn in this episode: </b></p> <p>>> Why aggregate evaluation metrics are insufficient for high-stakes medical AI </p> <p>>> How models exploit shortcuts in training data: if all images from a rare sensor in the training set happen to be healthy, the model doesn't learn to read that sensor, it learns that the sensor means healthy, bypassing the visual task entirely and producing systematic false negatives in production</p> <p>>> How Overjet evolved from over twenty narrow, sensor-specific and indication-specific models into a single foundation model called Unity, using noisy labels generated by the small models as the training signal for a much larger backbone, then building independent prediction heads for each clinical indication on top of it</p> <p>>> Why the decision to keep prediction heads architecturally independent from one another was driven as much by FDA regulatory strategy as by modeling considerations</p> <p>>> How Overjet uses dental treatment plan procedure codes as a production monitoring signal</p> <p><b>Meet our guest:</b></p> <p><b>Sadegh Salehi </b>is Director of Research and Principal Scientist at Overjet, where he leads the team responsible for building, evaluating, and deploying FDA-cleared vision models for dental disease detection and quantification. </p> <ul><li>Connect with Sadegh Salehi on<a href="https://www.linkedin.com/in/scott-lipnick-2a64b41a/" target="_blank"> </a>LinkedIn: https://www.linkedin.com/in/sadegh-salehi/</li></ul> <p><b>About the host:</b></p> <p><b>Ross Katz</b> is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.</p> <ul><li>Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/</li></ul> <p><b>Connect with us:</b></p> <ul><li>Follow the podcast for more insightful discussions on the latest in biotech and data science.</li><li>Subscribe and leave a review if you enjoyed this episode!</li></ul> <p><b>Sponsored by…</b></p> <p>This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn<b>. </b>https://www.linkedin.com/company/corrdyn/</p>
73 total episodes available with 1 transcripts
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- What is Data in Biotech?
<p><i>Data in Biotech </i>is a fortnightly podcast exploring how companies leverage data to drive innovation in life sciences. </p> <p>Every two weeks, Ross Katz, Principal and Data Science Lead at CorrDyn, sits down with an expert from the world of biotechnology to understand how they use data science to solve technical challenges, streamline operations, and further innovation in their business. </p> <p>You can learn more about CorrDyn - an enterprise data specialist that enables excellent companies to make smarter strategic decisions - at <a href="https://www.corrdyn.com" target="_blank">www.corrdyn.com</a></p> - How often does this podcast release new episodes?
This podcast updates weekly.
- Where can I listen to this podcast?
This podcast is available on 10 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
No, this podcast does not typically feature guests.
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